# Silicon | The Data Partner for High-Growth Startups URL: https://meetsilicon.com/ Your Silicon data team This week - Product events and Stripe connected Mon - Activation and churn defined with product Tue - Growth dashboard shipped Wed - Broken signups model fixed before the 9am report Thu - Onboarding funnel by signup source In progress Monthly metrics review Thu, 10:00 Our work Answers Alerts # The data partner for high-growth startups We bring your company’s numbers into one place, define each one with the team that owns it, and let anyone ask questions in Slack. - Your company’s data in one place - Key metrics defined with each team - Dashboards and an AI analyst in Slack - Live in about ten weeks, maintained after Talk to Sujay 30-minute call with the founder Companies with an existing data team can use the platform on its own. Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Data work today Somebody at your company is already doing data work. Usually it’s someone in ops, finance or growth, in spreadsheets, late in the evening, on top of their actual job. We’d rather they didn’t have to. - CSV exports - Revenue tracker v7 - Copy-paste - Production database - “Is this number right?” - Three churn definitions - Stripe export - Board pack final_v3 - CRM report - Broken formulas - Late evenings - Manual fixes Problem ## What companies usually try first High-growth startups need reliable numbers well before a data team fits the budget. Each of these works for a while. - 01 ### Keep running on spreadsheets Every team exports its own numbers, so sales and finance end up with different revenue for the same month. Someone loses Monday morning to the weekly report, and the board deck gets checked by hand the night before. 58% of leaders say key business decisions are based on inaccurate or inconsistent data most of the time. Source: DevPro Journal - 02 ### Hire a data team Eventually the right call. A first data hire usually spends their early months building infrastructure before anyone sees a dashboard, and experienced data engineers are slow to find. $150k average total pay for one data engineer in the US. Source: Built In, Data Engineer salaries, 2026 - 03 ### Buy a dashboard tool Looker or Metabase will chart whatever you give them. Someone still has to get the data into shape first and fix it when a field changes upstream, which brings you back to hiring. 65 days median time to hire a senior data engineer at a US company, from opening the role to a signed offer. Source: KORE1, Time to fill a data engineer role Silicon takes on that work as your data partner. The foundation we build stays in place as your team grows. How the data partnership works → Two ways to work with us ## Start from where your company is today We can build and maintain your company’s data for you, with the platform included. Or, if you already have a data team, they can use the platform on its own. No data team We do the work ### Data partnership We build your company’s data foundation and maintain it as the company grows. Your team has dashboards and the AI analyst from launch, both built on the Silicon platform. Your team’s part Explain how the business runs, sign off each key metric, and tell us what you need next. How the data partnership works → Priced after discovery · monthly or annual A typical start Depends on how many tools you run - Weeks 1–2 Discovery We map where your data lives and which numbers your leadership team relies on. You get a written plan and a quote. - Weeks 3–8 Build We bring your company’s data into one place and define each key metric with the person who runs that team. - Weeks 7–10 Launch Dashboards for each team and the AI analyst in Slack, all built on those definitions. - After launch Maintain New tools, new metrics, requests from any team, and fixes when something in your data changes. metrics/net_revenue.yml 1 # Reviewed in pull request #318 2 metrics: 3 - name: net_revenue 4 label: Net revenue 5 model: ref('fct_invoices') 6 measure: sum(amount_usd - refunds_usd) 7 filter: status = 'paid' 8 dimensions: [plan, region, month] 9 owner: finance You have a data team Your team does the work ### Silicon platform Our AI analytics platform, connected to the warehouse and dbt project your team already maintains. They define each metric once, and everything below runs on those definitions. - 01 Define each metric once Your team writes metric definitions in YAML next to the dbt project and reviews them like any other code. Every answer and dashboard reads from them. Semantic layer → - 02 Answer questions in Slack Anyone in the company asks in plain English. The answer comes back with a chart, the metric it used and the SQL behind it. AI analyst → - 03 Build dashboards and reports Save any answer as a chart, group charts into dashboards, and send them to Slack or email on a schedule. Dashboards & reports → - 04 Catch breaking changes Silicon reads each pull request and comments when a change would break a dashboard or metric, with a drafted fix. Schema protection → See the platform → From $1,499 a month · unlimited users Security ## Security built into Silicon from day one Read-only access, queries that run in your warehouse, AI that respects every permission, and no training on your data. How we handle data ### Data stays in your warehouse Silicon connects with read-only credentials and runs queries in your warehouse. Results are cached briefly to keep dashboards fast. ### Your data never trains models Customer data is never used to train AI models. Answers are built from your semantic layer, not memorised. ### AI inherits your permissions The AI analyst runs every query as the person asking, so row- and column-level rules apply to every answer. ### Deploy your way Hosted in the US, with an EU region on Pro. Enterprise can run Silicon privately in your own VPC. On every plan - Row- and column-level security - Read-only warehouse access - SSH tunnels to private warehouses - Private spaces for work in progress On Pro and Enterprise - User groups Pro - SSO, SAML and SCIM 2.0 Enterprise - Custom role builder Enterprise - Private deployment in your VPC Enterprise Signs it’s time ## Signs a company has outgrown its spreadsheets Talk to Sujay - 01 The board deck is assembled from three spreadsheets. - 02 Product analytics and billing report different numbers for the same month. - 03 Someone spends Mondays rebuilding the same report. - 04 The CEO wants AI on company data, and nobody trusts the data yet. - 05 A product question takes a week to answer. You’ll be talking to the person building Silicon. Bring whatever you have, even if it’s mostly spreadsheets. Sujay SN Founder and CEO Talk to Sujay FAQ ## Questions? We’ve got answers ### What is Silicon? + Silicon partners with high-growth startups to build and maintain their data foundation. Our AI analytics platform runs on that foundation, and anyone in the company can ask it a question in plain English and see where each answer comes from. Companies with an existing data team can use the platform on its own. ### Do we need a data team to use Silicon? + No. When we partner with you, we build and maintain your data foundation as the company grows: connectors, warehouse, models, key metrics and dashboards. If you already have a data team, they can use the platform on its own. ### What does the data partnership include? + Data pipelines, warehouse setup, dbt models, metric definitions, dashboards and reports, the AI analyst in Slack, reliability monitoring and fixes, and a monthly review of your numbers. It’s scoped to your needs, runs monthly or annually, and always includes the platform. ### Can we use just the platform? + Yes. Team is $1,499 a month and Pro is $2,399 a month, with unlimited users on both. Your data team connects Silicon to your warehouse and dbt project, and you can partner with us later if you need more hands. ### Is Silicon secure? + Silicon connects with read-only access, runs queries in your warehouse and never uses your data to train models. AI answers respect row- and column-level permissions. Enterprise adds SSO, SCIM and private deployment in your VPC. We haven’t completed a SOC 2 or HIPAA audit yet; the Security page describes how we handle data today. ## Tell us where your data lives today Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # About Silicon: The Team Behind Your Numbers URL: https://meetsilicon.com/about/ About Silicon # The data partner for high-growth startups Silicon partners with high-growth startups to build and maintain their data foundation, and our AI analytics platform runs on top of it. We’re Verge Labs Private Limited, based in Bengaluru, and we started with a simple belief: any team should be able to ask a question and see where the answer comes from. Founder ## The founder ### Sujay SN Founder, Silicon Sujay SN founded Silicon in July 2026 to close the spreadsheet-to-dashboard-to-incident loop: the question that takes days, the metric with three definitions, the dashboard that broke overnight. Before Silicon, Sujay worked at Merlin AI (YC S24), Inkle and Dive AI (Surge 04). Every early customer gets a direct line to the founder, not a sales rep. Talk to Sujay Why we exist ## Data work only matters when people can trust what changes next 01 ### Context travels with the answer Metrics, definitions and caveats should be visible at the moment a decision gets made. 02 ### Reliability is a product surface Broken dashboards, stale cohorts and schema drift deserve first-class workflows, not after-the-fact cleanups. 03 ### Built for the people on call The best analytics system helps teams fix the pipeline, not just screenshot the symptom. How we work ## Short feedback loops, good tools and fewer handoffs 01 ### Customer loops over guessing We stay close to the moments where analytics breaks: metric changes, dashboard reviews, pull requests and executive questions. 02 ### Engineering-grade analytics We borrow from software: reviews, diffs, tests, alerts and rollback paths for data work. 03 ### Measured by trust restored The goal is not more charts. It’s fewer unanswered caveats when a team needs to act. ## Building reliable analytics? Let’s compare notes Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Blog: Guides for High-Growth Startups | Silicon URL: https://meetsilicon.com/blog/ Blog # The Silicon blog Guides and field notes on semantic layers, dbt, AI analytics and keeping dashboards reliable. Guides Hiring a data engineer vs working with a data partner Oct 20, 2026 · 6 min read ## Hiring a data engineer vs working with a data partner What a high-growth startup gets from hiring its first data engineer, bringing in a consultant or working with a data partner, with the time and cost of each. Guides Why AI gets your company's numbers wrong Oct 7, 2026 · 4 min read ## Why AI gets your company's numbers wrong AI tools give confident answers about company data, but they guess what your columns mean. What a 2026 benchmark found, and the first step any startup can take. Guides dbt is where data work becomes software work Jul 7, 2026 · 12 min read ## dbt is where data work becomes software work dbt turns scattered SQL into tested, documented, governed data workflows. Here’s why that matters for trusted analytics, reusable metrics, and AI answers. Guides How to monitor dashboard impact before merging a PR Jul 7, 2026 · 7 min read ## How to monitor dashboard impact before merging a PR The pull request is the last good moment to protect dashboard trust. Here’s how PR impact checks connect code changes to the dashboards, metrics, owners, and AI answers they affect. Guides Tableau alternatives for governed self-serve BI Jul 7, 2026 · 7 min read ## Tableau alternatives for governed self-serve BI The best Tableau alternative depends on whether your team needs visualization depth, governed metrics, AI answers, or analytics reliability. Guides Data freshness vs data quality vs data reliability Jul 7, 2026 · 7 min read ## Data freshness vs data quality vs data reliability Freshness, quality, and reliability are related, but they measure different parts of whether data can be trusted for decisions. Guides Why self-serve analytics fails at scale Jul 7, 2026 · 7 min read ## Why self-serve analytics fails at scale Self-serve analytics often fails when speed grows faster than governance, ownership, metric consistency, and reliability checks. Guides How AI analytics works with dbt metrics Jul 7, 2026 · 7 min read ## How AI analytics works with dbt metrics AI analytics becomes more trustworthy when it can answer questions using governed dbt metric definitions instead of guessing from raw tables. Guides What is dashboard reliability? Jul 7, 2026 · 6 min read ## What is dashboard reliability? Dashboard reliability is the confidence that every chart, metric, and report is accurate, fresh, and safe to use for decisions. Guides Semantic layer vs metrics layer: what is the difference? Jul 7, 2026 · 6 min read ## Semantic layer vs metrics layer: what is the difference? Semantic layers and metrics layers both help teams define trusted business logic, but they solve different parts of the analytics reliability problem. Guides How to prevent broken dashboards after schema changes Jul 7, 2026 · 7 min read ## How to prevent broken dashboards after schema changes Broken dashboards usually start as ordinary schema changes. Here is how analytics teams can catch downstream impact before stakeholders lose trust. Guides What is schema drift in analytics? Jul 7, 2026 · 6 min read ## What is schema drift in analytics? Schema drift happens when the structure of your data changes underneath dashboards, reports, and AI answers. Here is how it breaks analytics and how to prevent it. Engineering Why your analytics pipeline breaks at scale May 28, 2026 · 8 min read ## Why your analytics pipeline breaks at scale Analytics pipelines rarely fail in one dramatic moment. They drift, duplicate, stale, and surprise teams unless correctness is treated like a product contract. Product Self-serve analytics without the chaos May 14, 2026 · 5 min read ## Self-serve analytics without the chaos Giving every team access to data is easy. Keeping that access trustworthy is the hard part. Guides A practical guide to data freshness Apr 30, 2026 · 8 min read ## A practical guide to data freshness Freshness is a promise you make to your stakeholders. Here is how to define it, measure it, and defend it. Company How we think about data trust at Silicon Apr 12, 2026 · 4 min read ## How we think about data trust at Silicon Trust is the only metric that compounds. A look at the principles behind everything we build. Data Science From prompt to production analytics in minutes Mar 26, 2026 · 8 min read ## From prompt to production analytics in minutes AI makes it easy to generate charts quickly. Production analytics still needs governed definitions, review, lineage, and trust built into the workflow. ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # A practical guide to data freshness | Silicon URL: https://meetsilicon.com/blog/a-practical-guide-to-data-freshness/ Guides A practical guide to data freshness A dashboard can say ‘updated five minutes ago’ and still be two days behind the business. The dashboard refreshed. The model ran. But the source that matters—the billing export, the CRM sync, or yesterday’s product events—never arrived. Technically, the interface is fresh. Practically, it is reporting an old world. Data freshness is not a timestamp attached to a chart. It is a promise that information moved through every stage of the workflow quickly enough for a particular decision. That promise changes with context. A monthly board report and a fraud alert should not share the same freshness target. ## Why freshness matters Stale data creates a special kind of data trust problem. It does not always look wrong. It often looks normal. The chart renders. The table loads. The metric has a value. Everyone continues the meeting as if the number is real. Then someone notices that yesterday’s orders are missing. Or that the CRM sync lagged. Or that the event pipeline stopped at midnight. Or that the board deck used Monday’s revenue while the finance close used Tuesday’s. Now the question is not just “what is the right number?” It is “how long have we been making decisions on the wrong version of reality?” That is why freshness deserves more than a timestamp. ## The old way: last updated labels Most teams start with dashboard-level freshness. A label says when the dashboard last refreshed. Maybe the BI tool shows a green status. Maybe someone checks the scheduler when a stakeholder complains. This is useful, but shallow. Dashboard refresh tells you the surface updated. It does not tell you whether the underlying sources arrived. It does not tell you whether a key model skipped a run. It does not tell you whether the transformation used a partial load. It does not tell you whether the metric is safe for the business moment. A dashboard can be freshly wrong. That is the trap. Freshness needs to be defined at the level where the promise is made. Four levels of freshness A practical freshness system usually has four layers. Source freshness Did the raw data arrive when expected? This is the first signal. If Stripe payments, Salesforce opportunities, product events, or support tickets are late, everything downstream inherits that delay. Source freshness should be measured against the actual behavior of the source. A daily CSV export, an hourly ingestion job, and a streaming events table should not share one generic SLA. Model freshness Did the transformed dataset update after the source arrived? A raw table can be current while a dbt model is stale. Maybe a job failed. Maybe a dependency was skipped. Maybe a transformation was disabled during a backfill and never re-enabled. Model freshness tells you whether the trusted business object is current, not just whether raw data exists. Metric freshness Is the business number current enough for its use case? This is where things get interesting. Revenue, active users, churn, pipeline, and retention may depend on different sources with different update cycles. A metric is only as fresh as the slowest important dependency. Metric freshness is what users usually care about. They are not asking whether stg_salesforce__opportunities updated. They are asking whether pipeline coverage is safe to discuss. Surface freshness Did the answer, dashboard, report, app, or AI response use current trusted data? This is the user-facing layer. It should expose freshness in plain language. Not just “last run succeeded,” but “Revenue is current through 8:00 AM IST” or “Product activation is delayed because event ingestion is 4 hours behind.” The closer freshness gets to the decision, the more human it needs to become. ## Freshness depends on context There is no universal freshness SLA. Some data needs to be real time. Some data needs to be correct by tomorrow morning. Some data is useful weekly. Some data should not move until finance has closed the period. This is why “fresh” has to be defined by workflow. A fraud alert may need minute-level freshness. An executive dashboard may need daily consistency. A customer health score may need hourly or daily updates depending on how teams use it. A monthly board metric may need locked, audited data more than speed. The right question is not “how fresh can we make this?” It is “how fresh does this need to be for the decision it supports?” That question saves teams from two bad extremes: over-engineering real-time pipelines nobody needs, and under-monitoring stale data everyone relies on. ## How to define freshness expectations Start with the business workflow. For each important dashboard, metric, or AI answer, define: Expected update cadence Acceptable delay Business hours or reporting windows Critical upstream sources Downstream surfaces that need warnings Owner Escalation path User-facing explanation when freshness is violated This can be simple. The point is not to create a giant governance artifact. The point is to make the promise explicit. Example: Revenue dashboard Expected data through previous day by 8:00 AM local time Critical sources: payments, subscriptions, CRM account mapping Warning if any source is more than 2 hours late Owner: analytics engineering Escalation: finance ops channel User-facing message: “Revenue data is delayed because subscription data has not completed today’s sync.” That is much better than a red dot that says “failed.” ## Freshness alerts should include impact Bad freshness alerts create alert fatigue. A table is late. Which table? Why should anyone care? What uses it? Is a dashboard wrong? Is the business blocked? Is this a known delay? Should someone wake up? Good freshness alerts include impact. They tell you: Which source or model is stale ## How late it is ## What expected window it missed Which metrics and dashboards are affected ## Who owns the affected assets Whether the issue is new or recurring ## What changed recently That context changes the response. A stale sandbox table can wait. A stale revenue model before the leadership meeting cannot. Freshness without impact is noise. Freshness with lineage is operational context. ## Freshness and quality are related, not identical Fresh data can be bad data. Bad data can be fresh. This sounds obvious until a team treats freshness as a proxy for trust. “The pipeline ran today” is not the same as “the data is correct.” A source may arrive on time with half the expected rows. A model may update successfully while a join duplicates every customer. A metric may be current and still wrong because the definition changed. Freshness is one dimension of reliability. You also need checks for volume, schema, uniqueness, nulls, accepted values, and business-specific rules. Freshness tells you whether the data arrived in time. Quality checks tell you whether it still behaves the way the business expects. The useful question is not “is it fresh?” It is “is it safe to use?” Freshness is one input to that answer. AI makes freshness more visible AI analytics changes how users experience freshness. In a dashboard, stale data may hide behind a small timestamp. In an AI workflow, a user asks a direct question and expects a direct answer. If the agent answers from stale data, the failure feels personal. The system sounded confident. That means AI agents need freshness context, not just table access. A useful analytics agent should know: Which data sources are current Which metrics are delayed Which dashboards or saved answers are affected Whether a question depends on stale assets ## How to explain freshness limits to the user ## When to refuse, warn, or suggest a safer path The right AI answer is sometimes: “I can answer this, but the subscription data is four hours behind, so the revenue number may change.” That is not a worse experience. That is a trustworthy one. Make freshness visible where decisions happen Freshness indicators should not live only in the data team’s monitoring tool. They should show up where people use data: Dashboards Metric pages AI answers Slack alerts PR reviews Incident threads Embedded analytics Executive reporting workflows The wording should be human. “Model run failed” is useful for an engineer. “Pipeline coverage is current through yesterday at 11:00 PM” is useful for a sales leader. “This answer may exclude today’s product events” is useful for a PM. Freshness is a trust signal. Put it in the trust moment. ## Common freshness mistakes Using one SLA for everything Not all data deserves the same freshness expectations. Critical metrics need explicit promises. Low-value tables do not need to page anyone. Monitoring only dashboards Dashboard freshness is too late and too shallow. Monitor sources, models, metrics, and surfaces. Alerting without ownership If nobody owns the asset, the alert becomes background noise. Every important freshness promise needs an owner. Ignoring business calendars Daily reporting windows, month-end close, weekends, regional timezones, and sales cycles matter. Freshness should match how the business operates. Treating freshness as trust Freshness is necessary, not sufficient. Pair it with quality checks and lineage. Hiding freshness from users If the data is delayed, say so. Users can handle caveats. They cannot handle invisible uncertainty. ## A practical freshness checklist For each critical analytics asset, ask: What decision does this support? How current does the data need to be? Which upstream sources matter? What is the acceptable delay? Who owns the asset? What happens when freshness is violated? Where should users see the warning? Which downstream dashboards, metrics, apps, and AI answers are affected? Which quality checks should run alongside freshness? If you cannot answer these questions, the freshness promise is probably implicit. Implicit promises are how trust breaks. ## Where to start Start with one high-stakes dashboard or metric. Revenue is a good candidate. So is pipeline, activation, retention, churn, or customer health. Map the path: Raw sources Transformation models Metric definitions Dashboards or AI entry points Owners Expected update times Known failure modes Then add the smallest useful set of checks: Source freshness Model freshness Volume sanity checks Business-rule tests User-facing warning language Escalation path That is enough to make freshness operational. The lesson: freshness is part of the product Users do not care that a DAG succeeded. They care whether the number is safe to use. Freshness is one of the ways the data product earns that trust. It tells users how current the answer is, where the delays are, and whether the system is being honest about its own limits. The timestamp is not the promise. The workflow is the promise. Build the workflow. ## The takeaway Freshness becomes useful when it stops being an engineering timestamp and becomes an understandable contract with the person making the decision. FAQ ## Frequently asked questions ### What does data freshness mean? + Data freshness describes whether information is current enough for its intended decision, including source arrival, ingestion, transformation, and delivery to the consuming surface. ### How is freshness different from last updated time? + Last updated usually describes one layer. Freshness evaluates the age of the underlying business event across the entire path. ### How should freshness SLAs be chosen? + Choose them according to decision urgency, business consequence, source behaviour, and the user’s ability to recognise or tolerate delay. ### What should users see when data is late? + Show the latest trustworthy timestamp, the delayed dependency, expected recovery, affected outputs, and whether the user should wait or proceed cautiously. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Company Articles from the Silicon Blog | Silicon URL: https://meetsilicon.com/blog/category/company/ Blog / Company # Company How Silicon works, what we believe about data trust, and the decisions behind the product. Company How we think about data trust at Silicon Apr 12, 2026 · 4 min read ## How we think about data trust at Silicon Trust is the only metric that compounds. A look at the principles behind everything we build. ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Data Science Articles from the Silicon Blog | Silicon URL: https://meetsilicon.com/blog/category/data-science/ Blog / Data Science # Data Science Notes on analysis, metrics and how AI turns a question into analytics a team can rely on. Data Science From prompt to production analytics in minutes Mar 26, 2026 · 8 min read ## From prompt to production analytics in minutes AI makes it easy to generate charts quickly. Production analytics still needs governed definitions, review, lineage, and trust built into the workflow. ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Engineering Articles from the Silicon Blog | Silicon URL: https://meetsilicon.com/blog/category/engineering/ Blog / Engineering # Engineering How data pipelines, dbt projects and dashboards break as companies grow, and how teams prevent it. Engineering Why your analytics pipeline breaks at scale May 28, 2026 · 8 min read ## Why your analytics pipeline breaks at scale Analytics pipelines rarely fail in one dramatic moment. They drift, duplicate, stale, and surprise teams unless correctness is treated like a product contract. ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Guides Articles from the Silicon Blog | Silicon URL: https://meetsilicon.com/blog/category/guides/ Blog / Guides # Guides Practical guides on semantic layers, data freshness, schema changes and choosing analytics tools. Guides Hiring a data engineer vs working with a data partner Oct 20, 2026 · 6 min read ## Hiring a data engineer vs working with a data partner What a high-growth startup gets from hiring its first data engineer, bringing in a consultant or working with a data partner, with the time and cost of each. Guides Why AI gets your company's numbers wrong Oct 7, 2026 · 4 min read ## Why AI gets your company's numbers wrong AI tools give confident answers about company data, but they guess what your columns mean. What a 2026 benchmark found, and the first step any startup can take. Guides dbt is where data work becomes software work Jul 7, 2026 · 12 min read ## dbt is where data work becomes software work dbt turns scattered SQL into tested, documented, governed data workflows. Here’s why that matters for trusted analytics, reusable metrics, and AI answers. Guides How to monitor dashboard impact before merging a PR Jul 7, 2026 · 7 min read ## How to monitor dashboard impact before merging a PR The pull request is the last good moment to protect dashboard trust. Here’s how PR impact checks connect code changes to the dashboards, metrics, owners, and AI answers they affect. Guides Tableau alternatives for governed self-serve BI Jul 7, 2026 · 7 min read ## Tableau alternatives for governed self-serve BI The best Tableau alternative depends on whether your team needs visualization depth, governed metrics, AI answers, or analytics reliability. Guides Data freshness vs data quality vs data reliability Jul 7, 2026 · 7 min read ## Data freshness vs data quality vs data reliability Freshness, quality, and reliability are related, but they measure different parts of whether data can be trusted for decisions. Guides Why self-serve analytics fails at scale Jul 7, 2026 · 7 min read ## Why self-serve analytics fails at scale Self-serve analytics often fails when speed grows faster than governance, ownership, metric consistency, and reliability checks. Guides How AI analytics works with dbt metrics Jul 7, 2026 · 7 min read ## How AI analytics works with dbt metrics AI analytics becomes more trustworthy when it can answer questions using governed dbt metric definitions instead of guessing from raw tables. Guides What is dashboard reliability? Jul 7, 2026 · 6 min read ## What is dashboard reliability? Dashboard reliability is the confidence that every chart, metric, and report is accurate, fresh, and safe to use for decisions. Guides Semantic layer vs metrics layer: what is the difference? Jul 7, 2026 · 6 min read ## Semantic layer vs metrics layer: what is the difference? Semantic layers and metrics layers both help teams define trusted business logic, but they solve different parts of the analytics reliability problem. Guides How to prevent broken dashboards after schema changes Jul 7, 2026 · 7 min read ## How to prevent broken dashboards after schema changes Broken dashboards usually start as ordinary schema changes. Here is how analytics teams can catch downstream impact before stakeholders lose trust. Guides What is schema drift in analytics? Jul 7, 2026 · 6 min read ## What is schema drift in analytics? Schema drift happens when the structure of your data changes underneath dashboards, reports, and AI answers. Here is how it breaks analytics and how to prevent it. Guides A practical guide to data freshness Apr 30, 2026 · 8 min read ## A practical guide to data freshness Freshness is a promise you make to your stakeholders. Here is how to define it, measure it, and defend it. ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Product Articles from the Silicon Blog | Silicon URL: https://meetsilicon.com/blog/category/product/ Blog / Product # Product How the Silicon platform supports self-serve analytics while keeping one definition for each metric. Product Self-serve analytics without the chaos May 14, 2026 · 5 min read ## Self-serve analytics without the chaos Giving every team access to data is easy. Keeping that access trustworthy is the hard part. ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Data freshness vs data quality vs data reliability | Silicon URL: https://meetsilicon.com/blog/data-freshness-vs-data-quality-vs-data-reliability/ Guides Data freshness vs data quality vs data reliability Fresh data can be wrong. Correct data can be late. And a dataset can be fresh and correct today while remaining impossible to trust tomorrow. Data freshness, quality, and reliability are often collapsed into one green health badge. That makes the badge easy to read and the system hard to understand. Each concept answers a different question. Freshness asks whether the data is current. Quality asks whether it is valid and usable. Reliability asks whether the entire system can repeatedly deliver trustworthy answers as sources, schemas, and business definitions change. Teams need all three, but they should not confuse them. ## The short answer Data freshness is about timeliness. Data quality is about correctness. Data reliability is about whether people can consistently trust the data, metrics, dashboards, and AI answers they use for decisions. A dashboard can be fresh but wrong. A dataset can be high quality but late. A metric can be technically correct but unreliable if nobody knows when a change will break downstream reporting. What is data freshness? Data freshness measures how current the data is relative to the decision being made. For example, an operations dashboard may need data updated every few minutes. A board report may be fine with yesterday’s numbers. Freshness is only meaningful when it is tied to a business expectation. Freshness problems usually sound like: - Why is this dashboard still showing yesterday’s data? - Did the pipeline run today? - Is this number current enough for the meeting? - When was this model last updated? Freshness is important, but it is not enough. Fresh data can still be incorrect. What is data quality? Data quality measures whether data is valid, complete, consistent, and accurate enough to use. Quality issues include missing values, duplicate rows, invalid dates, broken joins, outliers, and mismatched definitions. Data quality problems usually sound like: - Why did this row duplicate? - Why are there nulls in a required field? - Why does revenue not match finance? - Why did the conversion rate spike suddenly? Quality is about the data itself. Reliability is about the broader system around it. What is data reliability? Data reliability is the confidence that the analytics workflow will keep producing trustworthy answers as data changes. It includes freshness and quality, but also goes further. Reliability includes metric governance, dependency mapping, ownership, documentation, schema-drift protection, and impact checks before changes reach production. Reliability problems usually sound like: - Which dashboards will this schema change break? - Who owns this metric? - Why do two teams define active users differently? - Can we trust the AI answer if the upstream model changed? - Did this pull request affect executive reporting? Reliability is what connects technical data health to business trust. ## How they work together Freshness, quality, and reliability reinforce each other. Freshness tells you whether the data arrived on time. Quality tells you whether the data looks right. Reliability tells you whether the system is designed so people can trust the answer repeatedly. If any one is missing, trust breaks. ## A practical example Imagine a revenue dashboard. If the dashboard uses yesterday’s data when the sales leader expects today’s pipeline, that is a freshness issue. If the dashboard double-counts expansion revenue, that is a quality issue. If a source column changes and nobody knows which dashboards or AI answers depend on it, that is a reliability issue. The business experiences all three the same way: the number cannot be trusted. ## Where AI analytics changes the stakes AI analytics raises the importance of reliability because answers are generated dynamically. If the system does not know which metrics are governed, which data is fresh, and which upstream changes affect downstream answers, it can produce confident but unreliable output. That means AI analytics needs more than natural-language query generation. It needs trusted context and reliability checks. ## Where Silicon fits Silicon helps teams connect freshness, quality, and reliability into one analytics workflow. It combines governed metric context, dashboards as code, PR impact checks, schema-drift protection, and AI answers. That helps teams catch problems before business users lose trust. ## The takeaway Freshness, quality, and reliability are not interchangeable. Freshness is timeliness. Quality is correctness. Reliability is sustained trust across the analytics system. Teams need all three if they want dashboards and AI answers that people can actually use for decisions. ## The takeaway Freshness and quality are measurements. Reliability is the operating system that ensures those measurements continue to matter. FAQ ## Frequently asked questions ### What is data freshness? + Freshness measures whether information is current enough for its intended decision. ### What is data quality? + Quality describes properties such as completeness, validity, uniqueness, consistency, and accuracy. ### What is data reliability? + Reliability is the ability of the wider data and analytics system to produce trustworthy outputs consistently over time. ### Can data be fresh but unreliable? + Yes. A freshly updated table may contain duplicates, use a changed schema, or feed inconsistent metric logic. ### Should these be combined into one score? + A summary can help prioritisation, but users still need the underlying signals and affected assets to make a safe decision. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # dbt is where data work becomes software work | Silicon URL: https://meetsilicon.com/blog/dbt-is-where-data-work-becomes-software-work/ Guides dbt is where data work becomes software work ## The warehouse changed. The workflow had to catch up. For a long time, analytics lived in a weird middle place. The business wanted trusted numbers. Analysts wrote SQL to produce them. Engineers owned production systems. BI tools became the place where definitions quietly multiplied. Everyone agreed that revenue should mean one thing, until the finance dashboard, sales dashboard, and board deck each had a slightly different answer. The data usually existed. The workflow was the problem. dbt became popular because it gave analytics work a better operating model: version-controlled SQL, modular models, tests, documentation, lineage, and repeatable production workflows. That sounds like tooling. It is really a shift in responsibility. Analytics code stopped being a pile of useful queries. It became software. ## The old way: SQL as scattered knowledge Every data team knows this version of the story. A stakeholder asks, “What was net revenue last month?” Someone finds a query from the last time this came up. It references a table with a name like orders_cleaned_final. There is a case when block that looks important. Nobody is totally sure who wrote it. The logic gets copied into a dashboard. Two weeks later, someone else copies a slightly different version into another dashboard. Now the business has three answers to one question. Nobody meant to create a metrics problem. They were just trying to move quickly. This is how analytics debt forms: - Business logic gets copied instead of reused - Transformations live inside dashboards - Metric definitions drift across teams - Changes break downstream reports without warning - Data quality checks happen after someone complains - Documentation is either missing or written once and forgotten The result is not just messy code. It is slower decision-making. Every important question becomes a small investigation into whether the data can be trusted. ## The dbt reframe: analytics needs an engineering workflow dbt’s core idea is simple: analysts and analytics engineers should be able to work more like software engineers. Not because every analyst needs to become a backend engineer. Because analytics has the same production problems as software: - Code needs review - Logic needs reuse - Changes need testing - Dependencies need visibility - Documentation needs to live close to the work - Deployments should be repeatable - Breakages should be caught before users find them dbt brings those practices into the transformation layer. The project becomes a graph of models, sources, tests, metrics, and documentation. That context is the real product. The tables matter. The graph matters more. ## Models make logic reusable A dbt model is usually a SQL file that defines a transformation. Instead of burying logic in dashboards or one-off notebooks, the team builds a chain of models that turn raw data into trusted business objects. A common pattern looks like this: - Sources: raw tables from production systems or ingestion tools - Staging models: cleaned, renamed, lightly standardized data - Intermediate models: reusable business logic and joins - Mart models: final datasets built for reporting, analysis, apps, or AI workflows For example, a team might start with raw stripe_payments, salesforce_accounts, and product_events. In dbt, they can turn those into: - stg_stripe__payments - stg_salesforce__accounts - int_customer_revenue - fct_recurring_revenue - dim_customers Now the revenue logic is not trapped in a dashboard tile. It has a name, an owner, dependencies, tests, and documentation. That changes the conversation. Instead of asking, “Which query did we use last time?” The team can ask, “Is fct_recurring_revenue the right model for this question?” Much better. ## Tests move trust upstream Most data quality issues are found too late. A dashboard looks wrong. A stakeholder flags it. The analyst opens the SQL. Someone checks the upstream table. The pipeline ran, but a column started returning nulls. Or duplicate IDs appeared. Or a status value changed from paid to completed and broke a filter. The team fixes it, but the trust damage already happened. dbt tests help move that work upstream. They let teams express assumptions about models and data before those assumptions become dashboard incidents. Out of the box, teams can check common expectations: - A primary key should be unique - A primary key should not be null - A field should only contain accepted values - A foreign key should match records in another model And when the business has its own rules, teams can write custom tests as SQL. That is the important part. Testing is not just “does the pipeline run?” It becomes “does this data still mean what we think it means?” ## Lineage turns breakages into investigations, not mysteries Data pipelines break. That is not the interesting part. The interesting part is how long it takes to understand what broke and who is affected. Lineage gives teams a map. That map is useful in two very normal situations. First, something breaks. A dashboard is wrong. Instead of spelunking through random SQL, the data team can move upstream through the DAG and see which models, sources, or tests are involved. Second, something changes. Maybe engineering renames a column. Maybe the finance team changes revenue recognition logic. Maybe the data team wants to refactor a staging model. Lineage helps answer the scary question: “What will this break?” Without lineage, every change feels risky. With lineage, change becomes inspectable. Still risky, because data work is data work. But inspectable. ## Documentation works when it lives with the code Documentation usually fails because it is separate from the work. Someone creates a data dictionary. It gets stale. Someone makes a Notion page. It gets forgotten. Someone adds dashboard descriptions. They explain the dashboard, but not the transformation logic underneath it. dbt pushes documentation closer to the models themselves. That matters because the model is where the meaning lives. A good model description explains what the dataset represents, how grain works, what each column means, and what assumptions the business logic makes. Good documentation does not need to be poetic. It needs to prevent the next person from making a bad guess. For example: This is not glamorous. It is incredibly useful. The best data teams do not document because documentation is virtuous. They document because future debugging is expensive. ## The semantic layer is the next trust boundary Once models are clean, another problem appears: metrics. Models define useful datasets. Metrics define how the business measures itself. Revenue. Active users. Churn. Gross margin. Pipeline. Retention. If every downstream tool defines those separately, trust drifts again. The spreadsheet has one version. The BI tool has another. The AI agent, unless grounded properly, invents a third. The dbt Semantic Layer is designed to centralize metric definitions on top of dbt models. This helps downstream tools and applications work from consistent business definitions instead of duplicating logic everywhere. This becomes especially important as AI enters the analytics workflow. An AI assistant that answers business questions is only as trustworthy as the context it can access. If “revenue” is just a word in a prompt, the answer is fragile. If “revenue” is a governed metric with a definition, dependencies, permissions, and lineage, the answer has something real to stand on. That is the practical AI story for data teams. Not magic. Context. ## Where dbt fits in the modern data stack dbt does not ingest your data. It does not replace your warehouse. It does not magically make messy source systems clean. It sits in the transformation layer. A typical workflow looks like this: - Data lands in a warehouse or lakehouse from tools like Fivetran, Airbyte, custom pipelines, product databases, or event streams - dbt transforms that raw data into tested, documented models - BI tools, notebooks, reverse ETL tools, apps, and AI agents consume the trusted models and metrics - The dbt project becomes the shared context layer for how the business defines and trusts data This is why dbt has become central to analytics engineering. It is not just where SQL runs. It is where the organization writes down what the data means. ## What good dbt projects get right A dbt project can still become a mess. It is very possible to create a beautiful DAG that nobody understands. The best projects usually share a few habits. ### They keep model layers clear Staging models clean and standardize. Intermediate models handle reusable logic. Marts serve business-facing use cases. When every model tries to do everything, the DAG becomes a junk drawer. ### They test business assumptions Not just primary keys. Good teams test the rules that actually matter: - Paid invoices should have payment dates - Active subscriptions should have customer IDs - Order totals should not be negative unless the record is a refund - Closed-won opportunities should have close dates Generic tests catch structural issues. Business tests catch semantic ones. ### They document the weird parts Every business has weird logic. The problem is not weird logic. The problem is undocumented weird logic. If churn excludes certain customer segments, say so. If revenue recognition changed in Q3, say so. If a column is deprecated but still used by an old dashboard, say so. The weird parts are where trust usually breaks. ### They treat refactoring as normal A dbt project is never finished. Models get renamed. Logic gets consolidated. Tests get added. Old marts get deprecated. Metric definitions evolve. That is healthy. The goal is not to freeze the project. The goal is to make change safer. ## The biggest mistake is treating dbt like a SQL folder dbt can look deceptively simple. It is SQL files, YAML, tests, docs, and a DAG. If you squint, it can feel like a nicer way to organize warehouse queries. That is the trap. A dbt project is not just a folder of transformations. It is the place where the data team encodes how the business works. That means the hard questions are not only technical: - What is the grain of this model? - Who owns this definition? - Is this logic reusable or one-off? - Should this live in staging, intermediate, or marts? - Which assumptions are stable enough to test? - Which downstream teams depend on this model? - What should happen when the definition changes? The SQL matters. But the shape of the project matters more. A messy dbt project can still produce clean tables. For a while. Then the model count grows. Naming conventions drift. Intermediate models become permanent. Business logic gets duplicated. Tests exist, but only for easy things. Documentation explains columns, but not decisions. Now the team has moved the mess from dashboards into dbt. Better, maybe. But not fixed. ## dbt works best when it becomes a shared contract The best dbt projects are not just technically correct. They create agreement. A finance lead should be able to trust that fct_recurring_revenue means the same thing this month as it did last month. A growth PM should know which activation model powers their dashboard. An analyst should be able to change a staging model and understand what might break. An AI assistant should be able to answer a revenue question using the governed metric, not a random interpretation of the word “revenue.” That is the contract. Not a giant governance committee. Not a quarterly taxonomy project that everyone politely ignores. A practical contract, written into the workflow: - Models define reusable business objects - Tests define what must stay true - Docs explain meaning and assumptions - Lineage shows dependencies - Reviews catch risky changes - Metrics centralize the numbers people argue about most This is why dbt is such a natural home for analytics engineering. It gives data teams a way to make trust operational. ## The AI angle: context beats cleverness AI makes this more important, not less. If an AI agent is going to help with analysis, it needs more than access to the warehouse. Raw access is not intelligence. It is just a bigger surface area for confusion. Ask an ungrounded assistant: “Why did revenue drop last week?” It might write a very confident answer. It might even query something. But what does “revenue” mean? Bookings? Recognized revenue? Net revenue? Gross revenue? Does it include refunds? What about test accounts? What about currency conversion? What about the enterprise contract that got backdated? These are not edge cases. These are the job. The value of dbt in an AI workflow is that it gives agents structured context to work with: - Certified models - Metric definitions - Column descriptions - Model lineage - Tests and failures - Business logic - Permissions and governance That context changes the quality of the answer. Without it, AI is guessing over tables. With it, AI can reason over the way the business has already agreed to define itself. Still not magic. Much more useful. ## What changes for the data team When dbt is working well, the data team’s job changes. They spend less time answering the same question in slightly different SQL. They spend less time reconciling dashboards. They spend less time explaining why two reports disagree. They spend more time building the shared layer that makes future work faster. That can look like: - Turning repeated stakeholder questions into reusable marts - Moving fragile dashboard logic into tested models - Adding business-specific tests for high-value datasets - Cleaning up model naming and ownership - Documenting the weird parts before they become incidents - Defining governed metrics for the numbers people use every week - Reviewing changes before they break downstream workflows This is the part that can feel invisible. Nobody celebrates the test that caught a duplicate customer ID before the board dashboard refreshed. Nobody throws a party because a model description saved an analyst thirty minutes. Nobody writes a Slack announcement for a clean lineage graph. But that is the work that compounds. Every trusted model makes the next analysis easier. Every test reduces the blast radius. Every documented assumption saves someone from guessing. Every governed metric makes the next dashboard, notebook, app, or AI answer less fragile. ## The lesson: dbt is operational memory Data teams have always carried a lot of organizational memory. They know why the revenue number changed. They know which source table is unreliable. They know why churn excludes a certain segment. They know which dashboard is deprecated but still somehow used in the Monday meeting. For a long time, that memory lived in people’s heads, old SQL, Slack threads, and tribal knowledge. dbt gives teams a better place to put it. Not all of it. There will always be context outside the transformation layer. But enough of it to make the work more durable. The model remembers the logic. The test remembers the assumption. The documentation remembers the meaning. The lineage remembers the dependency. The metric remembers the agreement. That is the real reason dbt matters. It is not just a transformation tool. It is a way for data teams to turn scattered knowledge into shared infrastructure. ## Where this goes next The modern data stack has spent years getting data into the warehouse, transforming it, and showing it in dashboards. The next phase is about making that data usable across more workflows: analysis, apps, operations, embedded reporting, customer-facing experiences, and AI agents that can actually do useful work. That makes the transformation layer even more important. Because the more places data goes, the more trust matters. dbt sits at a critical point in that system. It is close enough to the raw data to shape it, and close enough to the business to encode what it means. That is a powerful place to be. The warehouse gave teams a central place to store data. dbt gave teams a central place to define it. And in a world where every tool wants to generate answers, that definition layer becomes the difference between faster insight and faster nonsense. ## Getting started If you are starting with dbt, do not begin by trying to model the entire company. Start with one important business workflow. Pick a recurring question people already argue about: - What is revenue? - Which customers are active? - How do we define churn? - What counts as a qualified lead? - Which product events matter for activation? Then build the smallest trusted path: - Define the raw sources - Create clean staging models - Build one business-facing model - Add tests for the assumptions that matter - Document the model and its columns - Expose it to the dashboard, notebook, app, or AI workflow that needs it - Use lineage to understand what depends on it before changing it That is enough to start. Not a perfect data platform. Not a giant transformation program. Just one trusted path from raw data to a real decision. Then do it again. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides How to monitor dashboard impact before merging a PR ### How to monitor dashboard impact before merging a PR ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # From prompt to production analytics in minutes | Silicon URL: https://meetsilicon.com/blog/from-prompt-to-production-analytics-in-minutes/ Data Science From prompt to production analytics in minutes The first AI-generated chart feels like the future. The tenth contradictory chart feels like the past wearing a chatbot. Natural-language analytics solves an interface problem: people can ask a question without writing SQL. Production analytics has to solve a harder problem—making sure the answer uses the organisation’s definitions, permissions, current data, and review standards. A prompt can describe intent. It cannot define revenue, choose the correct customer grain, or know which late source makes today’s answer unsafe. Those agreements have to exist before the model is asked to reason with them. Getting from prompt to production is therefore less about removing the analytics workflow and more about making the trusted workflow fast enough to feel invisible. That distinction matters. ## The old workflow was too slow For years, analytics teams lived in the request queue. A stakeholder needed a cohort cut. A sales leader wanted pipeline by segment. Finance needed revenue by region with one small but important caveat. Someone filed a ticket, explained the question, waited for an analyst, clarified the definition, reviewed a dashboard, asked for a tweak, and then copied the final screenshot into Slack. Nobody loved this workflow. Business teams moved slower than their questions. Data teams became bottlenecks for every recurring cut of the business. Analysts spent too much time translating familiar asks into familiar SQL instead of doing higher-leverage work. So when AI made it possible to ask the warehouse a question directly, the appeal was obvious. Of course teams want that. They should want that. The trap is assuming the ticket queue was the only problem. The real problem was not just that analytics was slow. It was that analytics had to be trusted. ## A prompt is not a metric definition A prompt can describe what a person wants. It cannot, by itself, guarantee what the business means. Ask for “active users” and the system still needs to know whether that means logins, product events, paid accounts, weekly active workspaces, or something else entirely. Ask for “revenue” and it needs to know whether to include refunds, credits, taxes, discounts, churned subscriptions, and late-arriving invoices. The model can guess. That is exactly the problem. In production analytics, a good answer depends on governed context: - Which tables are approved for this question - Which metric definition should be used - Which filters are default business logic and which are user choices - Which joins preserve the right grain - Which rows the user is allowed to see - Whether the source is fresh enough for the decision - Who owns the dashboard, metric, or model if something looks wrong Without that context, natural language becomes a faster way to create metric drift. ## From prompt to production means adding the workflow back The goal is not to slow AI down until it looks like the old ticket queue. The goal is to connect AI to the parts of analytics that make answers safe to use. A production-ready AI analytics workflow looks less like “ask anything against raw tables” and more like this: The user asks a business question. The question can be messy, because people are messy. “Why did enterprise activation drop last week?” is a good question even if it does not name the exact metric, table, or date grain. The system maps the question to governed context. Before writing SQL, the system should identify the relevant metrics, semantic definitions, dbt models, dashboard history, and business glossary entries. It should know that activation has a canonical definition. It should know which model powers it. It should know who owns it. The query is generated inside constraints. AI can draft the query, but it should draft within the approved warehouse, schema, permissions, and metric layer. The fastest answer is not useful if it quietly bypasses the trusted definition. The result is checked before it is trusted. Freshness, row counts, joins, filters, and unusual distribution changes should be visible. If the answer depends on stale or suspicious data, the system should say so. The output explains itself. A production answer should show how it was produced: which definition it used, which filters were applied, what data was queried, and where someone can inspect the lineage. That is how AI moves from impressive demo to useful workflow. ## The semantic layer becomes the safety rail A semantic layer is not just a nice abstraction for dashboards. In AI analytics, it becomes the safety rail. When the model answers through governed metrics, it does not have to rediscover the business every time someone asks a question. Revenue means revenue. Active users means active users. Gross margin means gross margin. The definitions are reusable, reviewable, and connected to the same logic the rest of the company uses. This collapses a false tradeoff. Business users get speed. Data teams keep governance. The answer can be conversational without becoming a free-for-all against the warehouse. That is the whole point: self-serve should not mean self-invented. ## Review does not disappear. It moves earlier. There is a common fantasy that AI analytics removes the need for review. It does not. It changes what needs review and when. Instead of reviewing every one-off request after it has already become a dashboard, data teams can review the underlying definitions, certified datasets, permissions, and system behavior. They can decide which metrics are safe for self-serve. They can monitor where generated answers appear, which questions recur, and which definitions need better documentation. The analyst is no longer the person manually writing every query. They become the person designing the trusted paths people use to ask better questions. That is a better job. ## What should be visible to the user If AI is going to answer business questions, it needs to be honest about its work. A useful answer should make a few things obvious: - The metric or definition used - The time range and filters applied - The source tables or models queried - Freshness of the underlying data - Any assumptions made to interpret the question - Whether the user has permission to see the full result - Suggested follow-up questions that stay within the same governed context This is not just transparency theater. It is how people build trust over repeated use. The first time an answer cites the right definition, the user relaxes a little. The second time it flags stale data instead of pretending, they trust it more. The third time it links back to lineage and an owner, the workflow starts to feel dependable. Trust compounds through small proofs. ## Production analytics is a loop, not a one-shot answer The best version of AI analytics is not a single prompt that returns a single chart. It is a loop. A user asks a question. The system answers with governed context. The user explores a follow-up. The useful analysis becomes a saved view, dashboard, alert, or data app. The data team sees which questions are repeated and turns them into better certified workflows. The semantic layer improves. The next answer gets better. That is how speed and rigor reinforce each other instead of fighting. AI should make analytics feel more immediate, but it should also make the organization smarter about its own definitions, workflows, and decisions over time. ## Where Silicon fits Silicon is built around a simple belief: AI analytics only works when trust is part of the system, not an afterthought. That means answers need to connect to governed metrics, dashboard context, data freshness, permissions, and the lineage behind the number. It also means data teams need visibility into how questions are being answered, where definitions are being used, and what might break before the business notices. A prompt can start the workflow. It should not be the whole workflow. The future of analytics is not slower because governance matters. It is faster because governance is built in. That is how you get from prompt to production: not by removing rigor, but by making the trusted path the fastest one. ## The takeaway A prompt should begin the workflow, not replace it. The future is fast because rigor is built into the path, not because rigor was removed. FAQ ## Frequently asked questions ### What is production AI analytics? + It is natural-language analysis grounded in governed metrics, semantic context, permissions, freshness checks, lineage, and repeatable review rather than unconstrained queries against raw tables. ### Why is generated SQL not enough? + SQL can be syntactically correct while using the wrong metric, grain, join, time field, permissions, or stale source. ### What should an AI answer explain? + It should expose the metric definition, filters, period, sources, freshness, assumptions, warnings, and a route to inspect lineage or ownership. ### How does an AI analysis become a production asset? + Repeated or valuable analysis should be reviewed, tested, assigned an owner, and saved as a governed dashboard, alert, report, or data app. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Hiring a data engineer vs working with a data partner | Silicon URL: https://meetsilicon.com/blog/hiring-a-data-engineer-vs-working-with-a-data-partner/ Guides Hiring a data engineer vs working with a data partner Most startups start thinking about a data hire after a specific moment. The board asks for revenue by plan and three people send three different numbers, or the founder spends a weekend rebuilding a spreadsheet before an investor update. By then the company runs on a dozen apps, and nobody owns the work of bringing their data into one place. There are three common ways to get that work done. You can hire a data engineer, bring in a consultant for a project, or work with a data partner. Each suits a different company, and the time and cost differ more than most founders expect. ## Option 1: Hire your first data engineer A data engineer connects the company's apps, brings the data into one place and keeps it up to date. Many startups hire an analytics engineer instead, who also defines the key metrics and builds the first dashboards. The main cost is time. KORE1, a recruiting firm, puts the internal search for one data engineer at 6–10 weeks, followed by two to four months of ramp-up, so a company typically waits five to seven months from the decision to reliable data (KORE1). One senior analytics engineer and the tools they need cost about $120–227k a year at a company of around $5M revenue (Valiotti). A single hire also means the knowledge of how every number is built sits with one person. When they take a holiday or leave, requests wait. Hiring makes sense when data is part of the product itself, when you need someone in the room every day, or when there are already more requests than one person outside the company could handle. ## Option 2: Bring in a data analytics consultant A consultant is usually hired for a project with an end date, such as reporting for a fundraise or a move off an old reporting tool. Good consultants work quickly because they have done the same setup many times. The difficulty comes after the project ends. Metric definitions need updating when pricing or plans change, new apps get added, and dashboards break when something in the data changes. Unless someone on your team takes the work over, the setup starts to drift within a few months, and the next fix is a new contract. A consultant fits well when you already have a data team that can maintain the work, or when you need specialist work beyond reporting, such as a pricing model. There is a longer comparison on our Silicon vs hiring a data analytics consultant page. ## Option 3: Work with a data partner A data partner builds your data foundation and keeps maintaining it as the company grows. At Silicon, that means connecting the apps your company runs on, defining each key metric with the person who owns it, and setting up dashboards and an AI analyst that anyone can ask in Slack. After launch, requests from any team go to a shared channel, and we update the definitions when pricing, plans or tools change. The foundation is usually live in about ten weeks, which is shorter than the time it takes to hire and ramp up one engineer. Our AI analytics platform checks each change to your data and flags the dashboards it affects, and we make the fix. A data partner suits companies with no data team, or with one data person answering requests from every team. If you later build your own data team, they start from documented metric definitions and data models. More detail is on the data partnership page. ## How the three compare | Hire a data engineer | Data analytics consultant | Data partner Time to reliable data | 5–7 months, including hiring and ramp-up | A few weeks, for the project's scope | About ten weeks for the foundation After launch | One person maintains it | Handed over to your team | Maintained as the company grows Cost | About $120–227k a year, with tools | Hourly, daily or per project | A fixed-scope build, then monthly or annual When someone leaves | Much of the knowledge leaves with them | Depends on the handover | Definitions are documented and the partner covers the work ## How to decide Three questions usually settle it. - Is there someone who will maintain the data after it is set up? If not, a one-off project tends to drift, and a hire or a partner is the safer choice. - How soon do you need reliable numbers? If a fundraise or board meeting is less than six months away, a first hire is unlikely to be ready in time. - Is data part of what you sell, or does it run the business? Products built on data usually need engineers in-house. For reporting, key metrics and everyday questions from each team, a partner can cover the work. If you're weighing these options, talk to Sujay, the founder, about your setup. If one person can still keep track of every number by hand, we'll tell you it's too early on the first call. FAQ ## Frequently asked questions ### Should a startup's first data hire be a data engineer or a data analyst? + If nobody has connected the company's data yet, a data engineer or analytics engineer usually comes first, because an analyst needs reliable data to work from. If the data is already in one place and each key metric is defined, an analyst can start answering questions straight away. ### How long does it take to hire a data engineer? + KORE1 puts an internal search at 6–10 weeks, followed by two to four months of ramp-up, so most companies wait five to seven months from the decision to reliable data. ### Can we build our own data team later if we start with a data partner? + Yes. Your new hires start from documented metric definitions and data models, and you decide how much of the work the partner keeps. ### What does a data partner cost? + It depends on how many tools your company runs on and how much ongoing help you need. Silicon gives a written scope and an exact quote after a short discovery. Keep reading ## More from the blog Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work Guides How to monitor dashboard impact before merging a PR ### How to monitor dashboard impact before merging a PR ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # How AI analytics works with dbt metrics | Silicon URL: https://meetsilicon.com/blog/how-ai-analytics-works-with-dbt-metrics/ Guides How AI analytics works with dbt metrics At 9:07 on a Monday morning, a sales leader asks an AI analyst a simple question: How much enterprise revenue did we close last quarter? The answer arrives before the coffee does. There is a chart, a percentage change, and a neat paragraph explaining that enterprise revenue grew 14%. It looks polished enough to paste into the weekly leadership update. Then Finance replies with a different number. Nothing is obviously broken. The SQL ran. The warehouse returned rows. The chart is mathematically correct. The problem is that the AI used booked contract value, while Finance reports recognized revenue after credits and currency adjustments. Both calculations are defensible. Only one is the company metric. This is the central problem with AI analytics: generating a query is easy. Knowing which version of the business to query is hard. ## AI can write SQL. It cannot invent agreement. Natural-language analytics removes a real bottleneck. People no longer need to know table names, SQL syntax, or which date function works in the warehouse. They can ask a question in the language they already use at work. But natural language is full of compressed assumptions. Revenue, active user, qualified lead, retained customer, and conversion are not columns waiting to be selected. They are agreements. Each one contains decisions about filters, time windows, joins, exclusions, and grain. An AI model presented with raw tables has to reconstruct those agreements from clues. It can inspect column names, documentation, and past queries. It can make a plausible guess. Plausibility is useful for exploration. It is dangerous when the answer is presented as a governed fact. dbt metrics change the problem. Instead of asking the model to rediscover the company every time, they give it a governed definition it can reuse. ## What a dbt metric gives the AI system A dbt metric is more than a formula. In a semantic model, the metric sits alongside the entities, dimensions, measures, and time grains that describe how it can be queried. That context constrains the space of valid answers. For an AI analytics system, this creates four important advantages: - A canonical calculation: the system does not invent a new revenue formula for each prompt. - Valid dimensions and joins: it knows how the metric can be broken down without changing its grain. - Consistent time logic: week, month, quarter, and fiscal periods resolve through approved definitions. - Explainable context: the answer can identify the metric, filters, and models it used. The result is not that the AI becomes incapable of making mistakes. The result is that a large class of avoidable mistakes—wrong table, wrong join, wrong aggregation, wrong date—becomes much less likely. ## From a question to a governed answer Consider a product manager asking: Why did activation drop for enterprise accounts last week? A raw-table agent might search for columns containing activation, enterprise, and account, then assemble a query. A governed system follows a different path. - Interpret the business terms. The system maps activation to the approved activation metric and enterprise account to a governed account segment. - Resolve the metric graph. It identifies the semantic model, entities, dimensions, measures, and valid joins needed for the question. - Apply the user’s context. Permissions, default filters, timezone, and the requested comparison period are added before execution. - Generate and run the query. SQL becomes an implementation detail produced from governed definitions rather than raw-table guesswork. - Validate the result. Freshness, missing data, unusual distributions, and important caveats are checked before the answer is presented. - Explain the answer. The response shows the metric definition, filters, period, sources, and follow-up paths. That workflow feels slightly less magical than asking an unconstrained model to search the entire warehouse. It is also far more useful. Production analytics should optimize for repeatable trust, not demo-day surprise. ## The metric layer does not remove exploration Governance is often described as the opposite of curiosity: approved dashboards on one side, free exploration on the other. That is a false choice. A governed metric can be the starting point for exploration. The user can compare segments, change time ranges, investigate drivers, and ask follow-up questions. What stays stable is the meaning of the metric itself. Think of it like a map. A map does not decide where you must go. It gives you a shared coordinate system so that north does not change halfway through the journey. This distinction matters for AI because conversational interfaces encourage iteration. One question becomes five. The user asks why revenue changed, then which segment moved, then whether the shift came from volume, price, churn, or currency. If every follow-up silently changes definitions, the conversation becomes an increasingly confident work of fiction. ## Where AI analytics still breaks A metrics layer solves definition drift. It does not solve every reliability problem. The cleanest semantic model in the world can still produce a bad answer if the data underneath it is stale, incomplete, or unexpectedly changed. ### The source is late The metric is correct, but yesterday’s billing data has not arrived. The system should say the result is incomplete rather than confidently comparing partial revenue with a complete prior period. ### The schema changed A source field was renamed or a model changed grain. The metric may still compile while producing a different result. Contracts, tests, lineage, and change-impact checks are still required. ### The entity is wrong Customer, account, workspace, and subscription are not interchangeable. An AI system needs explicit entities and relationships or it can answer the right question at the wrong grain. ### The user lacks context A correct number without its definition can still be misunderstood. The response needs enough context for the reader to know what was counted, what was excluded, and whether the answer fits the decision. ## What a trustworthy AI answer should show Most AI interfaces optimize for a clean final answer. Analytics interfaces should expose a little productive friction. The user should be able to inspect the reasoning without reading generated SQL line by line. - The metric name and plain-language definition - The dimensions, filters, and comparison period - The source semantic models or governed datasets - The latest trustworthy data timestamp - Warnings about missing data, unusual changes, or assumptions - A link to lineage, ownership, or the underlying analysis These details are not decorative transparency. They teach the user how the business defines the metric. Over time, the interface becomes a way to distribute institutional knowledge, not merely query results. ## How to implement AI analytics with dbt metrics Teams often begin by connecting a model to the warehouse and hoping better prompts will produce better answers. A more durable rollout starts with the questions the business asks repeatedly. - Choose a small set of decision-critical metrics. Revenue, activation, pipeline, retention, and churn are common starting points. - Define the metrics and semantic models in dbt. Make entities, dimensions, measures, time grains, and ownership explicit. - Test the assumptions that can change the meaning. Validate uniqueness, accepted values, relationships, and model contracts. - Connect the AI layer to governed metric APIs or metadata instead of exposing raw warehouse tables as the default context. - Return explanations with every answer. Include definitions, filters, freshness, and lineage. - Review real questions. Use failed or ambiguous prompts to improve metric definitions and semantic documentation. The final step is easy to underestimate. Real user questions reveal gaps no architecture diagram will show. Someone will ask for expansion revenue when only total revenue exists. Someone will use customer to mean account. Someone will ask for last quarter while thinking in the company’s fiscal calendar. Those are not prompt failures. They are opportunities to make the organization’s definitions clearer. ## What this changes for the data team AI analytics does not eliminate analytics engineering. It changes where the leverage sits. Instead of writing every query, data teams design the trusted paths through which many questions can be answered. They invest in semantic models, metric definitions, documentation, contracts, lineage, and evaluation. They monitor which questions recur and which answers produce confusion. The work becomes less about translating each request and more about building a system that translates the business consistently. That is a meaningful shift. It moves the data team from queue manager to product steward. ## The real product is not the chatbot The visible product may be a conversational interface. The durable product is the agreement underneath it: which metrics matter, what they mean, how they can be sliced, who can see them, and how the organization knows when they are unsafe. dbt metrics give AI analytics a foundation that raw-table prompting cannot. They turn natural language from a shortcut around governance into an interface for governance. That is the difference between an AI system that can produce a chart and one that can participate in a real business decision. FAQ ## Frequently asked questions ### Can an AI analyst use dbt metrics directly? + Yes. The implementation varies, but the AI layer can use metric definitions, semantic metadata, or a semantic-layer query interface to resolve business questions into governed metrics and dimensions. ### Do dbt metrics guarantee correct AI answers? + No. They reduce ambiguity in business logic, but correctness still depends on source quality, freshness, permissions, entity relationships, tests, and how faithfully the AI system uses the governed context. ### Why not let the model learn from existing dashboards? + Dashboards are useful context, but they often contain duplicated or local calculations. Learning from them can reproduce the inconsistency the semantic layer is meant to remove. Certified dashboards can help, but governed definitions should remain the source of truth. ### Is a semantic layer necessary for natural-language analytics? + It is possible to generate answers without one, especially for exploratory work. As usage grows, a semantic layer becomes increasingly important for consistent metrics, valid joins, permissions, and explanations. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # How to monitor dashboard impact before merging a PR | Silicon URL: https://meetsilicon.com/blog/how-to-monitor-dashboard-impact-before-merging-a-pr/ Guides How to monitor dashboard impact before merging a PR The pull request was green. Two days later, the board dashboard was not. The change was sensible: Finance had agreed on a better revenue definition, and an analytics engineer updated the model. Tests passed because the code did exactly what the new definition required. What the review did not show was everything that would change with it: the executive dashboard, Monday sales report, renewal alert, and AI answer that all depended on the old calculation. Analytics code review is good at asking whether a change works. Impact analysis asks the more important question: what will this change make people believe? It is also: “What will this change make people believe tomorrow?” Code review is missing the business graph A modern analytics stack is a graph, but most code review still treats it like a folder of files. A dbt model feeds another model. That model powers a metric. The metric appears in three dashboards. One of those dashboards is embedded in a weekly business review. Another is used by Sales leadership. Another is quietly referenced by an AI assistant that answers “how are we doing?” questions for half the company. The person reviewing the PR might see the model diff. They probably do not see the full blast radius. That is how small changes become trust problems. A column rename breaks a dashboard tile. A join change doubles rows in a downstream metric. A grain change makes conversion rate look better for reasons nobody can explain. A freshness change means the Monday dashboard is technically correct, but too late for the Monday meeting. None of these are exotic failures. They are the ordinary maintenance work of a data team. The problem is that the impact shows up somewhere else. ## The old workflow: merge first, investigate later The usual workflow is backwards. A data team merges a change, something downstream looks weird, and then everyone starts reconstructing the story after the fact. Which model changed? Which dashboards use it? Did the metric definition move? Was the dashboard already stale? Who owns this report? Is this a data issue, a semantic layer issue, or did someone change the business logic on purpose? This is slow, but worse, it is corrosive. Every unexplained dashboard change teaches the business to trust analytics a little less. The data team feels it too. Analysts end up in detective mode, tracing lineage manually, checking BI tools, reading old PRs, searching Slack, and asking around for context that should have been visible at review time. The fix is not to slow every PR down. Nobody wants an analytics process where changing a column requires a steering committee. The fix is to move impact into the PR itself. ## The new workflow: review the change and the consequence A good PR impact check should answer a simple set of questions before merge: Which dashboards depend on this change? Which governed metrics could move? Which AI answers or semantic definitions rely on this context? Are any executive, customer-facing, or operational reports affected? Who owns the affected assets? Does this change need migration notes, stakeholder review, or just a normal merge? That sounds obvious, but it changes the review conversation completely. Instead of reviewing a model in isolation, the team reviews the model as part of the business system it supports. The reviewer can still look at SQL, tests, and naming, but now they can also see that this change touches “Pipeline Health,” “ARR Forecast,” and the AI answer used by Sales Ops every Monday morning. That does not mean the PR should be blocked. It means the team can make a grown-up merge decision. Some changes are safe. Some need a dashboard owner tagged. Some need a metric note. Some should be split into a migration PR. Some are expected to change numbers, and the important thing is making that expectation visible before someone screenshots the old chart and starts a panic thread. ## This gets more important with AI analytics Dashboards are not the only downstream surface anymore. As teams add AI analytics, semantic layers, metric stores, and natural language interfaces, the same model or metric definition can feed both a dashboard and an answer. If the underlying context changes, the answer changes too. That is good when the change is intentional. It is terrifying when nobody knows it happened. AI does not remove the need for governance. It raises the cost of missing context. An agent can only give trusted answers if it understands the governed definitions, freshness, lineage, permissions, and business meaning behind the data it is using. So PR impact checks need to include more than BI assets. They need to include the semantic and AI layer as part of the downstream graph. If a PR changes the definition of active users, the system should not only tell you which dashboard tiles might move. It should also tell you which recurring questions, saved answers, or AI workflows might now produce a different result. That is the difference between “AI on top of your warehouse” and AI that is actually connected to how your business thinks. ## What Silicon changes Silicon is built around this idea: analytics work should carry its context with it. When a data team changes a model, metric, or schema, Silicon helps connect that change to the dashboards, governed definitions, owners, and AI answers that depend on it. The goal is not to create more process. The goal is to put the right context where the team already works: before merge. That means reviewers can see the likely dashboard impact while they are still reviewing the PR. Owners can be brought in before the numbers change. Metric shifts can be explained before they show up in a meeting. AI answers can stay tied to the same governed context as the rest of the analytics workflow. In practice, this turns PR review from a code-only checkpoint into a trust checkpoint. Not heavier. Smarter. ## The merge button should not be a cliff Data teams are always balancing speed and trust. Move too slowly, and the business works around you. Move too fast, and the business stops believing the numbers. PR impact checks are one of the rare places where that tradeoff can get better. If you can see what a change affects before it ships, you can move faster with less cleanup. You can tell stakeholders what changed before they ask. You can keep dashboards, metrics, and AI answers aligned without turning every update into a fire drill. The best analytics teams will not be the ones that never change their models. They will be the ones that can change them confidently. Because the PR was never just about code. It was about what the business would trust next. ## The takeaway The merge button should not be a cliff between technical correctness and business surprise. FAQ ## Frequently asked questions ### What is dashboard impact analysis? + It identifies the dashboards, metrics, reports, alerts, embedded views, and AI workflows affected by a proposed data or model change. ### Why should impact be checked before merge? + Before merge, the change is cheap to revise, owners can review it, and downstream users can prepare. After deployment, the same discovery becomes incident response. ### What information belongs in an analytics PR? + Include the business change, affected assets, schema or grain changes, before-and-after values, reviewers, rollout plan, and rollback path. ### Can lineage automate impact analysis? + Lineage can discover dependencies, while tests and previews validate known behaviour. Human review is still needed to judge business meaning and risk. ### How does AI change the blast radius? + AI answers and agents become additional downstream consumers of metrics, semantic context, and models, even when no visible dashboard depends on the change. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # How to prevent broken dashboards after schema changes | Silicon URL: https://meetsilicon.com/blog/how-to-prevent-broken-dashboards-after-schema-changes/ Guides How to prevent broken dashboards after schema changes The dashboard that breaks on Tuesday was usually changed in a pull request on Friday. The code review may have been careful. Tests passed. The model built. What nobody reviewed was the business blast radius: the executive chart, scheduled report, embedded metric, and AI answer that depended on the old shape. Dashboards sit at the end of a long chain of assumptions. Preventing breakage requires moving dashboard context upstream, to the moment when a change is still cheap to understand and easy to revise. That moment is before merge. ## Why dashboards break after schema changes Dashboards sit at the end of a long dependency chain. They depend on source tables, transformations, semantic definitions, joins, filters, and metric logic. When one of those layers changes, the dashboard may fail in one of three ways. First, it can fail loudly. A chart errors. A query refuses to run. A report does not send. Second, it can fail quietly. The dashboard loads, but the number is wrong. A filter removes valid rows. A join duplicates revenue. A renamed status value creates a misleading breakdown. Third, it can fail socially. The dashboard may be technically correct, but stakeholders no longer trust it because previous changes broke similar outputs. The third failure mode is the most expensive. Once trust is gone, every number becomes a debate. The prevention workflow Preventing broken dashboards is not about stopping teams from changing data models. It is about making change visible before it affects the business. ### Track downstream dependencies Before changing a model or column, teams need to know what depends on it. That includes dashboards, reports, alerts, metrics, and AI-generated answers. Without dependency mapping, a schema change is a blind merge. ### Run impact checks during pull requests The right time to catch dashboard impact is before the change ships. Pull request checks should show which business-facing assets are affected, what kind of risk exists, and whether the change needs review. This moves analytics reliability into the same workflow engineering teams already use for software quality. ### Connect dashboards to governed metrics Dashboards are easier to protect when they are built on governed definitions instead of one-off logic. A semantic layer or metrics layer gives teams a shared source of truth for revenue, activation, retention, pipeline, and other business concepts. When the metric definition is explicit, impact is easier to reason about. ### Monitor for silent changes Not every dashboard failure throws an error. Teams should monitor for unexpected null rates, row-count shifts, join explosions, freshness issues, and unusual metric movement. The goal is to catch suspicious changes before a stakeholder catches them in a board deck. ### Make ownership clear A broken dashboard often crosses team boundaries. The source owner changed a field. The analytics engineer maintained the model. The business team consumed the dashboard. Clear ownership keeps issues from bouncing between teams. ## Where AI makes this more important AI analytics raises the stakes. If users can ask natural-language questions and generate answers instantly, those answers need to rely on trusted context. Otherwise, AI simply produces wrong answers faster. Schema changes should not just be checked against dashboards. They should be checked against the metric context and semantic definitions that AI uses to answer questions. ## Where Silicon fits Silicon helps teams connect governed metric context, dashboards as code, PR impact checks, schema-drift protection, and AI answers in one workflow. Instead of learning that a dashboard broke from a stakeholder, teams can see the downstream impact of a change before it lands. That is the difference between reactive analytics support and reliable self-serve analytics. ## The takeaway Dashboards break when upstream changes are invisible to downstream users. To prevent that, analytics teams need dependency mapping, pull request impact checks, governed metrics, and reliability monitoring. The goal is not to avoid change. The goal is to make every change safe enough that the business can keep trusting the numbers. ## The takeaway The aim is not to protect dashboards from change. It is to protect users from discovering that change after they have already acted on it. FAQ ## Frequently asked questions ### Why do dashboards break after schema changes? + Dashboards depend on fields, joins, grain, filters, metrics, and semantic models. Upstream changes can invalidate any of those assumptions. ### What should be checked before merging a schema change? + Review downstream lineage, model contracts, affected metrics, dashboard owners, before-and-after values, and the rollout or rollback plan. ### Can tests catch every dashboard issue? + No. Tests catch known technical and business rules. Human review is still needed for meaning, presentation, and whether a changed result remains appropriate for the decision. ### How should breaking changes be released? + Deprecate interfaces, provide migration windows, notify owners with affected assets, compare outputs, and monitor after deployment. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # How we think about data trust at Silicon | Silicon URL: https://meetsilicon.com/blog/how-we-think-about-data-trust-at-silicon/ Company How we think about data trust at Silicon The most expensive analytics failure is not a broken chart. It is the meeting after the chart breaks, when everybody brings their own number. Once trust disappears, the cost spreads. Analysts re-check queries. Finance rebuilds calculations. Leaders delay decisions. Teams create private spreadsheets because the shared system no longer feels dependable. At Silicon, we treat trust as an operating property rather than a brand promise. A trustworthy system can explain where an answer came from, whether the data is current, what changed, and who owns the result. Speed matters. But the value of speed compounds only when confidence compounds with it. ## Trust is built before the dashboard A dashboard is usually where trust gets judged. It is rarely where trust gets created. By the time a metric appears in a board deck, a lot has already happened. Data was ingested. Schemas were interpreted. Transformations ran. Business logic was encoded. Tests passed or did not. Definitions were chosen. Permissions applied. A dashboard or AI answer selected the final view. If trust is missing from that upstream chain, the dashboard cannot magically add it. This is why we care about the boring infrastructure: ## Freshness checks Schema monitoring Data quality tests Lineage Ownership Metric definitions Impact analysis Review workflows Documentation close to the code These are not side quests. They are the product surface underneath every trusted answer. ## The old model: trust through human memory Every data team has people who know where the bodies are buried. They know not to use that customer table. They know the revenue definition changed in Q3. They know the old dashboard is deprecated but still linked in the sales wiki. They know the pipeline runs late on Mondays because of a vendor export. They know which dbt model is reliable and which one was supposed to be temporary two years ago. This knowledge is incredibly valuable. It is also a terrible place for the organization to store trust. When trust lives in human memory, every important question creates coordination work. Ask the analyst. Ask the analytics engineer. Search Slack. Read the old PR. Hope someone remembers why a metric excludes a certain customer segment. Silicon’s view is simple: the system should remember more. The model should remember the logic. The metric should remember the definition. The lineage should remember the dependency. The test should remember the assumption. The dashboard should remember its freshness. The AI answer should remember where it came from. That is how trust compounds. Governance should feel like leverage, not friction Governance has a reputation problem. For a lot of teams, it means committees, permissions, stale catalogs, and someone saying no from a spreadsheet. That is not the kind of governance we are interested in. Good governance is a workflow accelerator. It gives people safe defaults. It tells them which metric to use. It warns them when data is stale. It shows what a change will affect. It lets AI answer from approved definitions. It lets business users explore without wandering through the raw warehouse with a flashlight. The best governance is felt as clarity. You know which answer to trust. You know who owns it. You know what changed. You know what is safe to build on. That is not bureaucracy. That is leverage. AI makes trust more important, not less AI has changed what teams expect from analytics. A user should be able to ask a question in plain English. A data team should be able to generate analysis faster. A dashboard should be a starting point, not the end of exploration. Agents should help with the repetitive work that used to sit in ticket queues. We believe in that future. But AI does not remove the need for trust. It raises the stakes. An AI assistant with raw warehouse access can sound confident while using the wrong table, stale data, or a metric definition nobody approved. It can make analytics feel faster while quietly making it less reliable. The useful version of AI analytics is different. It is grounded in governed context: Certified metrics Model and column descriptions Lineage ## Freshness status Data quality signals Permissions Business definitions Relevant dashboards and prior analysis When an agent has that context, it can do more than generate a plausible answer. It can explain, warn, cite its sources, respect access rules, and help users reason through the data. Cleverness is not enough. Context is the moat. ## Trust should be visible A trusted analytics system should not ask users to blindly believe it. It should show its work. That does not mean overwhelming every user with implementation details. It means exposing the right trust signals at the right moment. For a business user, that might be: This metric is certified Data is current through 8:00 AM This answer uses the approved revenue definition This dashboard is owned by Finance Analytics A source feeding this chart is delayed For a data team, it might be: This PR affects seven dashboards This model has failing uniqueness tests This column is used downstream by a production report This metric definition changed last week This AI answer relied on these models and filters Trust is not a feeling. It is information presented in context. The workflow we want to enable Here is the workflow we think analytics should support. A data team defines trusted models and metrics once. Those definitions carry through dashboards, analysis, apps, and AI answers. When someone changes a model, they can see the downstream impact before merging. When data is stale, users see the warning where they are making the decision. When an AI agent answers, it uses governed definitions and explains what it used. When a stakeholder asks a repeated question, the answer becomes part of the shared layer instead of another one-off artifact. That is the loop. Build trusted context. Use it everywhere. Learn from usage. Improve the shared layer. Repeat. The data team stops being the department of bespoke answers and becomes the team that makes answers safer, faster, and more reusable across the company. ## What we optimize for We optimize for a few principles. Make the trusted path the easiest path People follow defaults. If the easiest way to answer a question uses certified metrics and fresh data, trust improves without a training campaign. Bring impact into review A change is only risky relative to what depends on it. Show downstream dashboards, metrics, and owners before the change ships. Keep context close to work Docs, tests, ownership, lineage, and freshness should live where people build and ask, not in a separate place they remember during audits. Treat AI as a workflow surface AI should not be a magic layer pasted on top of the warehouse. It should be another interface to governed data, with the same expectations around permissions, lineage, freshness, and definitions. Make uncertainty explicit If data is stale, say so. If a definition changed, show it. If a test failed, warn the user. Hidden uncertainty is what breaks trust. ## Trust creates speed Trust can sound like the opposite of speed. It is not. Low-trust analytics is slow in all the expensive ways. People double-check numbers. Analysts reconcile dashboards. Leaders ask which report is right. Teams avoid using self-serve tools because they are not sure what they will get. AI answers need human review because nobody knows what they were grounded in. High-trust analytics removes that drag. People ask better questions because they trust the starting point. Data teams ship changes faster because impact is visible. Business users explore more because the system guides them. AI becomes useful because it is connected to definitions, tests, and permissions. Trust is what makes speed durable. ## Where this goes The analytics surface area is expanding. Dashboards are still here. So are notebooks, embedded apps, operational workflows, Slack threads, customer-facing reports, and AI agents that can produce answers on demand. That expansion makes the trust layer more important. The more places data goes, the more expensive inconsistency becomes. The more people can ask questions, the more important shared definitions become. The more AI can generate, the more important grounding becomes. This is the system we are building toward at Silicon: analytics where trust is not a manual review step, but a property of the workflow. A world where the business can move quickly without turning every answer into a debate. A world where data teams create leverage, not just tickets. A world where AI helps because it understands the context that makes data meaningful. The lesson: trust is infrastructure Trust is not a dashboard label. It is not a quarterly governance initiative. It is not a vague promise that the numbers are probably fine. Trust is infrastructure: tests, lineage, definitions, freshness, ownership, permissions, review, and context working together. When that infrastructure is missing, every answer is a negotiation. When it exists, teams can move faster because they are building on shared ground. That is what we mean when we say trust compounds. Every reliable model helps the next analysis. Every governed metric helps the next dashboard. Every visible dependency makes the next change safer. Every grounded AI answer makes the next question easier to ask. That is the kind of analytics system worth building. ## The takeaway The best analytics system is not the one that answers every question. It is the one that knows when an answer is trustworthy—and when it is not. FAQ ## Frequently asked questions ### What does data trust mean in practice? + It means definitions are consistent, lineage is traceable, freshness is visible, permissions are respected, and changes can be explained before they surprise users. ### How is data trust measured? + Useful signals include incident frequency, stale-data duration, duplicated metrics, unexplained variance, time to resolution, and the percentage of critical assets with owners and tests. ### Why is explainability important for AI analytics? + AI confidence is not evidence. Users need definitions, filters, sources, assumptions, freshness, and lineage to evaluate an answer. ### Can trust be added after dashboards are built? + It can be improved, but retrofitting ownership, definitions, tests, and lineage is harder than making them part of the analytics workflow from the beginning. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Self-serve analytics without the chaos | Silicon URL: https://meetsilicon.com/blog/self-serve-analytics-without-the-chaos/ Product Self-serve analytics without the chaos Self-serve analytics often begins with a dashboard licence and ends with twelve definitions of revenue. The intention is good. Product managers should not wait days for a cohort cut. Sales leaders should not file a ticket every time they need pipeline coverage. Finance should not need an analyst to refresh the same weekly report. But removing the queue does not remove the need for agreement. When teams get access without shared definitions, ownership, or visible freshness, self-serve moves the bottleneck. The data team stops writing every query and starts debugging everybody else’s. Good self-serve is not unrestricted access to tables. It is a product experience that makes the trusted path easier than the improvised one. ## Why self-serve gets chaotic Most self-serve programs fail for predictable reasons. They confuse tool access with workflow design. They assume people will find the right dataset. They rely on dashboard titles to explain business logic. They let metric definitions live in too many places. They treat AI as a shortcut around governance instead of a new surface that needs governance even more. The result is familiar: A PM uses a product events table without excluding internal users Sales and finance use different pipeline definitions An executive dashboard shows stale data but no one notices before the meeting A metric gets renamed in dbt and three downstream reports break An analyst becomes the human API for every “quick question” A team trusts the fastest answer, not the most correct one Again, nobody is trying to create chaos. People are trying to move. The system just does not give them safe paths. ## The old model: request and wait ### Before self-serve, many companies ran on a simple analytics operating model: Someone had a question. They asked the data team. The data team wrote SQL, built a dashboard, sent a number, or opened a ticket. The business waited. This model is slow, but it has one advantage: the data team stays close to the logic. They know which tables are messy. They know which definitions are contested. They know why last quarter’s churn logic changed. They know not to use the deprecated dashboard that somehow still has 200 weekly views. Self-serve removes the waiting. Great. But if it also removes that context, the organization has only traded one problem for another. The new model has to preserve context while distributing access. That is the whole game. Governed self-serve is a product experience The best self-serve analytics systems feel simple because a lot of hard work has been hidden in the right places. A user asks a question. The system knows which metrics are certified. It understands permissions. It can explain freshness. It can show lineage. It can point to the owner. It can say when a number should not be trusted. It can guide the user toward the right dataset instead of exposing the whole warehouse and hoping for the best. This is not just governance as a policy document. It is governance as product design. Useful self-serve systems answer questions like: Which metric should I use? How fresh is this data? What does this field mean? Can I trust this dashboard? Who owns this model? What changed since last week? Is this answer based on approved business logic? If the interface cannot answer those questions, the data team will. Usually in Slack. Usually repeatedly. ## Start with workflows, not tools The fastest way to make self-serve messy is to start with the tool. “We bought BI, so everyone can self-serve.” “We connected the warehouse, so people can explore.” “We added AI, so now users can ask anything.” Maybe. But ask anything is not the same as answer well. Start with the recurring workflows instead: Weekly revenue review Product activation analysis Pipeline inspection Customer health monitoring Marketing campaign performance Support volume and SLA tracking For each workflow, define the trusted path: Which sources matter? Which models are certified? Which metrics are approved? Which slices are safe for exploration? Which users need access? Which freshness expectations apply? What should happen when the data is stale or failing tests? Self-serve works when users enter through these trusted paths. It gets chaotic when every question starts from raw tables and hope. Metrics need a home Metric drift is where self-serve trust usually dies. Everyone agrees that “active customer” matters. Then product defines it by usage, finance defines it by subscription status, customer success defines it by renewal risk, and leadership wants one number in the board deck. All of those definitions may be useful in context. The problem is when the context disappears. A governed metrics layer gives important numbers a home. Not because every metric must be controlled by committee, but because repeated business questions deserve reusable definitions. Good metric definitions include: Business meaning Calculation logic Grain Allowed dimensions Owner ## Freshness expectation Known exclusions Downstream usage When metrics have a home, self-serve becomes less about inventing logic and more about applying shared definitions to new questions. That is a much better job for business users. AI raises the stakes AI analytics makes self-serve feel more natural. A user can ask a question in plain English instead of navigating a BI interface or learning SQL. That is powerful. It is also dangerous if the agent is not grounded. An ungrounded AI assistant can produce a confident answer from the wrong table, the wrong metric, or stale data. It can make a beautiful chart that hides a bad assumption. It can turn analytics chaos into analytics chaos with better grammar. The answer is not to avoid AI. The answer is to connect AI to the trust layer. A useful analytics agent needs access to: Certified metrics Model and column descriptions Lineage ## Freshness and test status Permissions Dashboard context Business definitions Prior analysis and saved logic Then it can do something much more useful than guess. It can guide users through governed exploration. That is the real self-serve unlock. ## Trust signals should be visible Users should not need to know the internal data platform to understand whether an answer is safe. Trust signals belong in the experience. Show when data was last refreshed. Show whether tests are passing. Show whether a metric is certified. Show which model or dashboard produced the answer. Show the owner. Show warnings when a source is stale or a definition changed. This is not clutter if it is designed well. It is the difference between “here is a number” and “here is a number you can use.” Data teams sometimes worry that exposing caveats will reduce trust. Usually the opposite is true. Hidden uncertainty creates bigger trust problems later. Visible context teaches users how to reason about data correctly. The goal is not to make every business user paranoid. The goal is to make trust legible. ## What the data team should own Governed self-serve does not mean the data team owns every answer. It means the data team owns the shared layer that makes many answers safe. That includes: Core models Certified metrics Data quality checks ## Freshness expectations Lineage and impact analysis Permissions Documentation Usage patterns Review workflows for high-risk changes The business can own exploration, interpretation, and action. The data team owns the reliability infrastructure underneath it. This changes the role of the data team from request handler to system builder. That is the whole point. ## The operating model that works Self-serve analytics without chaos usually has three layers. First, the governed foundation. This is where data is modeled, tested, documented, and permissioned. dbt models, semantic definitions, quality checks, lineage, ownership. Not glamorous, but essential. Second, the guided experience. This is where users ask questions, explore dashboards, create reports, or work with AI agents. The interface should guide them toward trusted assets and warn them away from unsafe ones. Third, the feedback loop. This is where repeated questions become reusable assets. A Slack thread becomes a metric. A one-off analysis becomes a dashboard. A confusing field gets better documentation. A recurring data issue gets a test. That feedback loop is where self-serve compounds. Without it, the organization just produces more artifacts. With it, the system gets smarter every time people use it. ## Where to start Do not start by rolling out self-serve to the entire company with a celebratory Slack post and a link to the warehouse. Start smaller. Pick one high-value workflow where people already ask repeated questions: Revenue performance Product adoption Sales pipeline Customer retention Marketing spend Operational SLA tracking Build the trusted path for that workflow: Certified models Approved metrics Clear owners ## Freshness and quality checks Documentation for the weird parts Dashboards or AI entry points that use the right definitions Visible trust signals A process for turning repeated questions into reusable assets Then expand. Self-serve analytics is not a permissions project. It is not a dashboard migration. It is not an AI demo. It is an operating model for distributing data work without distributing data chaos. The lesson: freedom needs rails The best self-serve systems do not choose between speed and governance. They use governance to make speed safe. Business users get faster answers. Data teams get fewer repetitive tickets. Leaders get numbers they can trust. AI becomes more useful because it is grounded in the definitions the business already agreed to. That is the version worth building. Not everyone wandering through the warehouse with a flashlight. A shared, governed path from question to decision. ## The takeaway Self-serve scales when access and agreement grow together. Otherwise the organisation gets more answers and less confidence. FAQ ## Frequently asked questions ### What is governed self-serve analytics? + It gives business users independent access to analysis while shared metrics, permissions, semantic context, freshness, and ownership remain centrally governed. ### Why does self-serve create dashboard sprawl? + When useful analysis has no promotion or certification workflow, each user saves a new local version and calculations slowly diverge. ### Does governance make analytics slower? + Poor governance adds approvals. Good governance removes repeated decisions by providing approved datasets, reusable metrics, and visible ownership. ### How do you know self-serve is working? + Routine questions are answered independently, definitions remain consistent, trusted assets are easy to find, and data teams spend less time reconciling conflicting numbers. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Semantic layer vs metrics layer: what is the difference? URL: https://meetsilicon.com/blog/semantic-layer-vs-metrics-layer/ Guides Semantic layer vs metrics layer: what is the difference? Ask five analytics teams to define a semantic layer and you may get six architectures. Some use the term for a business-friendly model of entities and relationships. Others mean a governed catalogue of metrics. Vendors often stretch the phrase to include permissions, caching, query generation, and every feature between the warehouse and the dashboard. The confusion matters because semantic layers and metrics layers solve overlapping but different problems. One gives data business meaning. The other makes calculations consistent. Most modern analytics systems need both—even when the product calls them by a single name. ## The short answer A semantic layer helps tools understand what data means. A metrics layer helps teams agree on how business metrics are calculated. If someone asks, “What is enterprise revenue by region this quarter?” the semantic layer helps map terms like enterprise, revenue, region, and quarter to governed data. The metrics layer makes sure revenue is calculated the same way everywhere. ## Why the distinction matters Analytics breaks when every dashboard, spreadsheet, and AI assistant defines business logic differently. One team counts revenue after refunds. Another counts booked ARR. Another excludes expansion. Another uses a dashboard-specific SQL expression that nobody remembers. Soon, leaders are not debating strategy. They are debating definitions. Semantic and metrics layers reduce that chaos by making definitions explicit. ## What a semantic layer usually includes A semantic layer typically describes: - Business entities such as accounts, users, opportunities, tickets, and products. - Relationships between entities. - Dimensions such as region, segment, lifecycle stage, and plan. - Measures and aggregations. - Access rules and permissions. - Friendly names and descriptions. - Context that BI tools and AI systems can use to generate trustworthy answers. The semantic layer is the translation layer between raw data structures and business language. ## What a metrics layer usually includes A metrics layer typically defines: - Canonical metric formulas. - Aggregation logic. - Time grains and date handling. - Filters and eligibility rules. - Ownership and documentation. - Versioning or change history. - Tests that protect metric correctness. The metrics layer is the source of truth for the numbers people make decisions with. ## Where AI analytics changes the stakes AI analytics makes governed context more important, not less. If an AI assistant can generate SQL, summarize dashboards, and answer business questions, it needs reliable definitions. Otherwise, it may answer quickly while using the wrong field, outdated metric logic, or an unsafe join. The question is no longer only, “Can a human find the right dashboard?” It is also, “Can software understand the business meaning of the data well enough to answer correctly?” That requires semantic context and trusted metrics. ## Common failure modes Teams usually run into a few predictable issues when these layers are missing. Metric drift: the same business term gets calculated differently across dashboards. Dashboard drift: reports continue to use old logic after a model or definition changes. AI answer drift: natural-language systems generate answers from plausible but ungoverned assumptions. Ownership drift: nobody knows who is responsible for a definition when it breaks. These are not just documentation problems. They are reliability problems. ## How to choose where to start If your biggest pain is inconsistent numbers, start with a metrics layer. Pick the metrics that matter most to the business and define them clearly. If your biggest pain is that tools cannot understand your data model or business language, start with a semantic layer. Define the entities, relationships, dimensions, and context that analytics tools need. If you are building AI analytics, plan for both. AI needs business meaning and metric correctness at the same time. ## Where Silicon fits Silicon is built for teams that want governed AI analytics without losing reliability. It connects metric context, dashboards as code, PR impact checks, schema-drift protection, and natural-language answers. That means business users can ask questions in plain language while data teams keep definitions, dependencies, and change impact under control. ## The takeaway A semantic layer tells tools what data means. A metrics layer tells teams how trusted numbers are calculated. The best analytics systems do not treat these as separate islands. They connect business meaning, metric definitions, development workflows, and reliability checks so every dashboard and AI answer can be trusted. ## The takeaway The useful question is not which label your architecture uses. It is whether every analytical surface can reach the same governed meaning. FAQ ## Frequently asked questions ### What is a semantic layer? + A semantic layer defines business entities, relationships, dimensions, measures, permissions, and context so tools can query data in business terms. ### What is a metrics layer? + A metrics layer centralises approved calculations for measures such as revenue, activation, churn, retention, pipeline, and margin. ### Is a metrics layer part of a semantic layer? + Often yes. Architectures and vendor terminology vary, but governed metrics commonly sit within a broader semantic model. ### Why do AI analytics systems need these layers? + They constrain metric definitions, joins, dimensions, permissions, and time logic, reducing the need for a model to guess from raw tables. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Tableau alternatives for governed self-serve BI | Silicon URL: https://meetsilicon.com/blog/tableau-alternatives-for-governed-self-serve-bi/ Guides Tableau alternatives for governed self-serve BI Most teams looking for a Tableau alternative are not actually looking for another Tableau. They may say they want a faster dashboard tool or a lower licence bill. Underneath, the operating model has changed. Analytics now lives in version-controlled models, semantic layers, Slack, embedded products, automated reports, and AI interfaces—not only in visual workbooks. Tableau remains excellent at many forms of visual exploration. The question is whether visual exploration is still the centre of the problem your team needs to solve. A useful evaluation starts with the job, not the feature checklist. ## The short answer If your main need is visual analysis, Tableau may still be a fit. If your main need is governed self-serve BI with AI answers, metric context, dashboards as code, and reliability checks, you should evaluate tools built around the modern analytics workflow. A good Tableau alternative should help teams answer questions faster without creating metric sprawl, dashboard sprawl, or trust problems. ## Why teams look beyond Tableau Teams usually start looking for Tableau alternatives for a few reasons. They want faster self-serve answers. They want fewer dashboard requests. They want business users to ask questions without learning a complex BI interface. They want governance that does not slow everyone down. Or they want reliability checks around the dashboards and metrics they already use. The issue is rarely that Tableau cannot make charts. It is that the analytics workflow around the charts becomes hard to manage. ## Common evaluation criteria When comparing Tableau alternatives, look beyond visualization features. Governed metrics Can the tool use shared metric definitions so revenue, churn, pipeline, and active users mean the same thing everywhere? AI analytics Can business users ask natural-language questions and get answers grounded in approved context, not guessed from raw tables? ## Dashboard reliability Can the team understand whether dashboards are fresh, accurate, owned, and affected by upstream changes? Change-impact checks Can data teams see which dashboards, metrics, and AI answers a pull request might affect before merging it? Workflow fit Does the tool fit how modern data teams work with dbt, YAML, pull requests, semantic context, and production analytics workflows? ## Where Tableau is strongest Tableau is strong for visual exploration, flexible dashboarding, and mature enterprise BI deployments. Teams with established Tableau expertise and a need for complex visual analysis may still get a lot of value from it. ## Where teams often need more Modern analytics teams often need more than dashboards. They need a way to keep the whole analytics system trustworthy. That includes metric governance, semantic context, freshness awareness, schema-drift detection, PR impact checks, and AI answers that respect business definitions. These needs sit around the dashboard, not just inside it. ## Where Silicon fits Silicon is built for teams that want governed self-serve analytics with reliability built in. It connects AI answers, metric context, dashboards as code, PR impact checks, and schema-drift protection. That makes it a fit for teams that want faster answers without losing trust in the analytics system. ## How to choose Choose Tableau if your priority is advanced visualization and your team already has the governance and reliability workflow handled elsewhere. Choose a modern governed self-serve BI workflow if your priority is trusted answers, AI analytics, metric consistency, and dashboard reliability. ## The takeaway The best Tableau alternative is not simply the tool with the most chart types. It is the tool that matches the way your team wants analytics to work: governed, reliable, explainable, and fast enough for business users to use without turning the data team into a support queue. ## The takeaway Do not replace one dashboard interface with another and call it transformation. Choose the operating model you want analytics to have next. FAQ ## Frequently asked questions ### Why do companies look for Tableau alternatives? + Common reasons include dashboard sprawl, licensing, version-control needs, governed metrics, AI analytics, embedded workflows, and proactive reliability. ### What should a governed self-serve BI tool provide? + Reusable metrics, semantic context, permissions, certified datasets, freshness, ownership, lineage, and explainable answers. ### Is Tableau still a good choice? + It can be a strong choice when rich visual analysis and an established Tableau operating model are the primary requirements. ### How should alternatives be compared? + Compare the desired analytics workflow: metric governance, code review, AI grounding, change impact, embedding, collaboration, and operational delivery—not only chart types. ### Can a new BI tool solve metric inconsistency? + Not by itself. Consistency depends on governed definitions and workflows that survive across tools. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is dashboard reliability? | Silicon URL: https://meetsilicon.com/blog/what-is-dashboard-reliability/ Guides What is dashboard reliability? A dashboard is not reliable because it loads. A slot machine also loads. Reliability begins when the number survives the questions that follow: Is the data current? Is the metric defined consistently? Did an upstream change alter the result? Can somebody explain why the chart moved? Dashboards are decision systems disguised as pages of charts. A board, sales team, or operations manager changes behaviour because of what they see. That makes reliability a product requirement, not an engineering nice-to-have. The best dashboard is not merely attractive or fast. It is defensible. ## The short answer Dashboard reliability means business users can trust the numbers they see, and data teams can explain why those numbers are correct. A reliable dashboard answers four questions: - Is the data fresh enough for this decision? - Are the metrics defined consistently? - Did any upstream change affect this output? - Does someone own the dashboard when something breaks? If the answer to any of those is unclear, the dashboard may still be visually polished, but it is not reliable. ## Why dashboard reliability matters Most organizations do not suffer from a lack of dashboards. They suffer from a lack of trust. Different teams build overlapping reports. Metrics drift across tools. Source systems change. A field gets renamed. A join starts duplicating rows. An executive sees a number that does not match the number in another dashboard. Once that happens, every dashboard becomes negotiable. People stop asking, “What should we do?” and start asking, “Which number is right?” That is the cost of unreliable analytics. ## Common causes of unreliable dashboards Dashboard reliability usually breaks for predictable reasons. Metric drift The same business term is calculated differently in different places. Revenue, active users, churn, pipeline, and conversion rate are common examples. ## Freshness gaps The dashboard loads, but the data is stale. The user may not know whether the number is from five minutes ago, yesterday, or last week. ## Schema drift Upstream tables, columns, or types change without downstream impact checks. Dashboards may break loudly or, worse, keep running with incorrect logic. Silent query failures A chart renders, but a filter excludes valid rows, a join duplicates records, or a null spike changes the result. Unclear ownership Nobody knows who owns the dashboard, the underlying model, or the metric definition when something goes wrong. ## What reliable dashboards need Reliable dashboards need more than a BI tool. They need a workflow around the BI tool. ### Governed metric definitions Teams need shared definitions for the metrics that matter. If revenue means different things in different dashboards, the business will not trust the result. ### Freshness expectations A dashboard should make clear how fresh the data needs to be and whether it currently meets that expectation. ### Dependency mapping Teams should know which models, columns, metrics, dashboards, and AI answers depend on one another. ### Change-impact checks When a schema or model changes, the team should see which downstream dashboards are affected before the change is merged. ### Ownership and documentation Reliable dashboards need owners, context, and a clear path for resolving issues. ## Where AI analytics changes the problem AI makes dashboard reliability more important. When users can ask questions in natural language, the system needs to understand trusted definitions and reliable context. If the underlying dashboard or metric logic is wrong, AI will not fix the problem. It may simply produce the wrong answer faster. Reliable AI analytics requires the same foundation as reliable dashboards: governed metrics, semantic context, freshness checks, and impact awareness. ## Where Silicon fits Silicon is built for teams that want analytics speed and reliability together. It connects governed metric context, dashboards as code, PR impact checks, schema-drift protection, and AI answers. That helps teams move from reactive dashboard support to proactive analytics reliability. ## The takeaway Dashboard reliability is not about making dashboards prettier. It is about making them trustworthy. A reliable dashboard is fresh, governed, documented, impact-aware, and owned. When teams build that foundation, dashboards become decision systems people can actually trust. ## The takeaway A reliable dashboard can answer the most important analytical question of all: why should I believe this? FAQ ## Frequently asked questions ### What is dashboard reliability? + It is the confidence that a dashboard is accurate, fresh, understandable, resilient to change, and safe for its intended decisions. ### How is dashboard reliability different from uptime? + Uptime confirms availability. Reliability includes metric correctness, source freshness, semantic consistency, ownership, and downstream change management. ### What causes silent dashboard failures? + Wrong grain, duplicate joins, changed enum values, stale sources, local metric logic, and schema drift can alter numbers without producing visible errors. ### How should reliability be measured? + Track incidents, stale windows, broken dependencies, duplicated metrics, unexplained variance, ownership coverage, and time to detect and resolve issues. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is schema drift in analytics? | Silicon URL: https://meetsilicon.com/blog/what-is-schema-drift-in-analytics/ Guides What is schema drift in analytics? A column rename can be a one-line code change and a company-wide analytics incident. The source team sees a cleaner schema. Downstream, a join stops matching, a filter loses a category, and an AI system continues generating answers from documentation that describes yesterday’s table. This is schema drift: the structure of data changes while the systems built on top of it continue to assume the old contract. The dangerous version does not throw an error. It keeps running and quietly changes the meaning of the output. ## Why schema drift matters Most analytics systems assume that upstream data structures remain stable. A dashboard might depend on a column called account_id. A metric might join revenue to customer records using a specific key. A report might group pipeline by a status field. An AI analyst might generate SQL based on semantic context that describes tables and columns as they existed yesterday. When the schema changes, the system may fail loudly. A dashboard errors. A report does not run. A query breaks. But the more dangerous failure mode is quiet drift. The dashboard still loads, but the number is wrong. A filter silently excludes rows. A join duplicates revenue. A metric definition still executes but no longer means what the business thinks it means. This is why schema drift creates more than broken charts. It creates doubt. ## Common examples of schema drift Schema drift usually shows up in a few predictable ways: - A column is renamed without updating downstream dashboards. - A field changes from string to number, timestamp to date, or boolean to string. - A table is replaced by a new model but old reports still reference the original. - A status, category, or enum value changes. - A transformation starts returning nulls or unexpected values. - A join key changes cardinality and duplicates downstream rows. - A source system adds or removes fields that analytics workflows depend on. These changes are normal. Products evolve. Source systems change. Data models mature. The issue is not that schemas change. The issue is that most analytics stacks do not show the impact of those changes before they reach business users. ## Why traditional BI does not solve it Traditional BI tools are good at visualization and exploration. They are less good at protecting the reliability of the system behind the chart. A BI tool can tell you when a dashboard query fails. It usually cannot tell you, before a pull request is merged, which dashboards, reports, metrics, and AI answers will be affected by a schema change. That gap matters. By the time a stakeholder notices a broken dashboard, trust has already been damaged. The data team is now reacting instead of preventing. ## How to prevent schema drift from breaking analytics The goal is not to freeze schemas forever. The goal is to make change safe. A reliable analytics workflow should include four things. ### Map dependencies between data models and business outputs Teams need to know which dashboards, reports, and metrics depend on each model, column, and semantic definition. Without dependency mapping, every schema change becomes guesswork. ### Check impact before changes merge Schema changes should be evaluated during the development workflow, not after production dashboards break. Pull request checks should show which downstream assets are affected and whether the change is safe. ### Keep semantic context close to the data AI analytics systems need governed context. If an AI assistant is answering business questions, it should know which fields are approved, which metrics are canonical, and which definitions have changed. ### Alert teams when drift affects trusted outputs Not every schema change matters. The important question is whether the change affects something the business relies on. Alerts should focus on impact, not noise. ## Where Silicon fits Silicon is built for teams that want AI analytics and reliability in the same workflow. It connects governed metric context, dashboards as code, PR impact checks, schema-drift protection, and natural-language answers. That means teams can move quickly without asking business users to blindly trust every chart or AI-generated answer. Instead of finding out about schema drift from a stakeholder, teams can catch it before it breaks downstream work. ## The takeaway Schema drift is inevitable. Broken trust is not. As analytics systems become more automated and AI-assisted, teams need more than dashboards. They need reliability checks around the answers those dashboards produce. Schema drift protection gives data teams a way to change models, ship faster, and keep business metrics trustworthy. ## The takeaway A healthy data platform does not freeze the schema. It makes the cost and consequence of changing it visible. FAQ ## Frequently asked questions ### What is schema drift? + Schema drift is an uncoordinated change to fields, types, structures, relationships, or accepted values that affects downstream data consumers. ### What causes schema drift? + Application releases, vendor APIs, event-tracking changes, model refactors, migrations, and evolving enum or null behaviour are common causes. ### How is schema drift detected? + Model contracts, source tests, metadata comparison, lineage, pull-request impact analysis, and monitoring of null rates or distributions expose different forms of drift. ### Should teams prevent all schema changes? + No. The goal is safe evolution: understand impact, notify owners, provide migration paths, and monitor the rollout. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Why AI gets your company's numbers wrong | Silicon URL: https://meetsilicon.com/blog/why-ai-gets-your-company-numbers-wrong/ Guides Why AI gets your company's numbers wrong Ask an AI tool how many active customers you had last quarter and you will get an answer, usually with a chart and a confident sentence. What it rarely tells you is which definition of "active customer"it used, and whether that matches the one finance put in the board deck. That gap is the main reason AI answers about company data go wrong. The model usually writes valid SQL but misreads what your columns mean. ## What the benchmark found In April 2026, dbt Labs updated a benchmark it first ran in 2023 (dbt Labs). The team asked 11 business questions of an insurance dataset, 20 times each, with current AI models, and compared two approaches: - The AI writes the query itself after reading the table structure (often called text-to-SQL). - The AI uses metrics someone has already defined, such as "active customer"or "net revenue", and software writes the query. That set of definitions is called a semantic layer. The test data had been cleaned up and documented first. On it, the AI writing its own queries was right 90.0% of the time with the better of the two leading models. Answering from defined metrics, the same model was right 98.2% of the time, and the other reached 100%. The failures differed too. When the AI wrote its own query and got it wrong, it returned a plausible wrong number, which can reach a board deck unchallenged. When it worked from defined metrics and could not answer, it returned an error, which prompts someone to ask why. Two caveats. dbt Labs sells a semantic layer, and the benchmark covers 11 questions on one dataset. The benchmark is open source, though, so anyone can rerun it. ## Why the model guesses Most company data has no documentation explaining what its columns mean. A status column might hold five values, two of which mean a customer has churned. Revenue appears in billing, in the CRM and in accounting software, and each system records a different event (Signld). Someone who has worked at the company for a year knows which version to use. A model seeing the tables for the first time does not, so it picks the most plausible one. Newer models help. In the same benchmark, the AI writing its own queries answered 32.7% of all questions correctly in 2023 and 64.5% in 2026. Models still can't supply definitions nobody wrote down. Those live with the people who run each team. ## What fixes it Write the definitions down once, in a place both people and AI tools can read. In practice, that means: - Each key metric is defined in one place, and the team that owns it agrees how it is calculated. - The AI tool answers from those definitions instead of raw tables. - Every answer shows the definition and data behind it, so anyone can check it. This is the work most startups postpone, because it rarely feels urgent until two teams bring different numbers to the same meeting. The scope can be small. In dbt's benchmark, adding three cleaned-up tables took the share of questions answerable from defined metrics from none to all of them. ## Where to start If you are about to roll out an AI tool on company data, pick the five numbers your leadership reviews every week. For each one, write down how it is calculated, which system it comes from and who decides when it changes. That list is the start of a semantic layer, and it gives any AI tool a definition to answer from. The next step is turning that list into something software can read. A data engineer does this by connecting your systems and encoding each definition. Without one on the team, a data partner can do it for you. Silicon builds and maintains these definitions for startups as a data partner, and our AI analytics platform answers from them. If you have your list of five, book 30 minutes with the founder and we will go through how each metric would be defined from your current systems. FAQ ## Frequently asked questions ### Is a semantic layer only for companies with a data team? + No. It can start as a short written list of definitions and grow from there. ### Does a better AI model remove the need for metric definitions? + No. A model can only use a definition if someone has written it down. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides dbt is where data work becomes software work ### dbt is where data work becomes software work Guides How to monitor dashboard impact before merging a PR ### How to monitor dashboard impact before merging a PR ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Why self-serve analytics fails at scale | Silicon URL: https://meetsilicon.com/blog/why-self-serve-analytics-fails-at-scale/ Guides Why self-serve analytics fails at scale Self-serve analytics works beautifully until everybody starts using it. A small team can coordinate definitions through conversation. The analyst knows which dashboard is trusted, which filter is unusual, and why the revenue model excludes a particular customer type. Scale replaces those conversations with assumptions. More people create more assets. More tools reproduce more logic. Access grows faster than agreement, and the organisation eventually has data everywhere but confidence nowhere. The failure is not that self-serve was a bad idea. It is that access was treated as the finished product. ## The short answer Self-serve analytics fails at scale when teams optimize for access but do not build the systems that keep answers consistent, reliable, and explainable. The failure is rarely one dashboard. It is the accumulation of small inconsistencies across metrics, models, permissions, ownership, and data freshness. ## Why early self-serve works In the beginning, self-serve analytics feels fast because the environment is small. There are fewer models, fewer stakeholders, fewer definitions, and fewer dashboards. People know who to ask when something looks wrong. A small team can keep context in their heads. At that stage, a flexible BI tool may be enough. ## Why scale changes the problem As the company grows, the analytics surface area expands. Sales wants pipeline reporting. Product wants activation and retention. Finance wants revenue and margin. Customer success wants health scores. Executives want company-level dashboards. Each team starts building around its own immediate needs. That creates speed, but it also creates drift. ## Common failure modes Metric sprawl The same metric gets rebuilt in multiple dashboards with slightly different logic. Everyone thinks they are looking at revenue, churn, active users, or conversion, but the definitions do not match. Dashboard sprawl Old dashboards remain in circulation. New dashboards duplicate old ones. Nobody knows which one is canonical. ## Freshness confusion A dashboard loads, but users do not know whether the data is fresh enough for the decision they are making. ## Schema drift Source systems change, fields are renamed, and upstream models evolve. Without impact checks, downstream dashboards can break silently. Unclear ownership When a number looks wrong, nobody knows who owns the metric, model, dashboard, or decision context. ## The hidden cost The hidden cost of failed self-serve analytics is not just bad reports. It is organizational drag. Meetings turn into debates about whose number is right. Data teams become support queues. Business users lose confidence. Leaders make slower decisions because every answer needs manual validation. ## What scaled self-serve needs Scaled self-serve analytics needs guardrails, not bottlenecks. Teams need shared metric definitions, clear ownership, trusted semantic context, freshness checks, dependency mapping, and change-impact workflows. The goal is not to stop people from exploring data. The goal is to make sure exploration starts from trusted context. ## Where AI changes the stakes AI makes self-serve analytics easier to access. It also raises the cost of weak governance. If users can ask questions in natural language, the system needs to know which definitions are trusted, which data is fresh, and which downstream assets may be affected by changes. Otherwise, AI simply accelerates inconsistent analytics. ## Where Silicon fits Silicon helps teams keep self-serve analytics fast without losing control. It connects governed metric context, dashboards as code, PR impact checks, schema-drift protection, and AI answers. That gives business users faster answers while giving data teams the reliability workflow needed to trust those answers. ## The takeaway Self-serve analytics fails when access grows faster than trust. To scale it, teams need a reliability layer around metrics, dashboards, data changes, and AI answers. Without that foundation, self-serve becomes another source of analytics chaos. ## The takeaway Self-serve scales when the organisation distributes the ability to ask questions without distributing the ability to redefine the business. FAQ ## Frequently asked questions ### Why does self-serve analytics fail at scale? + Definitions, ownership, permissions, freshness expectations, and asset discovery often fail to scale alongside access. ### What is metric sprawl? + Metric sprawl occurs when similar business calculations are independently recreated across dashboards, tools, teams, or AI workflows. ### How can governance support self-serve? + Provide certified datasets, reusable metrics, semantic context, visible ownership, freshness, lineage, and a workflow for promoting useful exploration. ### What does successful self-serve look like? + Teams answer routine questions independently while shared definitions remain consistent and the data team spends less time reconciling conflicting numbers. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Why your analytics pipeline breaks at scale | Silicon URL: https://meetsilicon.com/blog/why-your-analytics-pipeline-breaks-at-scale/ Engineering Why your analytics pipeline breaks at scale The pipeline was green when the revenue dashboard lost eighteen percent overnight. No job had failed. No alert had fired. The warehouse was online, the transformations completed, and the dashboard refreshed on schedule. The number was simply wrong—a source change had quietly altered a join that dozens of downstream assets trusted. This is how analytics systems usually break at scale. Not as a dramatic outage, but as a series of reasonable changes whose combined impact nobody can see until a business decision starts to look suspicious. The uncomfortable lesson is that pipeline uptime and analytics reliability are different products. One tells you the machinery moved. The other tells you whether the output deserves to be believed. ## The old pipeline contract was too small For years, the implicit contract for data pipelines was simple: Did the job run? If yes, ship it. That was useful, but incomplete. A pipeline can run and still produce nonsense. A sync can finish and still drop a column. A model can build and still duplicate customers. A dashboard can load and still point a team at the wrong decision. The modern contract has to be bigger: Did the data arrive on time? Did the shape change? Did volume move outside an expected range? Did key distributions drift? Did business rules still hold? Which dashboards, metrics, apps, and AI answers depend on this model? Who needs to know before this becomes a fire drill? That is the difference between pipeline uptime and analytics reliability. Uptime tells you the machinery moved. Reliability tells you whether the output can be trusted. ### Failure mode 1: schema drift Schema drift is the classic quiet failure. An engineering team renames account_id to customer_id. A vendor adds a nested object. An ingestion tool changes a type from integer to string. A column that used to exist disappears from a daily export. Sometimes the pipeline breaks immediately. Honestly, that is the nice version. The worse version is when the pipeline keeps running. The transformation adapts badly. A join stops matching. A filter silently excludes new records. A downstream dashboard still renders, but the metric is now missing a slice of the business. Schema drift is not just a technical problem. It is a coordination problem. The people changing source systems often do not know which analytics assets depend on them. The people maintaining dashboards often do not know source changes are coming. The business only finds out when a number looks suspicious. The fix is not to freeze every schema. That would be absurd. Products change. Source systems evolve. Data models need to move. The fix is to make change visible before it causes damage. Useful controls look like: Source freshness checks Column-level contract checks Type validation on critical fields Automated downstream impact analysis Pull request comments that show affected dashboards and models Clear owners for high-value datasets When schema changes are inspectable, they stop being surprises. ### Failure mode 2: freshness lies Freshness sounds simple: is the data up to date? It is not simple. A table can be fresh at 9:00 and useless at 9:05 if the upstream system is late. A dashboard can say it refreshed today while one of its most important sources is still yesterday’s data. A pipeline can meet its SLA but miss the business moment that actually matters. Freshness is not a timestamp. It is a promise. The promise depends on the workflow. Executive reporting may tolerate a daily refresh. Fraud monitoring probably cannot. A customer health model might need hourly updates. A board dashboard needs consistency more than speed. The mistake is treating all freshness problems the same. Good teams define freshness where it matters: At the source, to know whether raw data arrived At the model, to know whether transformations are current At the metric, to know whether business numbers are safe to use At the dashboard or app, to know whether the user-facing surface is trustworthy Then they route alerts based on impact, not vibes. A late low-usage sandbox table should not wake anyone up. A stale revenue model before the Monday meeting probably should. ### Failure mode 3: volume drift Volume drift is where data starts whispering before it screams. A table usually receives 2 million events a day. Today it received 600,000. Or 9 million. Maybe that is a product launch. Maybe it is a tracking bug. Maybe it is a duplicate ingestion job. Maybe someone forgot to exclude internal traffic. The pipeline ran. The data is fresh. The number is wrong. This is why row counts, null rates, uniqueness, and distribution checks matter. Not because data teams need more dashboards about dashboards. Because the shape of data is often the first clue that business meaning has changed. Volume checks are especially useful when they are tied to context: Which source changed? Which model amplified the issue? Which metrics are affected? Which dashboards will show a different answer? Was there a deployment, migration, vendor change, or backfill nearby? A count by itself is a signal. A count connected to lineage is a diagnosis. ### Failure mode 4: duplicated business logic The most dangerous pipeline failures often do not look like failures at all. They look like two correct queries. One team calculates active customers using last login. Another uses paid subscription status. A third excludes enterprise accounts because that is what finance needed last quarter. Every version has a reason. Every version is defensible. None of them agree. This is how data trust breaks without a single red error. Duplicated business logic turns analytics into archaeology. Every request starts with a hunt for the “right” definition. Every dashboard becomes its own small data product. Every AI answer becomes fragile because the agent has no stable definition to reason from. At scale, the answer is not more meetings about metrics. It is fewer places for metric logic to hide. Move critical definitions into governed models and metrics. Put tests around them. Document the assumptions. Review changes. Show lineage. Boring? Absolutely. Effective? Also yes. ### Failure mode 5: incidents without blast radius ## When a pipeline breaks, the first question is usually “what happened?” The better first question is often “who is affected?” A failed staging model that feeds nothing important is different from a failed revenue model that powers the board deck, sales forecast, and customer-facing usage report. Treating those incidents the same is how teams either over-alert or underreact. Blast radius is the missing context in a lot of analytics operations. Without lineage, incidents become Slack archaeology. Someone asks who owns the dashboard. Someone asks which model feeds it. Someone pastes a query. Someone remembers an old migration. The team eventually finds the issue, but only after trust has already taken a hit. With lineage, the investigation starts with a map: Upstream sources Intermediate models Downstream dashboards Metrics and semantic definitions Owners Recent changes Open tests or warnings That does not make incidents fun. It makes them finite. Make correctness observable You cannot fix what you cannot see. This is the first principle of reliable analytics systems. Freshness, volume, schema, distribution, tests, and lineage should be first-class signals, not afterthoughts attached to a BI dashboard nobody checks. A healthy analytics pipeline should be able to answer: What changed? When did it change? What does it affect? Who owns the affected assets? Has this happened before? Is the data safe to use right now? That last question is the one that matters. Data observability is not about collecting more telemetry because telemetry is fashionable. It is about giving teams enough context to decide whether a number can be trusted. Shift reliability left The best time to catch a broken dashboard is before it is broken. That means reliability needs to show up where work already happens: pull requests, model reviews, dbt runs, deployment workflows, and release checks. If a PR changes a model, the reviewer should know which dashboards and metrics are downstream. If a source schema changes, the data team should know which transformations depend on the column. If a test fails, the alert should include business impact, not just a stack trace. This is the same lesson software engineering learned years ago. Do not wait for production to teach you basic things about the change. For analytics, shifting left means: Checking model contracts before merge Running targeted tests on affected dependencies Surfacing dashboard impact in code review Blocking risky changes when high-value assets are affected Keeping docs close to the models they describe ## Making ownership visible before something breaks It is not about slowing teams down. It is about making speed less fragile. Design for graceful failure Pipelines will break. Source systems will surprise you. Humans will ship imperfect changes. Vendors will do vendor things. The goal is not perfection. The goal is graceful failure. A graceful analytics failure has a few properties: The issue is detected early The affected assets are known The owner is clear Users can see that data is stale or unsafe The fix path is obvious The same issue is less likely next time That last part matters. Every incident should leave behind a better system: a new test, a clearer contract, a documented assumption, a more precise alert, a smaller blast radius. Otherwise the team is just renting reliability by the hour. ## What this changes for the data team When analytics reliability is designed into the pipeline, the data team spends less time doing emergency trust repair. Less “why is this dashboard wrong?” Less “which number should I use?” Less “did anyone know this column changed?” More reusable models. More governed metrics. More thoughtful reviews. More time spent making the next analysis easier instead of defending the last one. This is where pipeline work becomes leverage. Not because every test is interesting. Most tests are not. Not because lineage diagrams are inherently beautiful. Though sometimes they are. Because the boring infrastructure is what lets the organization move faster without turning every decision into a trust exercise. The lesson: reliability compounds A single freshness check is useful. A tested model is better. A tested model with lineage, ownership, documentation, and downstream impact is much better. The value compounds because every piece of context makes the next decision safer. Tests catch issues earlier. Lineage makes incidents smaller. Ownership reduces ambiguity. Documentation prevents bad guesses. Impact analysis keeps changes from surprising the business. That is how analytics pipelines scale. Not by pretending they will never break. By making breakage visible, bounded, and fixable. ## Where to start Do not start by trying to observe every table in the warehouse. Start with the assets people would notice if they were wrong. Pick one critical workflow: Revenue reporting Customer health Sales pipeline Product activation Executive dashboards ## AI analytics answers Then build the reliability loop around it: Define the important sources and models Add freshness and volume checks Test the business rules that matter Document the assumptions people usually ask about Map downstream dashboards and metrics Assign owners Surface impact before changes merge That is enough to change the operating model. The goal is not a perfect pipeline. The goal is a trusted path from raw data to decision. Then widen the path. ## The takeaway The goal is not a pipeline that never changes. It is a system where change becomes visible before it changes what the business believes. FAQ ## Frequently asked questions ### Why do analytics pipelines break as companies scale? + Dependencies, owners, metric definitions, and downstream consumers grow faster than informal knowledge can keep up. Small source or model changes therefore create larger and less visible consequences. ### What is the difference between pipeline uptime and data reliability? + Uptime confirms that jobs ran. Reliability evaluates whether the resulting data is fresh, complete, correctly shaped, semantically valid, and safe for downstream decisions. ### Which controls prevent silent pipeline failures? + Freshness expectations, schema contracts, metric tests, distribution checks, lineage, impact analysis, and clearly routed ownership catch different classes of silent failure. ### Where should a team start? + Start with the few pipelines behind executive, financial, customer-facing, or automated decisions. Define their promises and monitor those promises end to end. Keep reading ## More from the blog Guides Hiring a data engineer vs working with a data partner ### Hiring a data engineer vs working with a data partner Guides Why AI gets your company's numbers wrong ### Why AI gets your company's numbers wrong Guides dbt is where data work becomes software work ### dbt is where data work becomes software work ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Compare Silicon with Other Analytics Tools | Silicon URL: https://meetsilicon.com/compare/ Compare # How Silicon compares Honest comparisons with the tools and options teams most often weigh up alongside Silicon, including when the other choice fits better. ## Silicon vs Amplitude and Mixpanel Amplitude and Mixpanel are product analytics tools. They collect events through their own SDKs and are built for exploring user behaviour, with session replay and experiments alongside. Silicon works on the data already in your warehouse, so activation and retention are defined once, next to MRR and churn, and every team counts the same customers. Read the comparison → ## Silicon vs Tableau Tableau is a mature visual analytics tool with deep charting. Silicon is built for teams on a modern warehouse and dbt who want metrics defined in code, AI answers on those metrics, and dashboards that don’t break when models change. Read the comparison → ## Silicon vs Monte Carlo Monte Carlo is a dedicated data observability platform that monitors tables and pipelines for anomalies. Silicon is an analytics platform with reliability built in: it knows every dashboard, metric and report, so it can tell you exactly which ones a change would break, before you merge. Read the comparison → ## Silicon vs ThoughtSpot ThoughtSpot pioneered search-driven analytics and has a large enterprise footprint. Silicon is built around dbt: your metrics are defined in code, AI answers are built from them, and changes to models are checked against every dashboard before merge. Read the comparison → ## Silicon vs Omni Omni is a modern BI tool with its own modelling layer and strong spreadsheet-style workflows. Silicon puts reliability at the centre: metrics defined in dbt, dashboards as code, and every model change checked against what depends on it. Read the comparison → ## Silicon vs Metabase Metabase is an open-source BI tool with a free self-hosted edition, paid cloud plans and its own semantic layer. Silicon reads your metrics from dbt, answers questions in Slack from those definitions, and checks each change to your dbt models against the dashboards that depend on it before it merges. Read the comparison → ## Silicon vs hiring a data analytics consultant A data analytics consultant is usually hired for a project. They build a set of reports or dashboards, document the work and hand it over to your team. Silicon partners with high-growth startups to build and maintain their data foundation, and our AI analytics platform runs on that foundation, so the work carries on after the first dashboards ship. Read the comparison → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Talk to Sujay, the Founder | Silicon URL: https://meetsilicon.com/contact/ Talk to Sujay # Talk to the people building Silicon Book a 30-minute call with Sujay SN, Silicon’s founder. You’ll speak with the person building the product, not a sales rep. Sujay SN Founder, Silicon - 01 Where your data is today Your sources, warehouse, dbt setup and BI tools if you have them, and where things break. - 02 A live demo Silicon on sample data: an AI answer, a dashboard and a schema check on a pull request. - 03 Honest fit Whether you need the platform, the data partnership or both, and what it would take to get going. Prefer email? Write to help@meetsilicon.com. --- # Analytics Glossary: Semantic Layer to Schema Drift | Silicon URL: https://meetsilicon.com/glossary/ Glossary # Analytics glossary Plain definitions of the data terms you’ll hear when you build a data foundation. Dashboard reliability Dashboard reliability is the confidence that a dashboard loads, shows current data and calculates its numbers correctly every time someone opens it. Data freshness Data freshness is how recently the data behind a table, metric or dashboard was updated, compared with how recent it needs to be. Data lineage Data lineage is a map of how data flows from its sources, through transformations, to the metrics, dashboards and reports that use it. Data observability Data observability is the practice of monitoring data pipelines and tables for freshness, volume, schema and quality issues so problems are found before users notice. dbt dbt (data build tool) is an open-source framework for transforming data in a warehouse using version-controlled SQL, with tests, documentation and lineage built in. Embedded analytics Embedded analytics means putting dashboards, charts or data exploration directly inside another application, usually so a company’s customers can see their own data. Headless BI Headless BI is an approach where metric definitions live in a standalone semantic layer that any tool can query, rather than inside one BI tool. Metrics layer A metrics layer is the part of a semantic layer that defines business metrics, like revenue or churn rate, once in code so they can be reused consistently. Row-level security Row-level security restricts which rows of data a user can see, based on attributes such as their region, team or customer account. Schema drift Schema drift is any unplanned change to the structure of data, such as a renamed column, a dropped field or a changed data type, that can break the queries and dashboards built on it. Semantic layer A semantic layer is a shared set of business definitions, such as metrics, dimensions and joins, that sits between a data warehouse and the tools that query it. Text-to-SQL Text-to-SQL is the use of AI to turn a plain-English question into a SQL query that runs against a database. ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is dashboard reliability? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/dashboard-reliability/ Reliability fails in three ways: the dashboard errors, the data is stale, or the number is wrong without anyone noticing. The last is the most damaging because decisions get made on it. ## The three ways dashboards fail Broken dashboards error or show empty charts, usually after an upstream change. Stale dashboards load but show old data because a job failed or ran late. Wrong dashboards load fresh data but calculate it incorrectly, for example after a filter was changed in one chart and not in others. The first kind gets noticed quickly. The second and third can go unnoticed for weeks, and they are the ones that end up in board decks. ## How teams improve it Define metrics once so charts cannot disagree, check upstream changes against the content that depends on them before they merge, show when each chart was last refreshed, and review old dashboards so unused ones are archived instead of quietly going stale. ## How Silicon handles it Silicon checks pull requests against dashboards, builds charts from defined metrics, and runs Autopilot on a schedule to fix broken charts and flag stale or slow content. FAQ ## Dashboard reliability questions ### How do you measure dashboard reliability? + Track how often dashboards error, how often their data is older than expected, and how many incidents came from wrong numbers rather than errors. ### Who owns dashboard reliability? + Usually the data or analytics engineering team, with each metric owned by the team that uses it. ## Related - Schema drift → - Data freshness → - What is dashboard reliability? → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is data freshness? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/data-freshness/ A daily finance report is fresh if it reflects yesterday. A live operations dashboard may need data from the last few minutes. Freshness is always relative to an expectation. ## How freshness is measured Most teams compare the latest timestamp in a table, such as the newest loaded_at value, with the time now, and set a threshold per table. dbt source freshness checks do exactly this and warn or fail when a source is older than its threshold. ## Common causes of stale data A sync job fails or is paused, a scheduled dbt run errors partway through, an upstream vendor delays an export, or a dashboard caches results longer than intended. Each of these leaves the dashboard loading normally, which is why stale data is easy to miss. ## How Silicon handles it Scheduled reports in Silicon run fresh queries at send time instead of sending cached results, and Autopilot flags content that has gone stale. FAQ ## Data freshness questions ### What is a good data freshness SLA? + It depends on the decision the data supports. Daily is common for finance and leadership reporting. Operations teams often need data within the hour. ### Is data freshness the same as data latency? + They are related. Latency is how long data takes to move from source to warehouse. Freshness is how old the data is when someone looks at it. ## Related - Dashboard reliability → - Data observability → - A practical guide to data freshness → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is data lineage? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/data-lineage/ Lineage answers two questions: where did this number come from, and what will break if I change this? Column-level lineage tracks individual fields, which is what you need to predict the impact of a rename. ## Table-level and column-level lineage Table-level lineage shows that a dashboard depends on fct_orders, which depends on stg_orders. Column-level lineage shows that a chart uses fct_orders.customer_id, which comes from orders.user_id in the source. Impact analysis for a rename needs column level, because most changes touch one field rather than a whole table. ## Where lineage comes from dbt builds lineage between models from ref() calls. Lineage beyond dbt, into dashboards, saved queries and AI answers, has to come from the tools that run those queries, which is why many teams have lineage for the warehouse but not for the reports people read. ## How Silicon handles it Silicon combines dbt lineage with the charts, dashboards and metrics built on it, so a pull request comment can list the exact content a column change affects. FAQ ## Data lineage questions ### What is data lineage used for? + Mainly two things. Tracing a wrong number back to its source, and predicting what will break before a change is made. ### Does dbt provide data lineage? + Yes, between dbt models and sources. It does not include the dashboards and queries in BI tools unless those tools add it. ## Related - Schema drift → - dbt → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is data observability? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/data-observability/ Observability tools usually watch tables and pipelines. Analytics reliability extends this to the metrics and dashboards people actually use. ## The usual signals Observability tools typically watch five things: freshness (is the data recent), volume (did the expected number of rows arrive), schema (did the structure change), distribution (are values in their usual range) and lineage (what depends on what). ## Where it stops Most observability tools watch tables and pipelines and alert data engineers. They usually do not know which dashboards, metrics and saved queries sit on top of a table, so an alert says a table changed without saying which reports are affected or who should be told. ## How Silicon handles it Silicon focuses on the analytics side, meaning the metrics, dashboards and answers people use. It checks changes before they merge and alerts in Slack or Microsoft Teams with the content each change affects. Teams that need deep pipeline monitoring often use it alongside a dedicated observability tool. FAQ ## Data observability questions ### Is data observability the same as data quality? + Data quality is the goal. Observability is the monitoring that tells you when quality slips. ### Do small data teams need data observability? + Usually the basics, meaning freshness checks and dbt tests on key models. Full observability platforms pay off once there are many pipelines and owners. ## Related - Data freshness → - Schema drift → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is dbt? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/dbt/ dbt projects define models as SQL files and describe them in YAML. Many teams also define metrics in that YAML, which tools like Silicon read directly. ## How a dbt project is organized Models are SQL select statements, one per file, that build tables or views in the warehouse. Sources describe the raw tables that data arrives in. YAML files hold descriptions, tests and configuration, and ref() calls between models let dbt work out the order to build them in and draw lineage. ## dbt Core and dbt Cloud dbt Core is the open-source command-line tool. dbt Cloud is the hosted product from dbt Labs that adds scheduling, an IDE and the dbt Semantic Layer. Many teams run Core on their own scheduler and use other tools for metrics and BI. ## How Silicon uses dbt Silicon reads a dbt project directly, from Core or Cloud. Model and column descriptions give the AI analyst business context, metrics defined in meta become governed metrics, and model dependencies power schema protection. FAQ ## dbt questions ### Is dbt an ETL tool? + It handles the T, transformation, inside the warehouse. Loading data from sources is done by separate tools. ### Do I need dbt to use a semantic layer? + No, but most semantic layers today are built on dbt projects because the models and descriptions are already there. ## Related - Semantic layer → - Data lineage → - dbt integration → - dbt is where data work becomes software work → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is embedded analytics? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/embedded-analytics/ The hard parts are security and consistency: each customer must see only their data, and the numbers must match what the company sees internally. ## Ways to embed The simplest option is an iframe that shows a dashboard inside your app, with a signed token that tells the analytics tool who the viewer is. An SDK, such as a React component library, gives more control over layout and styling. Some teams query a semantic layer API and draw their own charts. ## What to check before choosing a tool How customers are isolated from each other, whether the embedded numbers come from the same definitions as internal reports, how pricing scales with the number of customer viewers, and how much white-labelling is possible so the analytics look like part of your product. ## How Silicon handles it Silicon embeds dashboards and AI answers with an iframe or the React SDK. Each embed carries a signed token with the customer’s identity, and row-level security applies to every query. FAQ ## Embedded analytics questions ### Is embedded analytics secure? + It can be, if every query is filtered by the viewer’s identity on the server side. Avoid setups that filter data only in the browser. ### Should we build embedded analytics ourselves? + Simple charts are quick to build. Filtering, exports, permissions and keeping numbers consistent with internal reporting are where in-house builds tend to grow. ## Related - Row-level security → - Headless BI → - Embedded analytics → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is headless BI? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/headless-bi/ It separates “what does revenue mean?” from “where is revenue displayed?”, so dashboards, spreadsheets, AI assistants and applications all share the same definitions. ## Why teams choose it Companies rarely use one tool for data. Dashboards, spreadsheets, notebooks, customer-facing apps and now AI assistants all ask for the same metrics. Headless BI lets each of them query one set of definitions instead of rebuilding them in every tool. ## The trade-offs A standalone semantic layer is one more system to run, and each consuming tool needs an integration with it. Some teams find it simpler to use a BI tool whose semantic layer is open, through an API or SQL interface, than to run a separate headless product. ## How Silicon handles it Silicon’s metrics are defined in dbt YAML and served through Silicon’s dashboards, Slack and Microsoft Teams, Google Sheets syncs, embedded analytics and the API, so other tools use the same definitions. FAQ ## Headless BI questions ### What are examples of headless BI tools? + Cube, the dbt Semantic Layer and AtScale are often described as headless. Several BI tools also expose their semantic layer to other tools. ### Is headless BI the same as a semantic layer? + Headless BI is an architecture. The semantic layer is the component at its center. ## Related - Semantic layer → - Embedded analytics → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is metrics layer? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/metrics-layer/ The terms are often used interchangeably. Strictly, a metrics layer focuses on metric definitions (aggregations, filters, ratios), while a semantic layer also covers entities, dimensions, joins and access rules. ## What a metric definition contains A metric definition names the measure (for example, the sum of net_amount), the table it comes from, any filters (exclude refunds, exclude test accounts), the time grain it can be reported at, and the dimensions it can be broken down by. Ratio metrics, such as conversion rate, reference two other metrics. ## Why teams add one Without a metrics layer, each dashboard rebuilds the same calculation and small differences creep in, such as one chart that excludes refunds and one that does not. Leadership then sees two revenue numbers for the same month and has to ask which is right. Defining the metric once removes that question for every chart built on it. ## How Silicon handles it In Silicon, metrics are defined in dbt YAML or Silicon YAML, reviewed in pull requests like any other code, and listed in the metrics catalog with their owner and description. FAQ ## Metrics layer questions ### What is the difference between a metrics layer and a semantic layer? + A metrics layer covers metric definitions. A semantic layer also covers entities, joins, dimensions and access rules. Most products sold today as one include parts of the other. ### Where should metric definitions live? + Next to the data models they use, in version control, so a change to a model and the metrics that depend on it can be reviewed together. ## Related - Semantic layer → - dbt → - Semantic layer vs metrics layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is row-level security? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/row-level-security/ In a well-governed setup the rule is defined once and applied to every query, including dashboards, exports, embedded views and AI answers. ## How it works Each user carries attributes, such as region = EMEA or customer_id = 42. A rule on a table or model says which attribute filters which column, and the query engine adds that filter to every query the user runs. Column-level security works the same way for whole columns, such as hiding salary or personal data. ## Where it is enforced Rules can live in the warehouse (Snowflake row access policies, BigQuery row-level security), in the semantic layer, or in the BI tool. Enforcing them in one place that every query passes through makes them easier to audit. ## How Silicon handles it Silicon applies row- and column-level permissions to every query, including dashboards, exports, embedded views and AI analyst answers, because the AI runs each query as the person asking. FAQ ## Row-level security questions ### Does row-level security slow queries down? + Usually only slightly, since it adds a filter. Performance depends on whether the filtered columns are indexed or clustered. ### Does row-level security apply to AI assistants? + Only if the assistant queries through the same layer that enforces the rules. An assistant with its own warehouse credentials can bypass them. ## Related - Embedded analytics → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is schema drift? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/schema-drift/ Drift often comes from upstream: an application team renames a field, or a vendor changes an export. In analytics, the damage shows up downstream as broken charts, empty reports or silently wrong numbers. The most reliable defence is to check changes before they merge, against everything that depends on the changed model. ## Example customer_id is renamed to account_id in fct_orders. Three dashboards that group by customer_id stop loading. ## Types of schema drift Renamed columns and tables, dropped columns, changed data types (a string that becomes an integer), changed grain (one row per order becomes one row per order line), and new values in a field that downstream logic does not expect. The last two are the hardest to catch because nothing errors and the numbers simply change. ## How teams catch it Tests in dbt catch some drift inside the warehouse. Observability tools catch drift after it has landed by watching tables. Checking pull requests catches it earliest, by comparing the proposed change with every dashboard, metric and saved query that depends on the affected columns before the change merges. ## How Silicon handles it Silicon’s schema protection comments on each pull request with the dashboards and metrics a change would break, and can draft a fix pull request that updates the affected content. FAQ ## Schema drift questions ### Is schema drift the same as schema evolution? + Schema evolution is a planned change that is handled on purpose. Schema drift is a change that reaches downstream users without that planning. ### Can dbt tests catch schema drift? + Partly. Tests catch drift inside the dbt project, such as a column that becomes null. They do not see the dashboards and saved queries outside dbt that depend on a column. ## Related - Data lineage → - Dashboard reliability → - Data observability → - What is schema drift in analytics? → - Schema protection → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is semantic layer? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/semantic-layer/ Instead of every dashboard or analyst writing their own SQL for “revenue”, the definition is written once and every tool asks the semantic layer for it. The result is one answer to the same question, wherever it is asked. Semantic layers matter even more with AI. A language model that queries raw tables has to guess what columns mean. One that queries a semantic layer works from definitions your data team has already approved. ## Example net_revenue is defined once as the sum of net_amount in fct_revenue. Finance dashboards, sales reports and AI answers all use that definition. ## What goes into a semantic layer A semantic layer usually holds four things: metrics (how a number is calculated), dimensions (the ways you can slice it, such as region or plan), entities and joins (how tables relate to each other), and access rules (who can see which rows and columns). In a dbt project, much of this already exists as model descriptions, column types and tests. Tools like Silicon read that project and add metric definitions in the same YAML, so the semantic layer lives in version control next to the models it describes. ## Where it goes wrong The most common failure is a semantic layer that only one tool can read. Definitions end up copied into a second BI tool, a spreadsheet and a notebook, and they drift apart within months. The second is a metric with no clear owner, defined once by an analyst who has since left, so no one knows which filters it applies. ## How Silicon handles it Silicon uses your dbt project as the semantic layer. Metrics defined in column meta become governed metrics that dashboards, scheduled reports, the metrics catalog and the AI analyst all query, and each metric shows its definition and owner. FAQ ## Semantic layer questions ### Do I need a semantic layer if I only have one BI tool? + It still helps. Even with one tool, definitions written inside individual charts drift apart. A semantic layer keeps one definition per metric and makes it reusable when you add a second tool or an AI assistant. ### Is a semantic layer the same as a data warehouse? + No. The warehouse stores the data. The semantic layer sits on top and describes what the data means and how to calculate metrics from it. ### Which semantic layers work with dbt? + The dbt Semantic Layer (MetricFlow), Cube, Lightdash and Silicon all read dbt projects. They differ in where metrics are defined and which tools can query them. ## Related - Metrics layer → - dbt → - Text-to-SQL → - Silicon semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # What is text-to-SQL? Definition and Examples | Silicon URL: https://meetsilicon.com/glossary/text-to-sql/ On raw tables, text-to-SQL has to guess at joins and column meanings, which is where wrong answers come from. Grounding it in a semantic layer means the model chooses from defined metrics instead of writing business logic from scratch. ## Why raw text-to-SQL gets numbers wrong A model writing SQL against raw tables has to decide which table holds revenue, whether to exclude refunds, how tables join and which date to filter on. Each choice can be reasonable and still differ from how the finance team calculates the number, so the answer looks right and does not match the board report. ## What makes it more reliable Grounding the model in defined metrics and documented joins, showing the generated query with every answer so people can check it, and running queries with the asker’s permissions. Teams also review frequent questions and save the good answers as verified ones. ## How Silicon handles it Silicon’s AI analyst answers from the metrics in your semantic layer, shows the query and definitions behind each answer, and runs as the person asking so permissions apply. FAQ ## Text-to-SQL questions ### Is text-to-SQL accurate enough for business reporting? + On raw tables it is often unreliable for business metrics. Grounded in a semantic layer, with the query shown for checking, it is used for day-to-day questions at many companies. ### Does text-to-SQL replace data analysts? + It takes over routine lookups. Analysts still define the metrics it relies on and handle questions that need judgment. ## Related - Semantic layer → - How AI analytics works with dbt metrics → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Integrations: Warehouses, dbt, Git and Slack | Silicon URL: https://meetsilicon.com/integrations/ Integrations # Runs on the stack you already have Silicon connects to nine warehouses, reads your dbt project, checks pull requests on four git hosts, and delivers answers and reports to Slack, Microsoft Teams and Google Sheets. Your data stays where it is. ## Warehouses ### Snowflake All plans ### BigQuery All plans ### Databricks All plans ### Amazon Redshift All plans ### PostgreSQL All plans ### Trino All plans ### ClickHouse All plans ### Amazon Athena All plans ### DuckDB All plans ## Transformation ### dbt All plans ## Git ### GitHub All plans ### GitLab Pro and Enterprise ### Azure DevOps Pro and Enterprise ### Bitbucket Pro and Enterprise ## Collaboration ### Slack All plans ### Microsoft Teams Pro and Enterprise ### Google Sheets All plans How it fits together ## One set of metrics, every tool Silicon queries your warehouse live, so there is no copy of your data to keep in sync. Metrics come from your dbt project or Silicon YAML, which means a dashboard, a Slack answer and a Google Sheets sync all use the same definition of each number. Git integrations check every pull request against the dashboards and metrics that depend on the models it changes, and comment before anything merges. Delivery integrations send answers, scheduled reports and alerts to the places your team already reads, with each person’s permissions applied. Most teams connect a warehouse and their dbt repository first, then add Slack or Microsoft Teams once their key metrics are defined. ## Don’t see your tool? Tell us what you run. Silicon’s roadmap is shaped by the stacks our customers use. Book a demo 30-minute demo with the founder --- # Amazon Athena Integration: Dashboards and AI Analytics | Silicon URL: https://meetsilicon.com/integrations/athena/ Integrations / Amazon Athena # Silicon + Amazon Athena Connect Silicon to Amazon Athena and query it live through your semantic layer. Query data in S3 through Athena. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Dashboards, scheduled reports and alerts on Amazon Athena data - AI analyst answers that run as governed SQL against Amazon Athena - Schema checks that compare pull requests with your live warehouse - Row- and column-level permissions applied to every query Set-up ## Connect Amazon Athena - Create a user or service account in Amazon Athena with read access to the schemas your dbt models build into. - Add the Amazon Athena connection details in your Silicon project settings. - Connect your dbt project, or define metrics in Silicon YAML, so Silicon knows your models and metrics. - Build dashboards, ask the AI analyst or schedule reports. Queries run live in Amazon Athena. Detailed instructions for connecting Amazon Athena are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## Amazon Athena questions ### Does Silicon copy my data out of Amazon Athena? + Silicon queries Amazon Athena live through your semantic layer, so the data stays in your Amazon Athena account. ### What access does Silicon need in Amazon Athena? + Read access to the schemas and tables your dashboards and metrics use. Silicon does not need write access to your data. ### Do we need dbt? + It helps, because Silicon reads your models and descriptions from it. Metrics can also be defined in Silicon YAML without a dbt project. ### Which Silicon plans include Amazon Athena? + All plans. See the pricing page for what each plan includes. ## More warehouses integrations - Snowflake → - BigQuery → - Databricks → - Amazon Redshift → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Azure DevOps Integration: Dashboards and Metrics as Code URL: https://meetsilicon.com/integrations/azure-devops/ Integrations / Azure DevOps # Silicon + Azure DevOps Schema protection and dashboards as code for repositories in Azure DevOps. Available on: Pro and Enterprise Book a demo 30-minute demo with the founder Features ## What you can do - Impact comments on pull requests - Drafted fix pull requests - Dashboards and charts stored as YAML Set-up ## Connect Azure DevOps - Connect Silicon to your Azure DevOps account and choose the repository that holds your dbt project. - Silicon checks each new pull request against the dashboards and metrics that depend on the changed models. - When a change would break something, Silicon comments on the pull request and can draft a fix for review. Detailed instructions for connecting Azure DevOps are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## Azure DevOps questions ### Does Silicon push to our main branch? + No. Fixes are opened as pull requests for your team to review and merge. ### Can dashboards be stored in the repository? + Yes. Dashboards and charts can be kept as YAML next to your dbt project and previewed from a branch. ### Which Silicon plans include Azure DevOps? + Pro and Enterprise. See the pricing page for what each plan includes. ## More git integrations - GitHub → - GitLab → - Bitbucket → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # BigQuery Integration: Dashboards and AI Analytics | Silicon URL: https://meetsilicon.com/integrations/bigquery/ Integrations / BigQuery # Silicon + BigQuery Connect Silicon to BigQuery and query it live through your semantic layer. Queries run in your Google Cloud project under the service account you provide. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Dashboards, scheduled reports and alerts on BigQuery data - AI analyst answers that run as governed SQL against BigQuery - Schema checks that compare pull requests with your live warehouse - Row- and column-level permissions applied to every query Set-up ## Connect BigQuery - Create a user or service account in BigQuery with read access to the schemas your dbt models build into. - Add the BigQuery connection details in your Silicon project settings. - Connect your dbt project, or define metrics in Silicon YAML, so Silicon knows your models and metrics. - Build dashboards, ask the AI analyst or schedule reports. Queries run live in BigQuery. Detailed instructions for connecting BigQuery are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## BigQuery questions ### Does Silicon copy my data out of BigQuery? + Silicon queries BigQuery live through your semantic layer, so the data stays in your BigQuery account. ### What access does Silicon need in BigQuery? + Read access to the schemas and tables your dashboards and metrics use. Silicon does not need write access to your data. ### Do we need dbt? + It helps, because Silicon reads your models and descriptions from it. Metrics can also be defined in Silicon YAML without a dbt project. ### Which Silicon plans include BigQuery? + All plans. See the pricing page for what each plan includes. ## More warehouses integrations - Snowflake → - Databricks → - Amazon Redshift → - PostgreSQL → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Bitbucket Integration: Dashboards and Metrics as Code | Silicon URL: https://meetsilicon.com/integrations/bitbucket/ Integrations / Bitbucket # Silicon + Bitbucket Schema protection and dashboards as code for repositories in Bitbucket. Available on: Pro and Enterprise Book a demo 30-minute demo with the founder Features ## What you can do - Impact comments on pull requests - Drafted fix pull requests - Dashboards and charts stored as YAML Set-up ## Connect Bitbucket - Connect Silicon to your Bitbucket account and choose the repository that holds your dbt project. - Silicon checks each new pull request against the dashboards and metrics that depend on the changed models. - When a change would break something, Silicon comments on the pull request and can draft a fix for review. Detailed instructions for connecting Bitbucket are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## Bitbucket questions ### Does Silicon push to our main branch? + No. Fixes are opened as pull requests for your team to review and merge. ### Can dashboards be stored in the repository? + Yes. Dashboards and charts can be kept as YAML next to your dbt project and previewed from a branch. ### Which Silicon plans include Bitbucket? + Pro and Enterprise. See the pricing page for what each plan includes. ## More git integrations - GitHub → - GitLab → - Azure DevOps → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # ClickHouse Integration: Dashboards and AI Analytics | Silicon URL: https://meetsilicon.com/integrations/clickhouse/ Integrations / ClickHouse # Silicon + ClickHouse Connect Silicon to ClickHouse and query it live through your semantic layer. Fast analytics on ClickHouse, self-hosted or in ClickHouse Cloud. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Dashboards, scheduled reports and alerts on ClickHouse data - AI analyst answers that run as governed SQL against ClickHouse - Schema checks that compare pull requests with your live warehouse - Row- and column-level permissions applied to every query Set-up ## Connect ClickHouse - Create a user or service account in ClickHouse with read access to the schemas your dbt models build into. - Add the ClickHouse connection details in your Silicon project settings. - Connect your dbt project, or define metrics in Silicon YAML, so Silicon knows your models and metrics. - Build dashboards, ask the AI analyst or schedule reports. Queries run live in ClickHouse. Detailed instructions for connecting ClickHouse are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## ClickHouse questions ### Does Silicon copy my data out of ClickHouse? + Silicon queries ClickHouse live through your semantic layer, so the data stays in your ClickHouse account. ### What access does Silicon need in ClickHouse? + Read access to the schemas and tables your dashboards and metrics use. Silicon does not need write access to your data. ### Do we need dbt? + It helps, because Silicon reads your models and descriptions from it. Metrics can also be defined in Silicon YAML without a dbt project. ### Which Silicon plans include ClickHouse? + All plans. See the pricing page for what each plan includes. ## More warehouses integrations - Snowflake → - BigQuery → - Databricks → - Amazon Redshift → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Databricks Integration: Dashboards and AI Analytics | Silicon URL: https://meetsilicon.com/integrations/databricks/ Integrations / Databricks # Silicon + Databricks Connect Silicon to Databricks and query it live through your semantic layer. Connect to a SQL warehouse in your Databricks workspace. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Dashboards, scheduled reports and alerts on Databricks data - AI analyst answers that run as governed SQL against Databricks - Schema checks that compare pull requests with your live warehouse - Row- and column-level permissions applied to every query Set-up ## Connect Databricks - Create a user or service account in Databricks with read access to the schemas your dbt models build into. - Add the Databricks connection details in your Silicon project settings. - Connect your dbt project, or define metrics in Silicon YAML, so Silicon knows your models and metrics. - Build dashboards, ask the AI analyst or schedule reports. Queries run live in Databricks. Detailed instructions for connecting Databricks are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## Databricks questions ### Does Silicon copy my data out of Databricks? + Silicon queries Databricks live through your semantic layer, so the data stays in your Databricks account. ### What access does Silicon need in Databricks? + Read access to the schemas and tables your dashboards and metrics use. Silicon does not need write access to your data. ### Do we need dbt? + It helps, because Silicon reads your models and descriptions from it. Metrics can also be defined in Silicon YAML without a dbt project. ### Which Silicon plans include Databricks? + All plans. See the pricing page for what each plan includes. ## More warehouses integrations - Snowflake → - BigQuery → - Amazon Redshift → - PostgreSQL → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # dbt Integration: Your dbt Project as a Semantic Layer | Silicon URL: https://meetsilicon.com/integrations/dbt/ Integrations / dbt # Silicon + dbt Silicon reads your dbt project directly: models, columns, descriptions, tests and the metrics you define in column meta. Your dbt project becomes your semantic layer. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Metrics defined under meta.metrics become governed, reusable metrics - dbt descriptions give the AI analyst business context - Model dependencies power schema protection and lineage - Works with dbt Core and dbt Cloud projects Set-up ## Connect dbt - Connect the Git repository that holds your dbt project, or upload it with the Silicon CLI. - Silicon compiles the project and lists your models, columns and descriptions. - Add metrics under meta in your model YAML and they appear in the metrics catalog. - Open a pull request to change a metric, and it is reviewed like any other code. Detailed instructions for connecting dbt are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## dbt questions ### Does it work with dbt Core and dbt Cloud? + Yes. Silicon reads the project files, so it works the same with either. ### Do we have to rewrite our metrics? + No. Metrics you already define in column meta are read as they are, and you can add more in the same YAML. ### Which Silicon plans include dbt? + All plans. See the pricing page for what each plan includes. ## More transformation integrations - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # DuckDB Integration: Dashboards and AI Analytics | Silicon URL: https://meetsilicon.com/integrations/duckdb/ Integrations / DuckDB # Silicon + DuckDB Connect Silicon to DuckDB and query it live through your semantic layer. Connect to DuckDB and MotherDuck. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Dashboards, scheduled reports and alerts on DuckDB data - AI analyst answers that run as governed SQL against DuckDB - Schema checks that compare pull requests with your live warehouse - Row- and column-level permissions applied to every query Set-up ## Connect DuckDB - Create a user or service account in DuckDB with read access to the schemas your dbt models build into. - Add the DuckDB connection details in your Silicon project settings. - Connect your dbt project, or define metrics in Silicon YAML, so Silicon knows your models and metrics. - Build dashboards, ask the AI analyst or schedule reports. Queries run live in DuckDB. Detailed instructions for connecting DuckDB are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## DuckDB questions ### Does Silicon copy my data out of DuckDB? + Silicon queries DuckDB live through your semantic layer, so the data stays in your DuckDB account. ### What access does Silicon need in DuckDB? + Read access to the schemas and tables your dashboards and metrics use. Silicon does not need write access to your data. ### Do we need dbt? + It helps, because Silicon reads your models and descriptions from it. Metrics can also be defined in Silicon YAML without a dbt project. ### Which Silicon plans include DuckDB? + All plans. See the pricing page for what each plan includes. ## More warehouses integrations - Snowflake → - BigQuery → - Databricks → - Amazon Redshift → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # GitHub Integration: Dashboards and Metrics as Code | Silicon URL: https://meetsilicon.com/integrations/github/ Integrations / GitHub # Silicon + GitHub Connect a GitHub repository so Silicon can check pull requests for breaking changes and keep dashboards as code in sync. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Impact comments on every pull request - Drafted fix pull requests - Dashboards and charts stored as YAML - Preview changes from a branch Set-up ## Connect GitHub - Connect Silicon to your GitHub account and choose the repository that holds your dbt project. - Silicon checks each new pull request against the dashboards and metrics that depend on the changed models. - When a change would break something, Silicon comments on the pull request and can draft a fix for review. Detailed instructions for connecting GitHub are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## GitHub questions ### Does Silicon push to our main branch? + No. Fixes are opened as pull requests for your team to review and merge. ### Can dashboards be stored in the repository? + Yes. Dashboards and charts can be kept as YAML next to your dbt project and previewed from a branch. ### Which Silicon plans include GitHub? + All plans. See the pricing page for what each plan includes. ## More git integrations - GitLab → - Azure DevOps → - Bitbucket → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # GitLab Integration: Dashboards and Metrics as Code | Silicon URL: https://meetsilicon.com/integrations/gitlab/ Integrations / GitLab # Silicon + GitLab Schema protection and dashboards as code for projects hosted on GitLab. Available on: Pro and Enterprise Book a demo 30-minute demo with the founder Features ## What you can do - Impact comments on merge requests - Drafted fix merge requests - Dashboards and charts stored as YAML Set-up ## Connect GitLab - Connect Silicon to your GitLab account and choose the repository that holds your dbt project. - Silicon checks each new pull request against the dashboards and metrics that depend on the changed models. - When a change would break something, Silicon comments on the pull request and can draft a fix for review. Detailed instructions for connecting GitLab are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## GitLab questions ### Does Silicon push to our main branch? + No. Fixes are opened as pull requests for your team to review and merge. ### Can dashboards be stored in the repository? + Yes. Dashboards and charts can be kept as YAML next to your dbt project and previewed from a branch. ### Which Silicon plans include GitLab? + Pro and Enterprise. See the pricing page for what each plan includes. ## More git integrations - GitHub → - Azure DevOps → - Bitbucket → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Google Sheets Integration: Scheduled Syncs of Governed Metrics URL: https://meetsilicon.com/integrations/google-sheets/ Integrations / Google Sheets # Silicon + Google Sheets Sync charts and query results to Google Sheets on a schedule, so spreadsheet users always work from governed numbers. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Scheduled syncs of chart results - Governed metrics in familiar spreadsheets Set-up ## Connect Google Sheets - Connect a Google account from your Silicon project settings. - Open a chart or saved query and choose Google Sheets as a scheduled delivery. - Pick the spreadsheet and how often it should sync. Each sync runs a fresh query. Detailed instructions for connecting Google Sheets are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## Google Sheets questions ### Which Silicon plans include Google Sheets? + All plans. See the pricing page for what each plan includes. ### Can people edit the synced data? + The synced range is overwritten on each run. Build formulas and charts on a separate tab that references it. ## More collaboration integrations - Slack → - Microsoft Teams → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Microsoft Teams Integration: Scheduled Reports and AI Answers URL: https://meetsilicon.com/integrations/microsoft-teams/ Integrations / Microsoft Teams # Silicon + Microsoft Teams Answers, scheduled reports and alerts delivered to Microsoft Teams channels. Available on: Pro and Enterprise Book a demo 30-minute demo with the founder Features ## What you can do - AI analyst answers - Scheduled deliveries - Metric and schema-change alerts Set-up ## Connect Microsoft Teams - Add the Silicon app to your Microsoft Teams workspace from your Silicon project settings. - Invite Silicon to the channels where your team wants answers, reports or alerts. - Ask questions in a channel, or schedule a dashboard or chart to arrive there. Detailed instructions for connecting Microsoft Teams are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## Microsoft Teams questions ### Do answers in Microsoft Teams respect permissions? + Yes. The AI analyst runs each query as the person asking, so row- and column-level permissions apply. ### What can Silicon send to Microsoft Teams? + Answers to questions, scheduled dashboards and charts, metric alerts and alerts about changes that would break dashboards. ### Which Silicon plans include Microsoft Teams? + Pro and Enterprise. See the pricing page for what each plan includes. ## More collaboration integrations - Slack → - Google Sheets → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # PostgreSQL Integration: Dashboards and AI Analytics | Silicon URL: https://meetsilicon.com/integrations/postgres/ Integrations / PostgreSQL # Silicon + PostgreSQL Connect Silicon to PostgreSQL and query it live through your semantic layer. Point Silicon at a Postgres database or read replica. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Dashboards, scheduled reports and alerts on PostgreSQL data - AI analyst answers that run as governed SQL against PostgreSQL - Schema checks that compare pull requests with your live warehouse - Row- and column-level permissions applied to every query Set-up ## Connect PostgreSQL - Create a user or service account in PostgreSQL with read access to the schemas your dbt models build into. - Add the PostgreSQL connection details in your Silicon project settings. - Connect your dbt project, or define metrics in Silicon YAML, so Silicon knows your models and metrics. - Build dashboards, ask the AI analyst or schedule reports. Queries run live in PostgreSQL. Detailed instructions for connecting PostgreSQL are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## PostgreSQL questions ### Does Silicon copy my data out of PostgreSQL? + Silicon queries PostgreSQL live through your semantic layer, so the data stays in your PostgreSQL account. ### What access does Silicon need in PostgreSQL? + Read access to the schemas and tables your dashboards and metrics use. Silicon does not need write access to your data. ### Do we need dbt? + It helps, because Silicon reads your models and descriptions from it. Metrics can also be defined in Silicon YAML without a dbt project. ### Which Silicon plans include PostgreSQL? + All plans. See the pricing page for what each plan includes. ## More warehouses integrations - Snowflake → - BigQuery → - Databricks → - Amazon Redshift → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Amazon Redshift Integration: Dashboards and AI Analytics URL: https://meetsilicon.com/integrations/redshift/ Integrations / Amazon Redshift # Silicon + Amazon Redshift Connect Silicon to Amazon Redshift and query it live through your semantic layer. Works with provisioned clusters and Redshift Serverless. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Dashboards, scheduled reports and alerts on Amazon Redshift data - AI analyst answers that run as governed SQL against Amazon Redshift - Schema checks that compare pull requests with your live warehouse - Row- and column-level permissions applied to every query Set-up ## Connect Amazon Redshift - Create a user or service account in Amazon Redshift with read access to the schemas your dbt models build into. - Add the Amazon Redshift connection details in your Silicon project settings. - Connect your dbt project, or define metrics in Silicon YAML, so Silicon knows your models and metrics. - Build dashboards, ask the AI analyst or schedule reports. Queries run live in Amazon Redshift. Detailed instructions for connecting Amazon Redshift are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## Amazon Redshift questions ### Does Silicon copy my data out of Amazon Redshift? + Silicon queries Amazon Redshift live through your semantic layer, so the data stays in your Amazon Redshift account. ### What access does Silicon need in Amazon Redshift? + Read access to the schemas and tables your dashboards and metrics use. Silicon does not need write access to your data. ### Do we need dbt? + It helps, because Silicon reads your models and descriptions from it. Metrics can also be defined in Silicon YAML without a dbt project. ### Which Silicon plans include Amazon Redshift? + All plans. See the pricing page for what each plan includes. ## More warehouses integrations - Snowflake → - BigQuery → - Databricks → - PostgreSQL → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Slack Integration: Scheduled Reports and AI Answers | Silicon URL: https://meetsilicon.com/integrations/slack/ Integrations / Slack # Silicon + Slack Ask the AI analyst questions in any channel, receive scheduled reports, and get schema-change alerts where your team already works. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - AI analyst answers in threads - Scheduled dashboard and chart deliveries - Threshold alerts on metrics - Schema-change alerts Set-up ## Connect Slack - Add the Silicon app to your Slack workspace from your Silicon project settings. - Invite Silicon to the channels where your team wants answers, reports or alerts. - Ask questions in a channel, or schedule a dashboard or chart to arrive there. Detailed instructions for connecting Slack are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## Slack questions ### Do answers in Slack respect permissions? + Yes. The AI analyst runs each query as the person asking, so row- and column-level permissions apply. ### What can Silicon send to Slack? + Answers to questions, scheduled dashboards and charts, metric alerts and alerts about changes that would break dashboards. ### Which Silicon plans include Slack? + All plans. See the pricing page for what each plan includes. ## More collaboration integrations - Microsoft Teams → - Google Sheets → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Snowflake Integration: Dashboards and AI Analytics | Silicon URL: https://meetsilicon.com/integrations/snowflake/ Integrations / Snowflake # Silicon + Snowflake Connect Silicon to Snowflake and query it live through your semantic layer. Queries run in your Snowflake account using the warehouse and role you choose. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Dashboards, scheduled reports and alerts on Snowflake data - AI analyst answers that run as governed SQL against Snowflake - Schema checks that compare pull requests with your live warehouse - Row- and column-level permissions applied to every query Set-up ## Connect Snowflake - Create a user or service account in Snowflake with read access to the schemas your dbt models build into. - Add the Snowflake connection details in your Silicon project settings. - Connect your dbt project, or define metrics in Silicon YAML, so Silicon knows your models and metrics. - Build dashboards, ask the AI analyst or schedule reports. Queries run live in Snowflake. Detailed instructions for connecting Snowflake are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## Snowflake questions ### Does Silicon copy my data out of Snowflake? + Silicon queries Snowflake live through your semantic layer, so the data stays in your Snowflake account. ### What access does Silicon need in Snowflake? + Read access to the schemas and tables your dashboards and metrics use. Silicon does not need write access to your data. ### Do we need dbt? + It helps, because Silicon reads your models and descriptions from it. Metrics can also be defined in Silicon YAML without a dbt project. ### Which Silicon plans include Snowflake? + All plans. See the pricing page for what each plan includes. ## More warehouses integrations - BigQuery → - Databricks → - Amazon Redshift → - PostgreSQL → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Trino Integration: Dashboards and AI Analytics | Silicon URL: https://meetsilicon.com/integrations/trino/ Integrations / Trino # Silicon + Trino Connect Silicon to Trino and query it live through your semantic layer. Query federated sources through your Trino cluster. Available on: All plans Book a demo 30-minute demo with the founder Features ## What you can do - Dashboards, scheduled reports and alerts on Trino data - AI analyst answers that run as governed SQL against Trino - Schema checks that compare pull requests with your live warehouse - Row- and column-level permissions applied to every query Set-up ## Connect Trino - Create a user or service account in Trino with read access to the schemas your dbt models build into. - Add the Trino connection details in your Silicon project settings. - Connect your dbt project, or define metrics in Silicon YAML, so Silicon knows your models and metrics. - Build dashboards, ask the AI analyst or schedule reports. Queries run live in Trino. Detailed instructions for connecting Trino are in the Silicon docs. Questions about your setup? Talk to Sujay. FAQ ## Trino questions ### Does Silicon copy my data out of Trino? + Silicon queries Trino live through your semantic layer, so the data stays in your Trino account. ### What access does Silicon need in Trino? + Read access to the schemas and tables your dashboards and metrics use. Silicon does not need write access to your data. ### Do we need dbt? + It helps, because Silicon reads your models and descriptions from it. Metrics can also be defined in Silicon YAML without a dbt project. ### Which Silicon plans include Trino? + All plans. See the pricing page for what each plan includes. ## More warehouses integrations - Snowflake → - BigQuery → - Databricks → - Amazon Redshift → - All integrations → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Metric Library: Definitions, Formulas and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/ Metric library # Metric library Definitions, formulas and copy-ready dbt YAML for the metrics most teams track. Define them once, and every dashboard and AI answer in Silicon uses the same logic. ## Revenue ### Monthly recurring revenue (MRR) MRR = sum of monthly subscription value for all active subscriptions ### Annual recurring revenue (ARR) ARR = MRR × 12 ### Net revenue Net revenue = gross revenue − refunds − discounts − returns ### Average order value (AOV) AOV = net revenue ÷ number of orders ### Gross margin Gross margin = (revenue − cost of goods sold) ÷ revenue ## Growth ### Active users Active users = count of distinct users with at least one qualifying event ### Signup conversion rate Signup conversion rate = signups ÷ unique visitors ### Activation rate Activation rate = activated signups ÷ total signups ## Customers ### Retention rate Retention rate = customers retained ÷ customers at start of period ### Churn rate Churn rate = customers lost during period ÷ customers at start of period ### Net revenue retention (NRR) NRR = (starting MRR + expansion − contraction − churned MRR) ÷ starting MRR ### Average revenue per user (ARPU) ARPU = revenue ÷ active users (or accounts) ## Sales ### Customer acquisition cost (CAC) CAC = sales and marketing spend ÷ new customers acquired ### Win rate Win rate = opportunities won ÷ opportunities closed (won + lost) ### Pipeline coverage Pipeline coverage = open pipeline value ÷ quota ## Operations ### Average resolution time Average resolution time = total resolution hours ÷ resolved tickets ### On-time delivery rate On-time delivery rate = orders delivered on time ÷ orders delivered ### Ticket volume Ticket volume = count of tickets created ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Activation rate: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/activation-rate/ ## Formula Activation rate = activated signups ÷ total signups ## Why it matters Activation predicts retention better than signups do. Improving it is usually the fastest way to grow active users. ## Common pitfalls - Pick one activation event and keep it stable, or trends become meaningless. - Use a fixed window, such as 7 days after signup. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_signups meta: metrics: activation_rate: type: number sql: ${activated} / NULLIF(${signups}, 0) format: percent description: Share of signups that activate columns: - name: signup_id meta: metrics: signups: type: count_distinct description: All signups - name: activated_signup_id meta: metrics: activated: type: count_distinct description: Signups that activated within 7 days Once it’s defined, anyone can ask about activation rate in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Active users → - Signup conversion rate → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Active users: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/active-users/ ## Formula Active users = count of distinct users with at least one qualifying event ## Why it matters Active users is the base of most engagement metrics. Daily, weekly and monthly versions come from the same definition at different time grains. ## Common pitfalls - Define “active” as a meaningful action, not just a login or page view. - Exclude internal and test accounts. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_events columns: - name: user_id meta: metrics: active_users: type: count_distinct filters: - is_qualifying_event: true description: Distinct users with a qualifying event Once it’s defined, anyone can ask about active users in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Signup conversion rate → - Activation rate → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Annual recurring revenue (ARR): Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/annual-recurring-revenue/ ## Formula ARR = MRR × 12 ## Why it matters ARR is the standard headline figure for SaaS companies and the basis for most valuation and planning conversations. ## Common pitfalls - Do not include one-time services revenue. - Be explicit about whether trials and paused accounts count. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_subscriptions meta: metrics: arr: type: number sql: ${mrr} * 12 description: Annual recurring revenue columns: - name: monthly_amount meta: metrics: mrr: type: sum filters: - is_active: true description: Monthly recurring revenue Once it’s defined, anyone can ask about annual recurring revenue in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Monthly recurring revenue (MRR) → - Net revenue → - Average order value (AOV) → - Gross margin → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Average order value (AOV): Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/average-order-value/ ## Formula AOV = net revenue ÷ number of orders ## Why it matters AOV shows whether growth comes from more orders or bigger ones, and is a direct lever for pricing and bundling decisions. ## Common pitfalls - Exclude cancelled orders from both the numerator and the denominator. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_orders meta: metrics: average_order_value: type: number sql: ${net_revenue} / NULLIF(${orders}, 0) description: Average net revenue per order columns: - name: net_amount meta: metrics: net_revenue: type: sum filters: - is_cancelled: false description: Net revenue from completed orders - name: order_id meta: metrics: orders: type: count_distinct filters: - is_cancelled: false description: Completed orders Once it’s defined, anyone can ask about average order value in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Monthly recurring revenue (MRR) → - Annual recurring revenue (ARR) → - Net revenue → - Gross margin → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Average resolution time: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/average-resolution-time/ ## Formula Average resolution time = total resolution hours ÷ resolved tickets ## Why it matters Resolution time is closely tied to customer satisfaction and shows where support processes slow down. ## Common pitfalls - Medians are often more useful than averages because a few long tickets skew the mean. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_tickets columns: - name: resolution_hours meta: metrics: avg_resolution_hours: type: average filters: - is_resolved: true description: Mean hours to resolve a ticket Once it’s defined, anyone can ask about average resolution time in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - On-time delivery rate → - Ticket volume → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Average revenue per user (ARPU): Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/average-revenue-per-user/ ## Formula ARPU = revenue ÷ active users (or accounts) ## Why it matters ARPU tracks monetisation separately from user growth, and shows the effect of pricing and packaging changes. ## Common pitfalls - State clearly whether the denominator is users or paying accounts. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_account_months meta: metrics: arpu: type: number sql: ${revenue} / NULLIF(${paying_accounts}, 0) description: Revenue per paying account columns: - name: revenue meta: metrics: revenue: type: sum description: Revenue in the period - name: account_id meta: metrics: paying_accounts: type: count_distinct filters: - is_paying: true description: Paying accounts Once it’s defined, anyone can ask about average revenue per user in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Retention rate → - Churn rate → - Net revenue retention (NRR) → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Churn rate: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/churn-rate/ ## Formula Churn rate = customers lost during period ÷ customers at start of period ## Why it matters Churn sets the ceiling on growth. If churn is high, acquisition spend just refills a leaking bucket. ## Common pitfalls - Decide whether downgrades count as churn; most teams track them separately as contraction. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_customer_periods meta: metrics: churn_rate: type: number sql: ${customers_churned} / NULLIF(${customers_at_start}, 0) format: percent description: Share of customers lost columns: - name: customer_id meta: metrics: customers_at_start: type: count_distinct filters: - was_active_at_start: true description: Customers active at period start - name: churned_customer_id meta: metrics: customers_churned: type: count_distinct description: Customers who churned in the period Once it’s defined, anyone can ask about churn rate in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Retention rate → - Net revenue retention (NRR) → - Average revenue per user (ARPU) → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Customer acquisition cost (CAC): Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/customer-acquisition-cost/ ## Formula CAC = sales and marketing spend ÷ new customers acquired ## Why it matters CAC, compared with customer lifetime value, tells you whether growth is profitable. ## Common pitfalls - Include salaries and tools, not just ad spend. - Lag spend appropriately if your sales cycle is long. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_acquisition_months meta: metrics: cac: type: number sql: ${acquisition_spend} / NULLIF(${new_customers}, 0) description: Spend per new customer columns: - name: sales_marketing_spend meta: metrics: acquisition_spend: type: sum description: Sales and marketing spend - name: new_customer_id meta: metrics: new_customers: type: count_distinct description: New customers won Once it’s defined, anyone can ask about customer acquisition cost in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Win rate → - Pipeline coverage → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Gross margin: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/gross-margin/ ## Formula Gross margin = (revenue − cost of goods sold) ÷ revenue ## Why it matters Gross margin shows how efficiently each sale turns into profit before operating costs, and limits how much you can spend to acquire customers. ## Common pitfalls - Be consistent about what counts as cost of goods sold, especially hosting and support costs. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_revenue meta: metrics: gross_margin: type: number sql: (${net_revenue} - ${cogs}) / NULLIF(${net_revenue}, 0) format: percent description: Share of revenue kept after direct costs columns: - name: net_amount meta: metrics: net_revenue: type: sum description: Net revenue - name: cogs_amount meta: metrics: cogs: type: sum description: Cost of goods sold Once it’s defined, anyone can ask about gross margin in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Monthly recurring revenue (MRR) → - Annual recurring revenue (ARR) → - Net revenue → - Average order value (AOV) → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Monthly recurring revenue (MRR): Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/monthly-recurring-revenue/ ## Formula MRR = sum of monthly subscription value for all active subscriptions ## Why it matters MRR is the heartbeat of a subscription business. Movements in MRR break down into new, expansion, contraction and churned revenue. ## Common pitfalls - Annual plans must be divided by 12, not counted in the month they are billed. - One-off fees and usage overages are usually excluded. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_subscriptions columns: - name: monthly_amount meta: metrics: mrr: type: sum filters: - is_active: true description: Monthly recurring revenue from active subscriptions Once it’s defined, anyone can ask about monthly recurring revenue in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Annual recurring revenue (ARR) → - Net revenue → - Average order value (AOV) → - Gross margin → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Net revenue: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/net-revenue/ ## Formula Net revenue = gross revenue − refunds − discounts − returns ## Why it matters Gross revenue overstates what the business keeps. Net revenue is the figure finance reports and the one most teams mean when they say “revenue”. ## Common pitfalls - Refunds often land in a later period than the original sale; decide which date you attribute them to. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_revenue columns: - name: net_amount meta: metrics: net_revenue: type: sum description: Revenue after refunds and discounts Once it’s defined, anyone can ask about net revenue in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Monthly recurring revenue (MRR) → - Annual recurring revenue (ARR) → - Average order value (AOV) → - Gross margin → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Net revenue retention (NRR): Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/net-revenue-retention/ ## Formula NRR = (starting MRR + expansion − contraction − churned MRR) ÷ starting MRR ## Why it matters NRR above 100% means the existing customer base grows on its own. It is one of the strongest signals of product value in SaaS. ## Common pitfalls - Only include customers who existed at the start of the period. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_mrr_movements meta: metrics: net_revenue_retention: type: number sql: ${ending_mrr} / NULLIF(${starting_mrr}, 0) format: percent description: Revenue kept and grown from existing customers columns: - name: starting_mrr meta: metrics: starting_mrr: type: sum description: MRR at period start from existing customers - name: ending_mrr meta: metrics: ending_mrr: type: sum description: MRR at period end from the same customers Once it’s defined, anyone can ask about net revenue retention in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Retention rate → - Churn rate → - Average revenue per user (ARPU) → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # On-time delivery rate: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/on-time-delivery-rate/ ## Formula On-time delivery rate = orders delivered on time ÷ orders delivered ## Why it matters It is a direct measure of operational reliability and a leading indicator of repeat purchase. ## Common pitfalls - Use the promise shown to the customer, not an internal target. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_deliveries meta: metrics: on_time_delivery_rate: type: number sql: ${on_time_deliveries} / NULLIF(${deliveries}, 0) format: percent description: Share of orders delivered on time columns: - name: delivery_id meta: metrics: deliveries: type: count_distinct description: Delivered orders - name: on_time_delivery_id meta: metrics: on_time_deliveries: type: count_distinct description: Orders delivered by the promised date Once it’s defined, anyone can ask about on-time delivery rate in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Average resolution time → - Ticket volume → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Pipeline coverage: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/pipeline-coverage/ ## Formula Pipeline coverage = open pipeline value ÷ quota ## Why it matters Coverage shows whether there is enough pipeline to hit target. A common rule of thumb is three times quota, adjusted for your win rate. ## Common pitfalls - Only count opportunities expected to close in the period. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_pipeline_periods meta: metrics: pipeline_coverage: type: number sql: ${open_pipeline} / NULLIF(${quota}, 0) description: Pipeline as a multiple of quota columns: - name: open_amount meta: metrics: open_pipeline: type: sum description: Open pipeline closing this period - name: quota_amount meta: metrics: quota: type: sum description: Sales quota for the period Once it’s defined, anyone can ask about pipeline coverage in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Customer acquisition cost (CAC) → - Win rate → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Retention rate: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/retention-rate/ ## Formula Retention rate = customers retained ÷ customers at start of period ## Why it matters Retention compounds. Small improvements in retention have a larger long-term effect on revenue than equal improvements in acquisition. ## Common pitfalls - Exclude customers acquired during the period from the calculation. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_customer_periods meta: metrics: retention_rate: type: number sql: ${customers_retained} / NULLIF(${customers_at_start}, 0) format: percent description: Share of customers retained columns: - name: customer_id meta: metrics: customers_at_start: type: count_distinct filters: - was_active_at_start: true description: Customers active at period start - name: retained_customer_id meta: metrics: customers_retained: type: count_distinct description: Start customers still active at period end Once it’s defined, anyone can ask about retention rate in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Churn rate → - Net revenue retention (NRR) → - Average revenue per user (ARPU) → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Signup conversion rate: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/signup-conversion-rate/ ## Formula Signup conversion rate = signups ÷ unique visitors ## Why it matters It measures how well your site and onboarding turn interest into accounts, and is the first step of most growth funnels. ## Common pitfalls - Visitors and signups must be counted over the same period and from the same traffic sources. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_web_sessions meta: metrics: signup_conversion_rate: type: number sql: ${signups} / NULLIF(${visitors}, 0) format: percent description: Share of visitors who sign up columns: - name: visitor_id meta: metrics: visitors: type: count_distinct description: Unique visitors - name: signup_id meta: metrics: signups: type: count_distinct description: Accounts created Once it’s defined, anyone can ask about signup conversion rate in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Active users → - Activation rate → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Ticket volume: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/ticket-volume/ ## Formula Ticket volume = count of tickets created ## Why it matters Volume, broken down by category, shows where customers struggle. Spikes often point to a product or billing issue. ## Common pitfalls - Merge duplicates before counting. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_tickets columns: - name: ticket_id meta: metrics: tickets: type: count_distinct description: Tickets opened Once it’s defined, anyone can ask about ticket volume in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Average resolution time → - On-time delivery rate → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Win rate: Formula and dbt YAML | Silicon URL: https://meetsilicon.com/metrics/win-rate/ ## Formula Win rate = opportunities won ÷ opportunities closed (won + lost) ## Why it matters Win rate shows sales effectiveness and is a key input to pipeline coverage targets. ## Common pitfalls - Exclude open opportunities, or the rate will look artificially low. ## Define it in dbt for Silicon Add this to your model’s YAML. Aggregate metrics sit under a column’s meta.metrics; ratios are type: number metrics that reference other metrics. Rename models and columns to match your project. schema.yml Copy models: - name: fct_opportunities meta: metrics: win_rate: type: number sql: ${won_opportunities} / NULLIF(${closed_opportunities}, 0) format: percent description: Share of closed deals won columns: - name: opportunity_id meta: metrics: closed_opportunities: type: count_distinct filters: - is_closed: true description: Closed opportunities - name: won_opportunity_id meta: metrics: won_opportunities: type: count_distinct description: Won opportunities Once it’s defined, anyone can ask about win rate in plain English and get an answer built on this definition. See the metrics reference for every option. ## Related - Customer acquisition cost (CAC) → - Pipeline coverage → - Semantic layer → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # The Silicon Platform for Data Teams | Silicon URL: https://meetsilicon.com/platform/ Silicon platform # For companies that already have a data team Connect Silicon to your warehouse and dbt project. Your team defines each metric once, anyone can ask in Slack or build a dashboard, and Silicon warns you before a change breaks one. Book a demo 30-minute demo with the founder Product tour coming soon See pricing How it fits together ## How Silicon sits on your stack Your team keeps running the warehouse and dbt. Silicon reads them where they are and turns your metric definitions into answers, dashboards and checks. Silicon platform 06 ### Where your team gets answers People ask and read where they already work. Slack Microsoft Teams Google Sheets Web app Email digests Inside your product 05 ### Ask, share and build Every tool reads the same metric definitions, so a chart and an answer in Slack agree. AI analyst ↗ Dashboards & reports ↗ Data apps ↗ Embedded analytics ↗ 04 ### Keep it from breaking Changes to your data are checked before they reach a dashboard. Schema protection ↗ Dashboards as code ↗ [ Run by your data team ] Or we can run it for you → 03 ### One definition for every metric Revenue, churn and pipeline are defined once, with the team that owns each number. Metric definitions ↗ Joins and dimensions Row and column permissions What depends on what 02 ### Your data foundation Pipelines, warehouse and models, kept accurate as the company grows. Pipelines Warehouse dbt models Tests and monitoring 01 ### The tools you already use Nothing gets replaced. Silicon reads what you have today. Billing CRM Product database Spreadsheets Support Marketing Features ## What’s in the platform Eleven tools on one set of metric definitions. A dashboard, an answer in Slack and an agent all pull from the same place, so they agree. Why did churn rise in March? Silicon Two enterprise accounts on legacy pricing churned on Mar 31. Analytics ### AI analyst Ask in plain English, get answers you can verify. net_revenue: type: sum description: After refunds Analytics ### Semantic layer Define every metric once. Use it everywhere. net_revenue Finance active_accounts Product churn_rate Customer success Analytics ### Metrics catalog Browse every metric and how they connect. Net revenue · Q3 $4.21M Analytics ### Dashboards & reports Dashboards, schedules and alerts on governed data. Autopilot · nightly run Fixed 2 broken charts Flagged 5 stale dashboards Suggested 3 new charts Agents Beta ### Autopilot Fixes broken charts and flags stale ones. # from Claude or Cursor silicon.query( metric="net_revenue", by="month") Agents ### Silicon MCP server ↗ Query your metrics from Claude or Cursor. analytics/dbt #482 Breaks 3 dashboards, 12 metrics Fix PR #483 drafted Reliability ### Schema protection Catch breaking changes at the pull request. v14 Added ARR by segment v13 Churn to monthly v12 Fixed EMEA filter Reliability ### Dashboards as code Version, review and ship BI like software. analytics/dbt #219 Add activation_rate + metrics: + activation_rate: + type: ratio Reliability ### Write back to dbt ↗ Build a metric in Silicon and open a pull request to dbt. Q4 forecast $5.02M Win rate Build ### Data apps From prompt to production app in minutes. Your product · Customer portal Build ### Embedded analytics Governed analytics inside your product. See all of it in a 30-minute demo Book a demo → Product tour coming soon ## By role - Data engineers → - Analytics engineers → - Data analysts → - Growth & product → - Business teams → Integrations ## Runs on the stack you already have Nine warehouses, dbt, four git hosts, Slack, Microsoft Teams and Google Sheets. Silicon reads what you already have, without moving your data. All integrations → FAQ ## Questions about the platform ### Can we use just the platform? + Yes. Team is $1,499 a month and Pro is $2,399 a month, with unlimited users on both. Your data team connects Silicon to your warehouse and dbt project, and you can partner with us later if you need more hands. ### How does the platform work? + Silicon connects to your warehouse and reads your dbt project. Your metrics and models become a governed semantic layer, which powers dashboards, reports and AI answers. Silicon also watches your repository and checks every pull request against what depends on it. ### How does Silicon avoid hallucinated answers? + Silicon answers from your semantic layer, not raw tables. It uses the metrics, dimensions and joins defined for your company, runs every query with the asking user’s permissions, and shows the SQL and definitions behind each answer so it can be checked. ### What happens when a schema changes? + When a pull request changes a model, Silicon compares it with every dashboard, report and metric that depends on it. If anything would break, it comments on the pull request with the affected assets and can draft a fix. ### Does Silicon replace our BI tool? + For most companies on a modern warehouse, yes. Silicon covers dashboards, scheduled reports, alerts, self-serve exploration and AI answers. Some teams run it alongside an existing BI tool while they migrate. ### Does Silicon work with Segment, PostHog or GA4? + Event data from tools like Segment, PostHog or GA4 works once it lands in your warehouse. Silicon reads it from there, next to your billing and CRM data, so product and revenue metrics share one set of definitions. ### Is Silicon secure? + Silicon connects with read-only access, runs queries in your warehouse and never uses your data to train models. AI answers respect row- and column-level permissions. Enterprise adds SSO, SCIM and private deployment in your VPC. We haven’t completed a SOC 2 or HIPAA audit yet; the Security page describes how we handle data today. ## See Silicon on your own stack Book 30 minutes with the founder. Bring your warehouse and dbt setup, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Pricing: Unlimited Users, No Per-Seat Fees | Silicon URL: https://meetsilicon.com/pricing/ Pricing # Pricing for the platform and the data partnership Team is $1,499 a month and Pro is $2,399 a month, with unlimited users on every plan and no per-seat fees. The data partnership is quoted after a short discovery, with the platform included. ## Team $1,499 /month For growing data teams that need governed AI analytics, dashboards and one repo of schema-impact checks. Talk to Sujay - Unlimited users - Unlimited dashboards and visualisations - Natural-language AI analytics - Native dbt integration and governed metrics - Scheduled reports and alerting - GitHub PR bot for 1 repo - Slack and email workflows - Guided onboarding ## Pro Recommended $2,399 /month No per-seat pricing Roll Silicon across your data team with broader schema protection, admin controls and priority support. Talk to Sujay - Everything in Team - Up to 3 projects or warehouses - Schema-impact checks for 3 repos - Slack and Microsoft Teams alerts - Advanced usage analytics and admin controls - API and webhooks - Embedding via iframe and React SDK (add-on) - Priority onboarding and support ## Enterprise Let’s talk Advanced security, deployment flexibility, support and customisation for larger data organisations. Talk to Sujay - Everything in Pro - Cloud, VPC or private deployment - SSO, SAML and SCIM 2.0 - Custom roles and user groups - Custom implementation support - Dedicated account manager - Priority support with defined SLAs - Early access to new enterprise features - Unlimited users on every plan - Your data stays in your warehouse - Your data never trains models Compare plans in detail + Feature | Team | Pro | Enterprise Product features Included seats | Unlimited | Unlimited | Unlimited Data explorer and report builder | | | Native dbt integration | | | Scheduled deliveries | | | Alerting | | | Google Sheets sync | | | SQL runner | | | Private spaces | | | Global search | | | Version history | 30 days | 60 days | Custom Usage analytics and insights | Basic | Advanced | Custom API and webhooks | — | | Smart caching | — | | AI agents | | | Custom Embedding (add-on) | Pay as you go | First 1,000 loads free, then $0.05/load | Usage-based Sources and integrations Warehouses | All 9 supported | All 9 supported | All 9 supported Git sync | GitHub | GitHub, GitLab, Azure DevOps, Bitbucket | GitHub, GitLab, Azure DevOps, Bitbucket Slack and Teams | Slack | Slack and Microsoft Teams | Slack and Microsoft Teams Schema-impact repos | 1 | 3 | Custom CSV export | 1M cells | 3M cells | Custom Support and implementation Support | Email | Private Slack channel, live chat | Private Slack channel, premium support Response SLA | 2 days | 1 day | 8 hours Data Protection Agreement | — | | Migration support sessions | — | | Dedicated account manager | — | — | Security and admin Warehouse SSH tunnel | | | User groups | — | | Custom role builder | — | — | Deployment region | US | US and EU | Custom Private deployment / VPC | — | — | SCIM 2.0 provisioning | — | — | Embedding, priced by use + Embedded analytics is an add-on. On Pro, the first 1,000 embed loads each month are free, then $0.05 per load. Enterprise uses usage-based pricing. About embedded analytics Embed loads per month 5,000 Estimated add-on cost $200 /mo Pro plan add-on. Estimate only; your invoice reflects actual loads. No data team yet ## Or partner with us to build it Data partnership Partner with us to build and maintain your data foundation We build your data foundation, define your key metrics with the teams that own them and maintain it as the company grows, on Silicon. Quoted for you after discovery · platform included See the service → Compared with building a team ### What the first year looks like either way US figures. Which route fits depends on how many data sources you have and how much help you need, so we’ll tell you honestly on the first call. | Hire one data engineer | Silicon data partnership First-year cost | $120,000–227,000 for one senior analytics engineer plus warehouse, ingestion and BI tools | Quoted after a short discovery, based on your data sources and how much help you need Time to a working data foundation | 6–10 weeks to hire, then 2–4 months before pipelines are reliable: 5–7 months in total | Work starts after discovery, with no hiring process What you get | One person’s time, plus the tools you buy and set up | Data engineering hours, the Silicon platform with unlimited users, and ongoing maintenance When your own team arrives | Your hire starts from whatever exists | Your engineers build on the same foundation, with Silicon’s CLI, YAML metrics and dbt integration Sources: hiring cost from Valiotti, data team cost guide; time to hire and ramp-up from KORE1, 2026. FAQ ## Questions about pricing? ### How much does Silicon cost? + Team is $1,499 a month and Pro is $2,399 a month, with unlimited users on both. Enterprise pricing is tailored to your scale and requirements. ### How is embedded analytics billed? + On Pro, embedding is an add-on: the first 1,000 embed loads each month are free, then $0.05 per load. Enterprise uses usage-based pricing. ### Can Silicon build and maintain our data for us? + Yes. Through our data partnership, we build and maintain your data foundation on Silicon as the company grows. It’s quoted after a short discovery and always includes the platform. See the data partnership page. ### Is Silicon really unlimited users? + Yes. Every plan includes unlimited users. There is no per-seat pricing. ### Where is my data stored? + Your data stays in your warehouse. Silicon runs queries there and caches results briefly to keep dashboards fast. Hosted workspaces run in the US, with an EU region on Pro. Enterprise can deploy in your own VPC. ### Do I need dbt to use Silicon? + No. Silicon works natively with dbt, and Silicon YAML lets you define metrics without dbt. ### Is Silicon SOC 2 certified? + Not yet. We have not completed a SOC 2 or HIPAA audit. Our Security page describes how we handle data today, and we’re happy to complete your security questionnaire. ### Can I change plans later? + Yes. You can move between Team and Pro at any time. ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # AI Data Analyst with Governed Answers | Silicon URL: https://meetsilicon.com/product/ai-analyst/ Questions answered this week 1,284 Example data New question in #growth Just now Why did activation drop for self-serve signups last week? Answered from the activation_rate metric. Analytics / AI analyst # Answers from your data, with the work shown Anyone in the company can ask a question in plain English, in Slack or in Silicon, and get a chart with the SQL and the metric definition behind it. Answers use the same metric definitions as your dashboards. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## A fast answer is worthless if it’s wrong Generic AI tools read raw tables and guess. The answer sounds confident, and nobody can tell where the number came from. - 01 Generic AI guesses what a column means and returns a confident, wrong number. - 02 Every team gets a different answer to the same question. - 03 Analysts lose their week to ad-hoc requests in Slack. How it works ## Ask, check the answer and share it Ask Verify Share 01 AI analyst ### Ask where you already work Drop a question in any Slack channel or in the Silicon app. The answer arrives in the thread with a chart, ready to share. Illustrative example Why did delivery delays spike last week? Ask Traced from fct_deliveries and dim_drivers: Delays rose 318% between Nov 8 and Nov 10 Heavy rainfall took 62% of drivers offline Order volume held steady at ~14,200 a day Back within SLA (2.0 hrs) by Nov 11 Features ## What it reads, what it returns, and where it works. ### What it reads Only the context your data team has defined and approved. - Governed metrics - dbt models and descriptions - Verified answers - User permissions ### What it returns Everything someone needs to trust and reuse the answer. - Charts - SQL - Explanations - Suggested follow-ups ### Where it works Where your team already asks questions. - Slack - Microsoft Teams (Pro) - The Silicon app - MCP and API ## How does Silicon avoid hallucinated answers? Silicon answers from your semantic layer, not raw tables. It uses the metrics, dimensions and joins your data team defined, runs every query with the asking user’s permissions, and shows the SQL and definitions behind each answer so it can be checked. FAQ ## Questions, answered ### Which questions can the AI analyst answer? + Anything your semantic layer can answer: metrics broken down by the dimensions you have defined, trends over time, comparisons and root-cause questions. If a question needs a metric that does not exist yet, it says so instead of guessing. ### Does it work in Slack and Microsoft Teams? + Yes. Slack is available on every plan. Microsoft Teams alerts and answers are included on Pro and Enterprise. ### Can it show me data I’m not allowed to see? + No. The AI analyst runs every query as the person asking, so row- and column-level permissions apply exactly as they do in dashboards. Admins can also limit which models each agent can use, or run an agent in metadata-only mode. ### Is my data used to train AI models? + No. Your data is never used to train models. ### Which warehouses does it support? + BigQuery, Snowflake, Redshift, Databricks, Postgres, Trino, ClickHouse, Athena and DuckDB. ## Related - Semantic layer → - Dashboards & reports → - What is a semantic layer? → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Autopilot: An Agent That Keeps Your BI Project Healthy | Silicon URL: https://meetsilicon.com/product/autopilot/ Autopilot · project settings 1 schedule: daily 2 permissions: 3 read_content: on 4 create_content: on 5 modify_existing_content: on 6 slack_updates: "#data-team" Agents / Autopilot # An agent that keeps your project healthy Autopilot runs on a schedule you set. Each run it fixes broken charts it can repair safely, flags stale and slow content, and drafts charts for questions people keep asking, with every action logged and explained. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## BI projects decay as the company grows Charts break when fields change, dashboards nobody opens pile up, and the same question gets asked every week because nobody saved the answer. - 01 A renamed field quietly breaks charts nobody is watching. - 02 Old dashboards bury the ones people actually use. - 03 Slow queries make dashboards feel unreliable. How it works ## Fix, flag and suggest Fix Flag Suggest 01 Autopilot ### Broken charts repaired between runs Autopilot finds charts that reference fields that no longer exist, removes the invalid references when it safely can, and flags the rest for review. Autopilot activity · last run 1 # 04:00 daily run 2 auto-fixed Weekly signups by channel 3 auto-fixed Revenue by plan 4 flagged slow Customer health (4.1s) 5 flagged stale Q1 launch dashboard 6 suggested Activation by cohort Features ## What it does, and the rules it follows ### Each run A fixed checklist, on the schedule you choose. - Broken content - Stale content - Slow queries - Chart suggestions ### Safety rules Conservative by default. - Soft-delete only - Skips content under 30 days - Reverses its own mistakes ### You stay in control Every action is visible and reversible. - Activity log with reasons - Runs with its owner’s permissions - Slack summaries FAQ ## Questions, answered ### Which plans include Autopilot? + Autopilot is in beta on the Pro and Enterprise plans. ### Can Autopilot delete our work? + It only soft-deletes, so admins can restore anything from the trash. It never touches content less than 30 days old, never removes the only chart on a dashboard, and prefers flagging over deleting when in doubt. ### Can it run without changing anything? + Yes. Turn off its create and modify permissions and it runs read-only, recording flags and insights for your team to act on. ### Whose permissions does it use? + It runs as the person who set it up, so every action uses that user’s permissions and is attributed to them. Only developers and admins can view the activity log. ## Related - AI analyst → - Schema protection → - Autopilot docs → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Dashboards, Scheduled Reports and Alerts | Silicon URL: https://meetsilicon.com/product/dashboards/ Home Ask AI Dashboards Charts Metrics Apps Schema checks All schema checks passing Dashboards / Revenue overview Last 12 months Share Net revenue · Q3 $4.21M +14.2% vs Q2 Active accounts 1,982 +6.1% vs Q2 Net revenue retention 112% +3 pts Churn rate 1.8% +0.2 pts Net revenue by month Revenue by segment Enterprise Mid-market Self-serve New accounts by week Ask about this dashboard Why did churn tick up in September? Two mid-market accounts on legacy pricing churned on Sep 30. Excluding them, churn held at 1.6%. Analytics / Dashboards & reports # Dashboards and reports built on your defined metrics Build a dashboard by asking a question or in the editor, then send it to Slack or email on a schedule. Alerts fire when a metric moves outside its usual range, and every chart uses the same definitions as your AI answers. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## Trust drops as the number of dashboards grows Most teams have more dashboards than anyone can maintain, and nobody is sure which one is right. - 01 Nobody knows which of five revenue dashboards is the real one. - 02 Reports go stale the moment someone changes a model. - 03 Alerts fire on every tiny fluctuation until people mute them. How it works ## Build, share and stay current Build Share Alert 01 Dashboards & reports ### Build by asking or clicking Charts, filters and drill-downs on governed metrics. Ask for a chart in plain English or build it in the explorer. Illustrative example Accounts payable $4.2M in invoices processed in Q3, paid 2.1 days faster than Q2. Breakdown Vendor spend Auto-coded invoices Invoice status 91% Paid 9% Pending Time to payment 1.8d Days to pay, Q1 to Q3 Features ## Everything you expect from your BI tool. ### Explore and build For analysts and business users alike. - Charts and dashboards - Filters and drill-down - SQL runner - Global search ### Share and deliver Get numbers to people where they work. - Scheduled deliveries - Alerting - Google Sheets sync - CSV export ### Organise and govern Keep content tidy as it grows. - Private spaces - Version history - User groups FAQ ## Questions, answered ### Can we migrate from Tableau or Looker? + Yes. Pro includes priority onboarding, and Enterprise includes custom implementation support for migrations. ### How often do scheduled reports refresh? + Every delivery runs fresh queries against your warehouse at send time. ### Can I export data? + Yes. CSV exports are included, up to 1M cells on Team and 3M cells on Pro. ### Is there a per-seat price? + No. Every plan includes unlimited users. ## Related - AI analyst → - Dashboards as code → - Metric library → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Dashboards as Code: Version-Controlled BI | Silicon URL: https://meetsilicon.com/product/dashboards-as-code/ silicon/dashboards/revenue-overview.yml 1 name: Revenue overview 2 slug: revenue-overview 3 tiles: 4 - type: saved_chart 5 properties: { chartSlug: net-revenue-by-month } 6 - type: saved_chart 7 properties: { chartSlug: arr-by-segment } Reliability / Dashboards as code # Dashboards as code, reviewed like software Every dashboard and chart lives as YAML next to your dbt models. Your team commits, branches and reviews changes in pull requests, and tests run before anything reaches production. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## Click-built dashboards have no history When BI lives only in a UI, changes skip review, nobody knows who changed what, and a broken dashboard is fixed by hand. - 01 Changes go live without anyone reviewing them. - 02 There is no record of who changed a chart, or why. - 03 Rolling back a broken dashboard is a manual rebuild. How it works ## Version, test and roll back your dashboards Version Test Roll back 01 Dashboards as code ### Version-controlled YAML Dashboards and charts are files in your repository. Branch them, review them and merge them like any other change. silicon/charts/arr-by-segment.yml 1 name: ARR by segment 2 tableName: fct_subscriptions 3 metricQuery: 4 metrics: [fct_subscriptions_arr] 5 dimensions: [fct_subscriptions_segment] 6 chartConfig: { type: cartesian } Features ## Software workflows for analytics. ### In your repository BI that lives next to the models it depends on. - Dashboards as YAML - Charts as YAML - Pull request review ### Before merge Problems caught before anyone sees them. - Automated validation - Preview environments - Schema mismatch checks ### After merge Confidence that you can always go back. - Version history (30 days Team, 60 days Pro) - Audit trail - One-click restore FAQ ## Questions, answered ### Which git hosts are supported? + GitHub on every plan, and GitLab, Azure DevOps and Bitbucket on Pro and Enterprise. ### Can people who don’t write code still edit dashboards? + Yes. Dashboards can be edited in the Silicon UI too. Teams that want review on every change can manage them as code. ### How long is version history kept? + 30 days on Team, 60 days on Pro, and a custom period on Enterprise. ## Related - Schema protection → - Semantic layer → - dbt is where data work becomes software work → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Build Data Apps with AI on Governed Data | Silicon URL: https://meetsilicon.com/product/data-apps/ Home Ask AI Dashboards Charts Metrics Apps Schema checks All schema checks passing Apps / Q4 revenue forecast Last 12 months Share Build a Q4 revenue forecast by region with a scenario slider for win rate. App ready Q4 revenue forecast · all regions Forecast $5.02M +19% QoQ Best case $5.48M Win rate 31% Worst case $4.61M Win rate 22% Scenario Win rate 27% · from win_rate By region North America $2.31M Europe $1.44M India $0.74M APAC $0.53M Build / Data apps # Data apps built from a description Describe the tool you need, such as a forecast with adjustable assumptions, and Silicon builds it on your metric definitions. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## Every custom tool used to be a sprint Forecasts, planning models and recurring decks end up in spreadsheets that drift from the real numbers. - 01 Forecasts live in spreadsheets nobody trusts. - 02 Weekly decks are rebuilt by hand every Monday. - 03 Custom tools wait in the engineering queue for weeks. How it works ## Describe the app you need and Silicon builds it Forecast Present Automate 01 Data apps ### Forecasting and scenario planning Build live forecasting tools with adjustable assumptions, backed by governed metrics instead of exported spreadsheets. Illustrative example Q4 revenue forecast At 4% monthly growth, Q4 revenue lands at $5.1M (range $4.8M–$5.4M). Breakdown Base scenario Upside scenario Plan attainment 96% On track 4% Gap Forecast vs actual $5.1M Monthly revenue with forecast band Features ## What teams build with it. ### Planning Interactive tools with adjustable assumptions. - Forecasts - Scenario planners - Goal trackers ### Reporting Recurring work, done automatically. - Live slide decks - Ops summaries - Finance reviews ### Governance Apps inherit everything your data team set up. - Governed metrics - User permissions - Version history FAQ ## Questions, answered ### Who can build data apps? + Anyone who can describe what they need. Apps are built on the same governed metrics and permissions as the rest of Silicon. ### Do apps follow the same permissions? + Yes. An app only shows each viewer the data they are allowed to see. ### Can apps be embedded? + Yes, through the embedding add-on with an iframe or the React SDK. ## Related - Embedded analytics → - Metric library → - From prompt to production analytics → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Embedded Analytics with Row-Level Security | Silicon URL: https://meetsilicon.com/product/embedded-analytics/ Home Ask AI Dashboards Charts Metrics Apps Schema checks All schema checks passing Dashboards / Embedded preview Last 12 months Share Acme Logistics · Customer portal Signed in as ops@northwind.com Shipments this month 12,480 +8.4% On-time delivery 96.2% +1.1 pts Avg. transit time 2.4 days −0.3 days Shipments by week (your account only) Ask about your shipments Which lanes were late last week? Mumbai → Pune ran 6% late on Tuesday after a weather delay. All other lanes were on time. Build / Embedded analytics # Analytics inside your own product Embed dashboards and AI answers in your product with an iframe or the React SDK. Row-level permissions limit each customer to their own data. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## Customer-facing analytics shouldn’t be a side project Building charts into your product usually means a second analytics stack, duplicated metrics and hand-written access rules. - 01 Engineering builds and maintains a second analytics stack. - 02 Metrics in the product drift from internal numbers. - 03 Access rules are hand-written and easy to get wrong. How it works ## Embedded, secured and in your brand Embed Secure Brand 01 Embedded analytics ### iframe or React SDK Drop in a signed iframe for the fastest start, or use the React SDK for full control over layout and interactions. app/analytics/page.tsx 1 import Silicon from '@silicon/sdk' 2 3 Features ## Built for product teams shipping to customers. ### Embed options Pick the depth of integration you need. - Signed iframe - React SDK - Embedded AI answers ### Security Safe to point at your own customers. - Row-level security - Customer-level permissions - Signed embed tokens ### Experience Feels native to your product. - White-labelling - Interactive dashboards - Same governed metrics Plans ## Embedding pricing | Team | Pro | Enterprise Embed loads | Add-on | First 1,000 free, then $0.05 per load | Usage-based pricing FAQ ## Questions, answered ### How is access controlled? + Each embed is signed with a token that carries the customer’s identity and attributes. Row-level security applies to every query. ### Can I embed AI answers? + Yes. Customers can ask questions inside your product and get answers scoped to their own data. ### How is embedding billed? + As an add-on on Pro: the first 1,000 loads are free, then $0.05 per load. Enterprise uses usage-based pricing. ## Related - Data apps → - Security → - Pricing → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Metrics Catalog and Metric Trees | Silicon URL: https://meetsilicon.com/product/metrics-catalog/ models/orders.yml 1 models: 2 - name: orders 3 columns: 4 - name: order_amount 5 meta: 6 metrics: 7 total_revenue: 8 type: sum 9 drivers: [total_order_amount] Analytics / Metrics catalog # Every metric in one place, with how they connect The metrics catalog lists every metric in your semantic layer, with its description, category and how often it is used. Anyone can explore a metric over time, and metric trees show which metrics drive which. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## Nobody can find the metric that already exists When metrics are hidden inside dashboards and YAML files, people rebuild them, name them differently and end up with two numbers for the same thing. - 01 The same metric gets rebuilt under a new name. - 02 Business users don’t know which metrics they can trust. - 03 Nobody can see which metrics move the one that matters. How it works ## Browse, explore and connect your metrics Browse Explore Connect 01 Metrics catalog ### Search every metric and see where it’s used Find metrics by name or description, filter by category, and see how popular each one is and which charts use it. Illustrative example Metrics catalog net_revenue is used in 14 charts across 5 dashboards. Breakdown Finance Growth Metrics by category 24 Finance 17 Growth Most used this month Top net_revenue, active_accounts, churn_rate Features ## What’s in the catalog and how you curate it ### Browse Find the metric before building a new one. - Search - Categories - Popularity - Descriptions ### Explore Look at any metric without building a chart. - Over time - Segment by - Compare to last year ### Curate Decide what business users see, in YAML. - Visibility rules - Default time settings - Shared metric trees FAQ ## Questions, answered ### Where does the catalog get its metrics? + From your semantic layer. Every metric you define in dbt or Silicon YAML appears in the catalog, filtered by each person’s permissions and user attributes. ### Can we hide metrics from business users? + Yes. Set a default visibility in silicon.config.yml and override it for any model or metric in its YAML. ### What is a metric tree? + A diagram of how metrics relate. Add drivers to a metric in YAML and the canvas connects them automatically, or draw connections by hand. ### Is the metrics catalog generally available? + The catalog and canvas are in beta, so some details may change. Saved trees are experimental and turned on per organization. ## Related - Semantic layer → - AI analyst → - Metric library → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Schema Change Protection for dbt and BI | Silicon URL: https://meetsilicon.com/product/schema-protection/ Downstream assets at risk 3 PR #482 Breaking change detected 2 min ago customer_id renamed to account_id in fct_orders. 3 dashboards and 12 metrics depend on it. Fix PR drafted. Reliability / Schema protection # Catch breaking changes before they reach a dashboard Silicon checks every pull request against the dashboards and metrics that depend on the models it changes. When a change would break one of them, it comments with the exact model and column affected and drafts a fix. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## 60% of data teams shipped a breaking change in the last six months Column renames, model changes and failed jobs silently break dashboards and metrics. The first person to notice is usually a stakeholder. (Monte Carlo, State of Data Quality, 2024) - 01 A change ships silently and the CEO finds out first. - 02 Nobody can say which dashboards a model change will touch. - 03 The fix takes days because the cause is buried in a commit. How it works ## Check each change, trace the cause and fix it Check Trace Fix 01 Schema protection ### Impact checked on every pull request Silicon compares the proposed schema with your live warehouse and every downstream asset, then comments on the PR with exactly what would break. analytics/dbt #482 Rename customer_id to account_id in fct_orders silicon bot commented just now This change breaks 3 dashboards and 12 metrics. - Revenue overview Dashboard - Customer health Dashboard - Weekly finance summary Report - net_revenue (+11 more) Metric Open fix PR View lineage Features ## What it detects, what it protects, and where it alerts. ### What it detects The changes that silently break analytics. - Column renames - Dropped columns - Type changes - Model changes - Failed jobs ### What it protects Everything downstream of your models. - Dashboards - Reports - Metrics - AI answers - Data apps ### Where it alerts Where your team will see it first. - GitHub pull requests - Slack - Microsoft Teams (Pro) Plans ## Included on every plan | Team | Pro | Enterprise Repositories | 1 | 3 | Custom Git hosts | GitHub | GitHub, GitLab, Azure DevOps, Bitbucket | GitHub, GitLab, Azure DevOps, Bitbucket Alerts | Slack | Slack and Microsoft Teams | Slack and Microsoft Teams ## What happens when a schema changes? When a pull request changes a model, Silicon compares the new schema with your live warehouse and every dashboard, report and metric that depends on it. If anything would break, it comments on the pull request with the affected assets and can draft a fix pull request. FAQ ## Questions, answered ### Which git hosts are supported? + GitHub on every plan. GitLab, Azure DevOps and Bitbucket on Pro and Enterprise. ### Does schema protection need dbt? + Schema protection reads the models and dependencies in your connected repository, whether they are defined in dbt or in Silicon YAML. ### Can Silicon open fixes automatically? + Silicon drafts the fix as a pull request. Nothing merges without your team’s review. ### How many repositories can I protect? + One on Team, three on Pro, and a custom number on Enterprise. ## Related - Dashboards as code → - What is schema drift? → - Prevent broken dashboards → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Semantic Layer and Metrics Catalog for dbt | Silicon URL: https://meetsilicon.com/product/semantic-layer/ models/finance/fct_revenue.yml 1 models: 2 - name: fct_revenue 3 columns: 4 - name: net_amount 5 meta: 6 metrics: 7 net_revenue: 8 type: sum 9 description: Revenue after refunds Analytics / Semantic layer # Define each metric once for every dashboard and answer Your metrics, dimensions, joins and business rules live in one place next to your dbt models. Dashboards and AI answers read from those definitions, so finance and product report revenue from the same definition. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## Three teams reporting three different revenue numbers When definitions live in people’s heads and spreadsheets, every dashboard and every AI tool invents its own version of the truth. - 01 Definitions live in people’s heads, spreadsheets and old dashboards. - 02 AI tools guess what a column means from raw tables. - 03 A metric changes and nobody downstream finds out. How it works ## One definition behind every answer and dashboard Define Discover Reuse 01 Semantic layer ### Define metrics next to your dbt models Metrics, dimensions and joins live in YAML in your repository, reviewed and versioned like the rest of your code. models/product/fct_signups.yml 1 models: 2 - name: fct_signups 3 meta: 4 metrics: 5 activation_rate: 6 type: number 7 sql: ${activated} / NULLIF(${signups}, 0) 8 format: percent Features ## What you define, where it’s used, and how it stays governed. ### What you define The business context your data team already knows. - Metrics - Dimensions - Joins - Owners - Business rules - Descriptions ### Where it’s used Every surface reads from the same definitions. - Dashboards - AI answers - Scheduled reports - Embeds and API ### How it stays governed Changes are reviewed before they reach anyone. - Verified metrics - Version history - Pull request review ## What is a semantic layer? A semantic layer is a shared set of business definitions (metrics, dimensions and the joins between tables) that sits between your warehouse and every tool that reads from it. Defining them once means every dashboard and AI answer calculates a metric the same way. FAQ ## Questions, answered ### Do I need dbt to use Silicon? + No. Silicon works natively with dbt projects, reading your models, metrics and descriptions directly. If you don’t use dbt, Silicon YAML lets you define the semantic layer in standalone YAML files that point at tables in your warehouse. ### How is a semantic layer different from a metrics layer? + A metrics layer defines calculations. A semantic layer also defines the dimensions, joins and relationships around them, so tools can explore data safely, not just compute numbers. ### Who can change a definition? + Definitions live in your repository, so changes go through the same pull request review as any other code. ### Can business users see where a number comes from? + Yes. The metrics catalog shows each metric’s description and where it is used. ## Related - AI analyst → - Dashboards as code → - Metric library → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Product Roadmap: What Silicon Is Building Next | Silicon URL: https://meetsilicon.com/roadmap/ 01 Backlog Planned next - dbt Fusion integration ↗ - In-app semantic layer editor - Customisable homepage - Build dashboards in embed mode - Custom SQL filters 02 Building Actively working on - Embed AI agents in your product - Agents as users in Silicon - Shared context across the platform - Self-improving semantic layer agent - Agents as code 03 Beta Features in early access - Pivot tables 2.0 - Metric trees ↗ - Autopilot ↗ - Connect external MCP servers to AI agents ↗ - Organisation themes for data apps ↗ - Embed data apps ↗ 04 Completed Shipped features - Custom data apps ↗ - AI agents ↗ - AI agents in Slack ↗ - Self-improving semantic layer ↗ - Silicon MCP server ↗ - Spreadsheet calculations ↗ - Verified charts and dashboards ↗ - Pre-aggregates and caching ↗ - Metrics catalog ↗ - Period-over-period comparisons ↗ - Nested space permissions ↗ - Custom role builder ↗ - Dashboards 2.0, with tabs ↗ - Improved scheduled deliveries ↗ - Alerts ↗ - One-to-many joins ↗ - Treemap charts ↗ - Improved map charts ↗ - Custom charts ↗ - Dashboard filters on SQL runner charts ↗ - Write back to dbt ↗ - Dashboards as code ↗ - Preview projects ↗ - Content validation ↗ - Version history ↗ - Embedding with the React SDK ↗ - Google Sheets sync ↗ - User attributes ↗ - Groups ↗ - SCIM provisioning ↗ ## Request a feature Need something that isn’t on the board? Tell us what your team needs and we’ll look at where it fits. Request ## Read the docs Every shipped feature is documented, with setup steps and examples. Open the docs --- # Security and Data Handling | Silicon URL: https://meetsilicon.com/security/ Security # Your data stays in your warehouse Silicon queries your warehouse with read-only access, applies every permission to every answer, and never uses your data to train AI models. This page describes how Silicon handles data today. ## Data stays in your warehouse Silicon connects with read-only credentials and runs queries in your warehouse. Results are cached briefly to keep dashboards fast. ## Your data never trains models Customer data is never used to train AI models. Answers are built from your semantic layer, not memorised. ## AI inherits your permissions The AI analyst runs every query as the person asking, so row- and column-level rules apply to every answer. ## Deploy your way Hosted in the US, with an EU region on Pro. Enterprise can run Silicon privately in your own VPC. ## How Silicon accesses data Silicon is a query layer. It does not copy your warehouse. Your warehouse Data stays here Silicon Read-only queries, permissions applied Your team Sees only what they’re allowed Read-only connections Connect with a warehouse user or service account that only has read access to the schemas Silicon needs. Queries run in your warehouse Every chart, dashboard and AI answer becomes SQL that runs in your warehouse, under the credentials you provide. SSH tunnels Connect to warehouses in private networks through an SSH tunnel. What Silicon stores Metadata such as your project structure, metric definitions, and chart and dashboard configuration. Query results are cached for up to a day to keep dashboards fast, and query history is kept for 32 days. ## AI and your data The AI analyst works through your semantic layer and inside the same permissions as everything else. No training on your data Customer data is never used to train AI models. Model providers Hosted Silicon uses OpenAI and Anthropic models. Self-hosted deployments can also use Azure OpenAI or Amazon Bedrock, keeping requests inside your cloud. Answers through the semantic layer The AI analyst queries governed metrics and models, not arbitrary tables. Raw SQL is only available to people whose role allows the SQL runner. Permissions are inherited Every AI query runs as the person asking, so their row- and column-level access applies. The AI never widens what someone can see. Scoped agents Admins can limit which models an AI agent can use, or run it in metadata-only mode so it answers from definitions without querying data. AI retention Conversation threads are kept so people can return to them; Enterprise can set a retention period. Logs of AI tool calls made through MCP are kept for 90 days. ## Access controls Roles and permissions Control who can view, explore, build and administer. Pro adds user groups; Enterprise adds a custom role builder. Row- and column-level security Restrict rows and columns using user attributes, applied to dashboards, exports, embeds and AI answers alike. Private spaces Keep work-in-progress dashboards and charts visible only to the people who need them. SSO, SAML and SCIM 2.0 Single sign-on and automated user provisioning on Enterprise. Support access Silicon staff cannot see inside your workspace by default. Support access needs separate consent from your team, lasts 15 minutes per session, and every session is logged. ## Deployment options Plan | Where it runs Team | Hosted by Silicon in the US Pro | Hosted by Silicon in the US or EU Enterprise | Hosted, or private deployment in your own cloud or VPC ## Compliance status Last updated 4 October 2026. SOC 2 Not yet audited. We will publish the report status here once an audit is under way. HIPAA Not yet available. We do not currently sign Business Associate Agreements. GDPR An EU hosting region is available on Pro, and we sign Data Protection Agreements on request. Security questionnaires We’re happy to complete your vendor security questionnaire. Email help@meetsilicon.com. ## Report a vulnerability If you believe you’ve found a security issue in Silicon, email help@meetsilicon.com with “Security report” in the subject line and enough detail for us to reproduce it. Please give us a reasonable chance to fix it before sharing it publicly. We’ll acknowledge your report and keep you updated as we work on it. FAQ ## Security questions ### Where is my data stored? + Your data stays in your warehouse. Silicon stores metadata such as your project structure and chart definitions, caches query results for up to a day to keep dashboards fast, and keeps query history for 32 days. Hosted workspaces run in the US, with an EU region on Pro. ### Can we self-host Silicon? + Yes, on Enterprise. Silicon can run in your own cloud or VPC, so data and AI requests stay in your infrastructure, and product telemetry can be turned off. ### Which AI providers does Silicon use? + Hosted Silicon uses OpenAI and Anthropic models. Self-hosted deployments can also use Azure OpenAI or Amazon Bedrock. Customer data is never used to train models. ### Is Silicon SOC 2 or HIPAA compliant? + Not yet. We have not completed a SOC 2 or HIPAA audit, and we won’t claim either until we have. We’ll publish the status here as it changes. ### Do you sign DPAs and complete security questionnaires? + Yes. Email help@meetsilicon.com and we’ll send our Data Protection Agreement or complete your questionnaire. ## Questions from your security team? Talk it through with the founder, or email help@meetsilicon.com for our DPA and questionnaire answers. Talk to Sujay 30-minute call with the founder --- # Data Partnership: Data Team as a Service | Silicon URL: https://meetsilicon.com/services/ Data partnership # Your data foundation, built and maintained by us Silicon partners with high-growth startups to build and maintain their data foundation. We connect the apps your company runs on, define your key metrics with the people who run each team, and build dashboards and the AI analyst on those definitions. Our AI analytics platform is part of every partnership, and anyone can see the data and definitions behind each answer. Talk to Sujay See the first ten weeks About ten weeks between the first call and the launch of your dashboards and AI analyst Platform included the Silicon platform comes with every partnership Monthly or annual priced after discovery, with a written plan and quote Why it exists ## Reliable data takes five to seven months with a single hire Hiring takes months, and the first hire still needs tools and time to learn your business. We build and maintain the foundation for you, with checks on each change to your data. 5–7 months from deciding to hire one data engineer to reliable data, including two to four months of ramp-up. Source: KORE1, Time to fill a data engineer role6–10 weeks to hire one data engineer through an internal search, before any of that ramp-up begins. Source: KORE1, Time to fill a data engineer role$120–227k a year for one senior analytics engineer and the tools they need, at a company of about $5M revenue. Source: Valiotti, Data team cost guide Who it’s for ## For high-growth startups that need their data built and maintained 01 ### High-growth startups without a data team Your numbers live in several apps that disagree, and someone still pulls reports together by hand. 02 ### Companies with one data person Your one data person answers requests from every team, and the foundation work keeps slipping. 03 ### A moment when your key numbers get checked Raising the next round, a new CFO or board, a pricing change, or an AI rollout. Probably not a fit if ### You already have a data team that maintains your data The Silicon platform on its own is the better fit, and your team stays in charge. See the platform → ### You want people placed inside your team We work as a partner with our own process and one point of contact for your team. ### Your data still fits in one spreadsheet If one person can keep track of every number by hand, it’s too early. We’ll tell you so on the first call. What you get ## What your company has after the first ten weeks Everything below runs on the Silicon platform. We maintain all of it after launch and add to it as you add teams and tools. 01 ### All your data in one place Every app the company runs on, connected and kept in sync by us. - Your product, billing, CRM, marketing and support tools connected and syncing - One secure home for your data, sized for the company, with access and cost controls - Checks each time new data comes in, with an alert to us when one fails 02 ### Key metrics, defined once Each key metric written down and signed off by the person responsible for it. - The 10–20 key metrics your leadership team relies on, defined with each team - A plain-language definition of every metric that anyone can read - The same definitions behind every dashboard, report and AI answer 03 ### Answers your team can use Dashboards and reports for each team, and the AI analyst for questions the dashboards don’t cover. - A dashboard for each team, built around the metrics it owns - Reports sent to Slack or email on the schedule each team needs - The AI analyst in Slack, showing the data and definition behind each answer How it works ## A typical first ten weeks Discovery comes first and ends with a written plan, so you know the scope and the price before the build starts. Companies with more tools take a little longer. W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 Discovery Weeks 1–2 We meet the people who run each team, map where your data lives and list the questions leadership needs answered. Weeks 1–2 Written plan and quote Connect your tools Weeks 3–5 We connect the apps your company runs on and bring their data into one secure place. Weeks 3–5 Build the foundation Weeks 4–7 We clean and organize the raw data into customers, revenue, product usage and the other things your metrics are built from. Weeks 4–7 Define key metrics Weeks 5–8 We write each metric definition with the person who owns it, and they sign it off before anything is built on it. Weeks 5–8 Metrics signed off Dashboards and AI analyst Weeks 7–10 We build the first dashboards for each team and switch on the AI analyst in Slack. Weeks 7–10 Live for the whole company After launch ## We keep building as the company grows After launch, your team sends requests in a shared Slack channel, we track each one until it’s done, and the platform tells us when a change in your data affects a dashboard. Requests #data-requests Add the new annual plan to revenue Finance Done Weekly signups by channel, sent to Slack Marketing Done Activation by cohort for the board pack Leadership In progress Connect the new support tool Operations Planned - ### New tools and data When the company adopts a new tool or launches a new product, we connect it and add it to your key metrics. - ### Requests from any team Dashboards, reports and one-off analysis, asked for in a shared Slack channel and tracked until they’re done. - ### Changes to key metrics When pricing, plans or targets change, we update the definition with its owner, and every dashboard that uses it reads the new definition. - ### Fixes when something changes The platform checks each change to your data and alerts us when a dashboard or metric is affected. We make the fix. - ### Regular reviews We review your key numbers with your leadership team, flag anything unexpected and plan the next quarter’s work. - ### Advice on what to track When a decision depends on a number you don’t track yet, we help you decide what to measure and how. Who does what ## What we do, and what we need from your team Our side - 01 ### Silicon team Your single point of contact. Discovery, metric definitions, dashboards, AI setup and the regular reviews. - 02 ### Specialists under our contract We bring in specialist data engineers for some of the build. They work to our standards, and you deal only with us. - 03 ### The Silicon platform Checks each change against your dashboards and metrics, alerts us when something breaks, and drafts a fix for us to review. Your side - Introduce us to the people who run each team - Grant access to the tools we connect - Sign off each key metric definition - Ask questions and send requests in Slack - Join the regular review of your numbers Plans and pricing ## Three ways to start, each scoped after discovery Fixed scope · can continue as a partnership ### Foundation build Your data foundation built from scratch, after a short discovery that is credited against the build. - Discovery and a written plan - Your tools connected in one place - Key metrics defined with each team - First dashboards and the AI analyst in Slack Monthly or annual ### Ongoing partnership Your data foundation maintained after launch, with the Silicon platform as part of it. - Silicon platform included - Time for new requests and changes included - Monitoring and fixes when something breaks - Regular reviews of your key numbers Fixed scope · 2–4 weeks ### Projects For a specific moment, such as a fundraise, a board meeting or moving off an old reporting tool. - Scope and price agreed up front - Built on the Silicon platform, which checks each change - Can continue as a partnership - No long-term commitment Every partnership is priced to your sources and how much ongoing help you need. Tell us where your data is today and you’ll get a written scope and quote after discovery. Unlimited users, platform included. Talk to Sujay As your team grows ## Your own data team builds on the same foundation When you hire your own data team, they work on the same foundation and metric definitions, with tools made for engineers. We keep handling as much of the day-to-day data work as you want, and the platform keeps checking each change. How Silicon checks each change → FAQ ## Questions about the data partnership ### Do we need a data team to work with you? + No. The partnership is built for companies without one. We build your data foundation, key metrics and dashboards on the Silicon platform, and maintain them as the company grows. ### What happens when we build our own data team? + Nothing has to be rebuilt. Your new hires start from documented metric definitions and data models, and you decide how much of the work we keep. ### How much does it cost? + It depends on how many tools your company runs on and how much ongoing help you need. After a short discovery you get a written scope and an exact quote. ### Is it monthly or annual? + Both. You can run the partnership month to month or on an annual agreement. The foundation build and projects are fixed scope. ### How is Silicon different from a data analytics consultant? + A data analytics consultant is usually hired for a project and hands the work over when it ends. We build your data foundation and keep maintaining it as the company grows, with the Silicon platform included. The full comparison is on our Silicon vs hiring a data analytics consultant page. ### Who does the work? + We do, and we are your single point of contact. For some of the build we bring in specialist data engineers, who work under our contract and to our standards. ### How much of our time does it take? + Most of it falls in the first weeks, with a kickoff, a few sessions to define your key metrics and sign-off on each one. After launch, your team sends requests in Slack and joins a regular review of the numbers. ### Do we have to use the Silicon platform? + Yes, the data partnership includes it. It holds your metric definitions and alerts us when a change affects them, and it stays with you whatever you decide later. ## Tell us where your data is today Book 30 minutes with the founder. We’ll look at the tools your company runs on and the numbers leadership relies on, then tell you what it would take. Talk to Sujay 30-minute call with the founder --- # Silicon for Analytics Engineers: Semantic Layer and BI as Code URL: https://meetsilicon.com/solutions/analytics-engineers/ Solutions / Analytics engineers # Metrics defined once and reviewed like code Your metrics live next to your dbt models, your dashboards live in git, and every AI answer is built from the definitions you approved. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## Sound familiar? - 01 Revenue has four definitions across four dashboards. - 02 Dashboard changes happen in a UI with no review or history. - 03 AI tools ignore your models and query raw tables. Features ## What changes with Silicon 01 ### Metrics in YAML Define metrics in dbt column meta or Silicon YAML. Every chart, dashboard and AI answer uses them. Semantic layer → 02 ### Dashboards in pull requests Charts and dashboards are versioned files. Change them in a branch, preview, review and merge. Dashboards as code → 03 ### AI that respects your work The AI analyst answers from your semantic layer and shows the SQL and definitions it used. AI analyst → How it works ## A day with Silicon - 10:02 Adds activation_rate as a ratio metric in fct_signups.yml. - 10:15 Updates the growth dashboard in the same branch. - 10:40 Teammate reviews the preview and approves. - 11:05 Growth asks the AI analyst about activation. It uses the new metric. FAQ ## Questions, answered ### How do I define metrics? + In your dbt YAML under a column’s meta.metrics, or in standalone Silicon YAML model files if you don’t use dbt. ### Can I preview dashboard changes before merging? + Yes. Changes made in a branch can be previewed and reviewed before they reach production. ### What happens if someone edits a dashboard in the UI? + Version history keeps every change (30 days on Team, 60 on Pro), so you can see who changed what and restore earlier versions. ## Silicon for other roles - Data engineers → - Data analysts → - Growth & product → - Business teams → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Silicon for Business Teams: Answers in Plain English URL: https://meetsilicon.com/solutions/business-teams/ Solutions / Business teams # Ask in plain English and see where the answer comes from Ask Silicon the way you would ask a colleague on the data team. You get a chart and a short note on how that number is defined. Talk to Sujay 30-minute call with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## Sound familiar? - 01 Waiting days in the data team’s queue for a simple number. - 02 Two dashboards showing different revenue for the same month. - 03 Not knowing whether a number is safe to put in a board deck. Features ## What changes with Silicon 01 ### Answers in Slack Ask in any channel and get the answer in the thread, with a chart you can share. AI analyst → 02 ### One definition per metric Every team works from the same metric definitions, so finance and product report revenue the same way. Semantic layer → 03 ### Reports that arrive on time Get the dashboards you check every week delivered to Slack or email, with alerts when a number moves. Dashboards & reports → How it works ## A day with Silicon - 08:00 Monday pipeline report arrives in email. - 10:20 Asks “Which regions missed target last month?” in Slack. - 10:21 Gets a chart and the definition of “target” used. - 14:00 Adds the chart to the board deck, with its definition noted. FAQ ## Questions, answered ### Do I need to know SQL? + No. Ask in plain English. The SQL is shown for anyone who wants to check it. ### We don’t have a data team. Can we still use Silicon? + Yes. When we partner with you, we build and maintain your data foundation and key metrics on Silicon, so your business teams can start asking questions in weeks. ### Can I see data I shouldn’t? + No. Answers respect the same permissions as your dashboards. ### What if the answer looks wrong? + Every answer shows the metric definitions and filters it used, so you can see exactly what was counted, and flag it to your data team. ## Silicon for other roles - Data engineers → - Analytics engineers → - Data analysts → - Growth & product → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Talk to Sujay 30-minute call with the founder --- # Silicon for Data Analysts: Less Ad-hoc, More Analysis URL: https://meetsilicon.com/solutions/data-analysts/ Solutions / Data analysts # More time for analysis and fewer ad-hoc requests The business asks Silicon first and gets answers built from your metrics. You get your week back for the analysis that needs a person. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## Sound familiar? - 01 Half the week goes to “quick questions” in Slack. - 02 Every answer has to be double-checked against the last one. - 03 Building a custom tool means waiting on engineering. Features ## What changes with Silicon 01 ### Self-serve that holds up Stakeholders ask in Slack and get a chart with the SQL behind it, from metrics you trust. AI analyst → 02 ### Dashboards and schedules Build dashboards on governed metrics and deliver them to Slack, email or Google Sheets on a schedule. Dashboards & reports → 03 ### Data apps from a prompt Describe a tool, like a forecast or a pipeline review, and get a working app on your governed data. Data apps → How it works ## A day with Silicon - 08:30 Weekly revenue report lands in #finance on schedule. - 09:45 Sales asks the AI analyst about pipeline by region. Answered in the thread. - 11:00 Uninterrupted time on the churn analysis. - 15:00 Ships a forecast app for the planning meeting. FAQ ## Questions, answered ### Will the AI analyst replace me? + No. It handles repeat questions from defined metrics. Questions that need judgement, new definitions or deeper analysis still come to you. ### Can I still write SQL? + Yes, if your role has SQL runner permission. Everyone else works from the semantic layer. ### Where can reports be delivered? + Slack and email on every plan, Microsoft Teams on Pro and Enterprise, and Google Sheets. ## Silicon for other roles - Data engineers → - Analytics engineers → - Growth & product → - Business teams → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Silicon for Data Engineers: Schema Protection and Lineage URL: https://meetsilicon.com/solutions/data-engineers/ Solutions / Data engineers # Ship model changes without the 9am fire drill Silicon checks every pull request against the dashboards, metrics and reports that depend on it, so you find out what breaks before your stakeholders do. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## Sound familiar? - 01 A column rename ships and a board dashboard breaks overnight. - 02 Nobody can say which reports a model change will touch. - 03 Broken numbers are traced by hand, commit by commit. Features ## What changes with Silicon 01 ### Impact on every pull request A comment on the PR lists every dashboard, report and metric your change would break. Schema protection → 02 ### Lineage to the column Trace any broken number back through metric and model to the exact column and commit. Schema protection → 03 ### BI in the same review flow Charts and dashboards live as YAML in git, so they change in the same pull request as the model. Dashboards as code → How it works ## A day with Silicon - 09:12 Opens a PR renaming customer_id to account_id in fct_orders. - 09:14 Silicon comments: 3 dashboards and 12 metrics would break. - 09:20 Reviews the drafted fix PR and merges both together. - 09:30 Standup. Nothing broke, so nothing to explain. FAQ ## Questions, answered ### Which git hosts does Silicon support? + GitHub on every plan. GitLab, Azure DevOps and Bitbucket on Pro and Enterprise. ### Do I need to change my dbt project? + No. Silicon reads your existing dbt project, including models, tests, descriptions and metrics defined in column meta. ### Does Silicon store our warehouse data? + No. Queries run in your warehouse. Silicon caches results briefly to keep dashboards fast; see the Security page for retention details. ## Silicon for other roles - Analytics engineers → - Data analysts → - Growth & product → - Business teams → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Silicon for Growth Teams: Retention, Activation, Funnels URL: https://meetsilicon.com/solutions/growth-and-product/ Solutions / Growth & product # Product numbers that match the revenue numbers Ask which signup source retains best or where trials drop off, and get an answer built on the same definitions finance uses. Activation means one thing across every chart. Book a demo 30-minute demo with the founder Works with the stack you already run. dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack dbt Snowflake BigQuery Databricks Redshift Postgres ClickHouse GitHub Slack Problem ## Sound familiar? - 01 Activation is defined three ways across three dashboards. - 02 Product events and Stripe revenue live in separate tools that never agree. - 03 A question about trial drop-off waits a week in the data team’s queue. Features ## What changes with Silicon 01 ### Ask about retention and activation Ask “What is day-30 retention by signup source?” in Slack and get a chart with the definition it used. AI analyst → 02 ### Product metrics tied to revenue Activation, retention and churn are defined once, next to MRR, so a cohort chart and the board deck count the same customers. Semantic layer → 03 ### Funnels on your own data Build a funnel from signup to paid on the events in your warehouse and share it as a dashboard or a weekly report. Dashboards & reports → How it works ## A day with Silicon - 09:00 Weekly activation report arrives in Slack. - 11:15 Asks “Where did September trials drop off?” in #growth. - 11:16 Gets a funnel from signup to first dashboard, with each step defined. - 15:30 Checks whether last week’s onboarding change moved activation. FAQ ## Questions, answered ### Does Silicon connect to Segment, PostHog or GA4? + Not directly. Event data from tools like Segment, PostHog or GA4 works once it lands in your warehouse, and Silicon reads it from there alongside your billing and CRM data. ### Does this replace Amplitude or Mixpanel? + It depends on how you use them. Silicon is built for product metrics that need to match revenue and other company numbers. Session-level tools like Amplitude and Mixpanel are built for exploring raw event streams, and many teams run both. ### Can we build funnels? + Yes. Silicon has a funnel chart that runs on the event tables in your warehouse. ### We don’t have a data team. Can we still use Silicon? + Yes. When we partner with you, we get your product events and billing data into one place and define activation, retention and churn with you. ## Silicon for other roles - Data engineers → - Analytics engineers → - Data analysts → - Business teams → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Product Tour: See Silicon in Two Minutes | Silicon URL: https://meetsilicon.com/tour/ Product tour # See Silicon on a real data stack Five steps, about two minutes, using a sample accounts-payable dataset. No sign-up needed. 1. Ask 2. Verify 3. Publish 4. Schedule 5. Protect Step 1 of 5 ## Ask a question in plain English A finance lead asks why time to payment improved last quarter. Silicon answers in seconds from the governed accounts-payable metrics. Next Illustrative example Why did time to payment improve last quarter? Ask From fct_invoices and dim_vendors (AP semantic model): Average time to payment fell from 3.9 to 1.8 days in Q3 Auto-coding accuracy rose to 91%, cutting manual review 62% of the gain came from the top 20 vendors Invoice volume grew 14.2% over the same period - Ask a question in plain English A finance lead asks why time to payment improved last quarter. Silicon answers in seconds from the governed accounts-payable metrics. - Open the reasoning Every answer shows the metric definition, the tables and the filters it used, so anyone can check where the number came from. - Save it to a dashboard The answer becomes a chart on the accounts-payable dashboard, built on the same governed metrics as everything else. - Deliver it every Monday Schedule the dashboard to Slack or email. It arrives fresh from live data, with no one exporting screenshots. - Catch the change that would break it A teammate opens a pull request renaming vendor_id. Silicon flags the dashboard and the scheduled report, and drafts the fix. --- # Silicon vs Amplitude and Mixpanel: Metrics Tied to Revenue URL: https://meetsilicon.com/vs-amplitude-mixpanel/ Compare / Silicon vs Amplitude and Mixpanel # Silicon vs Amplitude and Mixpanel Amplitude and Mixpanel are product analytics tools. They collect events through their own SDKs and are built for exploring user behaviour, with session replay and experiments alongside. Silicon works on the data already in your warehouse, so activation and retention are defined once, next to MRR and churn, and every team counts the same customers. Book a demo 30-minute demo with the founder ## Choose Silicon if - Product numbers need to match the revenue numbers in the board deck - Your product events already land in a warehouse - You want one definition of activation across product, finance and sales - You want answers in Slack without learning another tool ## Amplitude and Mixpanel may fit better if - You need session replay or built-in A/B testing - You don’t have a warehouse and want an SDK that collects events for you - Your product team explores raw event streams every day Side by side ## Side by side | Silicon | Amplitude and Mixpanel Where the data lives | Your warehouse; queries run there | Mostly in their own cloud, with options to sync data in from a warehouse Revenue and CRM data | Joined in the warehouse with product events | Sent or synced in alongside events Metric definitions | In code (dbt or Silicon YAML), shared by every team | Defined in each tool for product analysis Funnels and retention | Funnel charts on your event tables, retention defined as a metric | Built in, with deep event exploration Session replay and experiments | Not built in | Available Event collection | None; reads what is already in your warehouse | Own SDKs and integrations Based on publicly available information as of October 2026. Products change; check Amplitude and Mixpanel’s documentation for the latest. FAQ ## Questions, answered ### Should we replace Amplitude or Mixpanel with Silicon? + Not always. Many teams keep a product analytics tool for event exploration and use Silicon for metrics that need to match revenue. If your events already land in a warehouse, Silicon can cover funnels and retention too. ### Does Silicon connect to Amplitude or Mixpanel directly? + No. Both can export events to a warehouse, and Silicon reads them from there. ## Other comparisons - Silicon vs Tableau → - Silicon vs Monte Carlo → - Silicon vs ThoughtSpot → - Silicon vs Omni → - Silicon vs Metabase → - Silicon vs hiring a data analytics consultant → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Silicon vs Hiring a Data Analytics Consultant URL: https://meetsilicon.com/vs-data-analytics-consultant/ Compare / Silicon vs hiring a data analytics consultant # Silicon vs hiring a data analytics consultant A data analytics consultant is usually hired for a project. They build a set of reports or dashboards, document the work and hand it over to your team. Silicon partners with high-growth startups to build and maintain their data foundation, and our AI analytics platform runs on that foundation, so the work carries on after the first dashboards ship. Book a demo 30-minute demo with the founder ## Choose Silicon if - You don’t have a data team to hand the work over to - Your key metrics need to stay correct as pricing, plans and tools change - You want one point of contact for building and maintaining your data - You want anyone in the company to ask questions in Slack and see where each answer comes from ## Hiring a data analytics consultant may fit better if - You already have a data team and need extra hands for one defined project - You need specialist work beyond reporting, such as a forecasting or pricing model - You want someone working inside your team, on your own tools and process Side by side ## Side by side | Silicon | Hiring a data analytics consultant How the work is set up | A foundation build, then an ongoing partnership as the company grows | Usually a project with an agreed end date After launch | We maintain your data foundation and key metrics, and handle new requests from any team | Handed over to your team, or a new contract for more work Keeping key metrics correct | We update each definition with the person who owns it | Your team, once the project ends When something in your data changes | The platform flags the dashboards and metrics affected, and we make the fix | Depends on whether the consultant is still under contract Software | The Silicon platform is included, with dashboards and an AI analyst in Slack | Usually built in tools you license separately Pricing | A fixed-scope build, then monthly or annual | Usually hourly, daily or per project Who does the work | We do, with one point of contact. Specialist data engineers work under our contract | The consultant or their firm Consultants work in many different ways. This compares Silicon with the most common setup, a project with a fixed end date. FAQ ## Questions, answered ### Is Silicon a data analytics consultant? + No. Silicon is a data partner. We build your data foundation and keep maintaining it as the company grows, and our AI analytics platform is part of the partnership. For one specific piece of work, such as preparing numbers for a fundraise, we also run projects of two to four weeks. ### Is it cheaper to hire a data engineer instead? + One senior analytics engineer and their tools cost about $120–227k a year at a company of about $5M revenue (Valiotti), and hiring one takes 6–10 weeks before two to four months of ramp-up (KORE1). We quote the partnership after a short discovery, so you can compare it with both options. ### How does a data engineering consultant compare? + A data engineering consultant usually builds the pipelines and data models for a project, and reporting is often a separate piece of work. The Silicon foundation build covers both, and the partnership keeps them maintained afterwards. ### Can we work with Silicon and a consultant? + Yes. Some companies bring in a consultant for specialist work, such as a forecasting model. It can run on the same data foundation and metric definitions we maintain. ## Other comparisons - Silicon vs Amplitude and Mixpanel → - Silicon vs Tableau → - Silicon vs Monte Carlo → - Silicon vs ThoughtSpot → - Silicon vs Omni → - Silicon vs Metabase → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Silicon vs Metabase: Pricing, dbt and AI Compared URL: https://meetsilicon.com/vs-metabase/ Compare / Silicon vs Metabase # Silicon vs Metabase Metabase is an open-source BI tool with a free self-hosted edition, paid cloud plans and its own semantic layer. Silicon reads your metrics from dbt, answers questions in Slack from those definitions, and checks each change to your dbt models against the dashboards that depend on it before it merges. Book a demo 30-minute demo with the founder ## Choose Silicon if - Your metrics live in dbt and you want one definition used everywhere - You want changes to your dbt models checked against dashboards before they merge - Many people need access and you want unlimited users on every plan - You don’t have a data team and want us to build and maintain your data foundation ## Metabase may fit better if - You want a free, open-source tool you can host yourself - A small team needs simple dashboards at a low monthly price - Your data isn’t modelled in dbt and you’d rather define metrics inside the BI tool Side by side ## Side by side | Silicon | Metabase Pricing | Team $1,499/month and Pro $2,399/month, with unlimited users | Open source is free to self-host. Starter is $100/month for 5 users and Pro $575/month for 10, then $6–12 per extra user. Enterprise starts at $20,000/year Metric definitions | Read from your dbt project, or defined in Silicon YAML | Defined in Metabase’s own semantic layer (metrics, measures and segments) AI answers | An AI analyst in Slack and the app, answering from your defined metrics with the query shown | Metabase AI, including Metabot in Slack. Customer-facing AI is on Pro Changes that break dashboards | Checked on each dbt pull request, before the change merges | Dependency diagnostics lists broken questions after a column changes (Pro and Enterprise) Dashboards in git | YAML in your repository, reviewed in pull requests | Remote Sync to git with pull request review (Pro and Enterprise) Hosting | Hosted by Silicon, with your own cloud or VPC on Enterprise. Queries run in your warehouse | Self-hosted (free or paid) or Metabase Cloud Help building your data foundation | Available as a data partnership. We build and maintain it with you | Software only, with support on paid plans Based on metabase.com pricing and documentation as of October 2026. Prices and features change; check Metabase’s site for the latest. FAQ ## Questions, answered ### Is Metabase free? + The open-source edition is free to self-host, with unlimited users. Paid plans start at $100 a month for five users on Starter, and Pro is $575 a month for ten users, with each extra user charged monthly. ### Is Silicon cheaper than Metabase? + For a small team, usually not. Metabase Pro is $575 a month for ten users plus $12 for each extra user, and Silicon Team is $1,499 a month with unlimited users. The gap narrows as more people need access, and Silicon includes checks on dbt pull requests and the option of a data partnership. ### Does Metabase work with dbt? + Metabase connects to the tables your dbt models build, and its metrics are defined in Metabase’s own semantic layer rather than read from dbt. Silicon reads the metrics you already define in your dbt model YAML, so there is one definition to maintain. ### Can we move from Metabase to Silicon? + Yes. Most teams define their core metrics in dbt first, then rebuild their most-used dashboards on top. Metabase can keep running during the move. ## Other comparisons - Silicon vs Amplitude and Mixpanel → - Silicon vs Tableau → - Silicon vs Monte Carlo → - Silicon vs ThoughtSpot → - Silicon vs Omni → - Silicon vs hiring a data analytics consultant → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Silicon vs Monte Carlo: Data Observability and BI Compared URL: https://meetsilicon.com/vs-monte-carlo/ Compare / Silicon vs Monte Carlo # Silicon vs Monte Carlo Monte Carlo is a dedicated data observability platform that monitors tables and pipelines for anomalies. Silicon is an analytics platform with reliability built in: it knows every dashboard, metric and report, so it can tell you exactly which ones a change would break, before you merge. Book a demo 30-minute demo with the founder ## Choose Silicon if - You want BI, AI answers and change protection in one tool - You care most about what breaks for business users - You want impact checks inside pull requests ## Monte Carlo may fit better if - You need anomaly detection across a large estate of tables and pipelines - You already have a BI tool you’re happy with - You need observability for data that never reaches a dashboard Side by side ## Side by side | Silicon | Monte Carlo Primary job | Analytics with built-in reliability | Data observability Dashboards and reports | Included | Not a BI tool AI answers on governed metrics | Included | Not included Pull request impact checks | Dashboards, reports and metrics | Focused on tables and pipelines Anomaly detection on tables | Not the focus | Core feature Based on publicly available information as of October 2026. Products change; check Monte Carlo’s documentation for the latest. FAQ ## Questions, answered ### Can I use Silicon and Monte Carlo together? + Yes. Monte Carlo watches pipelines; Silicon protects the metrics and dashboards on top. They cover different layers. ### Does Silicon detect data anomalies? + Silicon focuses on structural changes that break analytics, such as renamed or dropped columns, type changes and failed jobs, plus metric alerts you configure. ## Other comparisons - Silicon vs Amplitude and Mixpanel → - Silicon vs Tableau → - Silicon vs ThoughtSpot → - Silicon vs Omni → - Silicon vs Metabase → - Silicon vs hiring a data analytics consultant → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Silicon vs Omni: Modern BI Compared URL: https://meetsilicon.com/vs-omni/ Compare / Silicon vs Omni # Silicon vs Omni Omni is a modern BI tool with its own modelling layer and strong spreadsheet-style workflows. Silicon puts reliability at the centre: metrics defined in dbt, dashboards as code, and every model change checked against what depends on it. Book a demo 30-minute demo with the founder ## Choose Silicon if - You want your dbt project to be the single source of metric definitions - You want breaking changes caught in pull requests - You want data apps built from a prompt on governed data ## Omni may fit better if - You prefer modelling inside the BI tool - Spreadsheet-style analysis is your team’s main workflow Side by side ## Side by side | Silicon | Omni Semantic layer | dbt or Silicon YAML | Omni’s modelling layer, with dbt integration Schema change protection | Impact checked on every pull request | Not a core feature Dashboards as code | YAML in git | Available AI answers | From governed metrics, in Slack and the app | Available Data apps | Built from a prompt | Not a core feature Based on publicly available information as of October 2026. Products change; check Omni’s documentation for the latest. FAQ ## Questions, answered ### Can Silicon and Omni read the same dbt project? + Yes. Both can work from your dbt project; Silicon reads metrics defined in your model YAML directly. ## Other comparisons - Silicon vs Amplitude and Mixpanel → - Silicon vs Tableau → - Silicon vs Monte Carlo → - Silicon vs ThoughtSpot → - Silicon vs Metabase → - Silicon vs hiring a data analytics consultant → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Silicon vs Tableau: Governed AI Analytics Compared URL: https://meetsilicon.com/vs-tableau/ Compare / Silicon vs Tableau # Silicon vs Tableau Tableau is a mature visual analytics tool with deep charting. Silicon is built for teams on a modern warehouse and dbt who want metrics defined in code, AI answers on those metrics, and dashboards that don’t break when models change. Book a demo 30-minute demo with the founder ## Choose Silicon if - Your metrics live in dbt and you want one definition everywhere - You want plain-English answers in Slack, built on governed metrics - You want dashboards reviewed in pull requests - You want breaking changes caught before merge ## Tableau may fit better if - You need highly custom, pixel-level visual design - Your team is deeply invested in Tableau workbooks and skills - You rely on extracts from many non-warehouse sources Side by side ## Side by side | Silicon | Tableau Metric definitions | In code (dbt or Silicon YAML), reused everywhere | Mostly defined per workbook or data source AI answers | From the semantic layer, with SQL and definitions shown | Available through Tableau’s AI features Dashboards as code | YAML in git, reviewed in pull requests | Workbooks managed in the Tableau UI Schema change protection | Impact checked on every pull request | Not built in Where it runs queries | Live in your warehouse | Live connections or extracts Based on publicly available information as of October 2026. Products change; check Tableau’s documentation for the latest. FAQ ## Questions, answered ### Can I migrate from Tableau to Silicon? + Yes. Most teams start by defining their core metrics in dbt, then rebuild their most-used dashboards on top. Speak to the founder for help planning a migration. ### Does Silicon replace Tableau entirely? + For warehouse-based reporting, self-serve and AI answers, usually yes. Some teams keep Tableau for specialist visual work during a transition. ## Other comparisons - Silicon vs Amplitude and Mixpanel → - Silicon vs Monte Carlo → - Silicon vs ThoughtSpot → - Silicon vs Omni → - Silicon vs Metabase → - Silicon vs hiring a data analytics consultant → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder --- # Silicon vs ThoughtSpot: AI Analytics Compared URL: https://meetsilicon.com/vs-thoughtspot/ Compare / Silicon vs ThoughtSpot # Silicon vs ThoughtSpot ThoughtSpot pioneered search-driven analytics and has a large enterprise footprint. Silicon is built around dbt: your metrics are defined in code, AI answers are built from them, and changes to models are checked against every dashboard before merge. Book a demo 30-minute demo with the founder ## Choose Silicon if - Your semantic layer should live in dbt, not in a separate tool - You want dashboards versioned and reviewed in git - You want schema protection alongside BI - You want transparent, published pricing ## ThoughtSpot may fit better if - You need a long-established enterprise vendor - Your models are built in ThoughtSpot’s own modelling layer Side by side ## Side by side | Silicon | ThoughtSpot Semantic layer | dbt or Silicon YAML, in your repo | Modelled inside ThoughtSpot AI answers | From governed metrics, with SQL shown | Search and AI-driven answers Dashboards as code | YAML in git | Primarily UI-managed Schema change protection | Built in | Not built in Pricing | Published: Team and Pro plans | Contact sales for most plans Based on publicly available information as of October 2026. Products change; check ThoughtSpot’s documentation for the latest. FAQ ## Questions, answered ### Is Silicon’s AI as capable as ThoughtSpot’s? + Silicon’s AI analyst is designed to answer from governed metrics and show its working. Try both on your own questions. Book a demo and bring yours. ## Other comparisons - Silicon vs Amplitude and Mixpanel → - Silicon vs Tableau → - Silicon vs Monte Carlo → - Silicon vs Omni → - Silicon vs Metabase → - Silicon vs hiring a data analytics consultant → ## Tell us where your data lives today. Book 30 minutes with the founder. Tell us where your data is today, and see Silicon running on sample data. Book a demo 30-minute demo with the founder