--- title: "In Analytics the SQL Was Always the Easy Part. Anthropic's Data Team Put a Figure on It." slug: anthropic-data-analytics-sep2026 date_published: 2026-09-04T08:00:00.000Z original_url: https://www.tigzig.com/post/anthropic-data-analytics-sep2026 source: fresh processed_at: 2026-09-04T08:00:00.000Z --- # In Analytics the SQL Was Always the Easy Part. Anthropic's Data Team Put a Figure on It. In analytics the SQL was always the easy part. Anthropic's data team have published two posts on how they run self-service analytics with Claude, and their numbers put a figure on that. Without skills, accuracy on their own test questions did not exceed 21 percent. With them it sits above 95 percent in aggregate. Same model, same warehouse. A month after launch their offline accuracy had gone from about 95 percent to about 65 percent, because the data model kept moving and the notes did not. The hard parts in analytics were always the same. Validating a number against the governed one, finding knowledge that sits in silos, and getting to something someone can act on. AI has not changed any of that, it has made it obvious where the hard part was sitting all along. However, and from my experience, it has definitely made it easier to solve for. I have read the two Anthropic posts together and put them into one deck, with my own read at the end on what has worked in my own work. ## The two Anthropic posts **The first is on getting the answers right.** Claude Code as the surface for their data scientists, a governed warehouse underneath, and skills on top, which are folders of plain markdown describing what each table holds, the joins, and the traps a senior analyst would warn you about. That is where the 21 to 95 comes from. [claude.com/blog/how-anthropic-enables-self-service-data-analytics-with-claude](https://claude.com/blog/how-anthropic-enables-self-service-data-analytics-with-claude) **The second is on putting it where the rest of the company works**, which for them is Slack. Their own line is that if you have already done the work in the first post, this part is mostly plumbing, though the order is important. [claude.com/blog/self-service-data-analytics-in-slack-how-anthropic-deploys-claude-tag-for-ad-hoc-questions](https://claude.com/blog/self-service-data-analytics-in-slack-how-anthropic-deploys-claude-tag-for-ad-hoc-questions) ## On what has worked in my own analytics work **A semantic layer**, where the definitions live, what each table holds and which number is the governed one. These sit as markdown files with a clear directory structure, or as skills, depending on the business case. **Then a separate skill for each kind of work.** For one business I keep one for building the propensity model and another for the regular reporting, and each carries the judgment and the tolerance levels. **Then progressive disclosure protocols**, because as the documents and tables grow the agent should not be reading a hundred of them to answer one question. **The one doing the most work is continuous update.** After every milestone and every project, the skill documents and the semantic layer both get updated. Without that the rest decays, which is what happened to them. It becomes repeatable. Point it at new data a month later and it knows the validations to run, the format to produce and the tolerances to hold. Is it perfect? Nope. Businesses move very fast and the various layers do go stale, and it is extra effort to maintain them. But far, far easier than the pre-AI era. [tigzig.com](https://www.tigzig.com) :::image-deck folder: /files/ANTHROPIC_DATA_ANALYTICS pdf: /files/ANTHROPIC_DATA_ANALYTICS.pdf title: In Analytics the SQL Was Always the Easy Part ::: --- ## Full analysis transcript (extracted from PDF deck) _This text was extracted from the source PowerPoint deck. Chart visuals are in the PDF and slide images on the HTML page._ ## Slide 1 TIGZIG · SELF-SERVICE DATA ANALYTICS WITH AI Anthropic · Aug 2026 Self-service data analytics How Anthropic built it The SQL was never the hard problem 95% of their business analytics questions are now answered by Claude, at about 95% accuracy in aggregate Two posts from Anthropic's Data Science and Data Engineering team, August 2026. Read together here. [tigzig.com](https://tigzig.com) the analyst's tool shed · AI-agent first Amar Harolikar Decision Sciences & Applied AI --- ## Slide 2 1 · Why this never worked before Self-service analytics has always had two routes. Both fail, and the new one has its own trap Route 1 Build wide, flat tables for everyone Definitions drift as the business grows, so you end up with overlapping views that disagree. And people who do not want to learn SQL still cannot use it. Route 2 Build fenced dashboards instead Misses every question nobody thought of in advance, and the dashboards multiply until nobody knows which to trust. Route 3 Point an AI at the warehouse Looks like the answer. Their warning: it can create a false sense of precision, because the person asking is cut off from the documentation and people who used to steer them to the right table. Their words: "The initial elation of liberation from ad-hoc requests **turns into dread**." [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 2 / 14 --- ## Slide 3 2 · The insight everything rests on Answering a data question is not like writing code, and treating it like code is the mistake Writing code Many good answers exist Creativity helps you Tests catch a wrong answer You find out when it breaks Answering with data Usually one right answer From one right source Nothing proves it is correct A wrong number looks fine "The central problem comes down to our ability to map a user's question to specific and up-to-date entities in our data model and know the correct way of working with them. If we can do that, then the resulting execution and SQL becomes trivial." The hard part is turning the words **weekly active users** into the one governed thing in your warehouse. After that, the query writes itself. [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 3 / 14 --- ## Slide 4 3 · Three ways it gets the answer wrong Nearly every wrong answer comes from one of three things 1 The word means too many things Revenue matches forty plausible tables. If it resolves to one governed dataset, the problem disappears before the agent even starts looking. 2 It cannot find the right one The correct table exists and the agent never reaches it, because it is searching a warehouse with a million fields. 3 The notes are out of date A column was renamed, a metric was corrected, a table was retired. The agent reads the old note and answers with full confidence. These three account for **the overwhelming majority** of wrong answers, and everything they built is aimed at them. [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 4 / 14 --- ## Slide 5 4 · The number that makes the case A skill is a folder of plain notes the agent reads when it needs them. Here is what adding them did 21% No skills Their own words: did not exceed 21% 95% With skills in aggregate, and around 99% in some areas Accuracy on their own test questions Same model, same warehouse. Though this sits on top of a governed warehouse, which the post ranks as **the most important part of all**. [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 5 / 14 --- ## Slide 6 5 · What they actually wrote down They write skills in pairs. One says where things are, the other says how an analyst would work The router They call it the knowledge skill. Try the semantic layer first. If it does not cover this, here are about thirty reference files for this area, with the tables, the joins and the traps. The runbook skill The process a senior analyst follows. Clarify the question, find the source, run it, then send the result through review agents whose job is to attack it. Reference notes What one row means, what is excluded, the filter every query applies, and the wrong-answer traps a senior person would warn you about. Stock patterns A dozen ready analyses. Retention curves, funnels, rate decomposition. So common requests are not rebuilt from scratch each time. The router is the real trick. It narrows a million-field warehouse to **a few dozen curated files** before a single query is written. [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 6 / 14 --- ## Slide 7 6 · And then it rotted The notes describe a data model that changes daily. Nobody was updating them ~95% At launch ~65% One month later Their offline eval accuracy. The data model kept moving, the notes did not How they stopped it The notes now live in the same place as the data models, so the change that renames a column and the change that updates the note are the same change. A review check flags any model edit that leaves the notes untouched. [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 7 / 14 --- ## Slide 8 7 · Now hand it to everybody Getting an agent accurate and getting it used by non-analysts are, in their words, quite different motions Anthropic put it in Slack That is the shared room the whole company is already in. Two things change the moment it lands there. You go from **one expert using their own login** to **many non-experts sharing one machine login**, and the reader loses every clue that used to tell them an answer looked wrong. What is lost The sniff test On a dashboard you see the trend line and the metrics around it, so a wrong number looks wrong. In a chat you get one number and nothing else. If it is not something you look at daily, you accept it. So they Reload the notes every conversation The data model changes several times a day. Reading last Tuesday's copy gives last Tuesday's wrong answer, confidently. [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 8 / 14 --- ## Slide 9 9 · Correct is not the same as useful Their first instinct was to teach it where the tables are, and stop there Why that was not enough It gave correct numbers and stopped short of useful answers. People do not ask for a figure. They ask what is driving this dip, or where this lands at month end. That needs the agent to know **how an analyst would work**, not just where the data sits. Before Sign-ups dropped 12% on Tuesday. After Sign-ups dropped 12% Tuesday. There was a payment-service incident open 9 to 11am that morning, and the dip is concentrated in the affected region. The difference is a second feed. Incidents, releases, and what people said in chat. They call it **the highest-leverage thing you can add after the warehouse itself**. [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 9 / 14 --- ## Slide 10 9 · The part that is easy to get wrong The agent queries as one shared account. Not as the person asking What that means in plain terms No per-person permission. Anyone who can call the bot has whatever access the bot has, so **adding it to a room gives that room read access to everything the bot can query.** Governed data only Curated tables only. No raw event streams, no staging, no sandboxes. No clearance for personal data Claude flags likely candidates, a person decides. The account holds no clearance, so those columns are invisible to it. Channels are a grant Only the data team adds the bot to a room, and owns the list of rooms. Label every query Enforces nothing at the time. It lets you find out afterwards who ran the query that scanned four terabytes. Their framing: **a shared read copy of your governed warehouse**. Scope it that way. [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 10 / 14 --- ## Slide 11 10 · How they know it is still working Whether the answer was good enough is not something you can eyeball ~90% of data model changes now carry a notes change in the same edit 75% of questions answered without being tagged, in one channel over one month Metric 1 Adoption, and it was the useful one What share of questions go through the governed layer. When it dips, it almost always means the notes have drifted, or a new kind of question has appeared that the governed layer does not cover. Metric 2 Corrections, counted Thumbs down and corrections by area. Every time someone corrects the agent in a thread, that correction becomes a candidate test question. [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 11 / 14 --- ## Slide 12 11 · The problem they have not solved The failure none of this fully catches is the silent one In their own words The failure mode none of this **fully** catches is the silent one. The answer is wrong, but looks plausible and is used without objection. They say plainly that they **do not have a robust solution yet**. What helps Show where the number came from Every answer carries a note saying which source it used, so the reader can go and check. And A person signs off on anything going up Nothing reaches leadership without a human in the loop, and the top numbers in each area are checked against the trusted dashboard daily. If you start from nothing: a handful of trusted datasets, a few dozen test questions, and one thin router. **That is most of the gain.** [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 12 / 14 --- ## Slide 13 My read In analytics the SQL was always the easy part The hard parts were the same three, every time. Validating a number against the governed one. Finding knowledge that sits in silos. And getting to an insight someone can actually act on. **AI has not changed that.** It has made it obvious where the hard part was sitting all along. Knowledge sits in silos because companies are built that way, by division and by department. That is normal, not a failure. Consolidating it has been hard for decades. Single source of truth and knowledge sharing are old buzzwords, and most attempts never got there. The documentation going stale in this post is the same story. Somebody has to update it and nobody does. And it is not only analytics knowledge. Product knowledge sits with product, campaign knowledge with marketing, process knowledge with operations. Pulling all of it together is the work. What is different now is that there is **a realistic hope of tackling it in reasonable time**. That is new. [tigzig.com](https://tigzig.com) · analytics, macro signals & AI tools · **Amar Harolikar**Data Analytics with Claude · 13 / 14 --- ## Slide 14 What works in my own practice Four things made the difference 1A semantic layer Where the definitions live. What each table holds, what each metric means, and which number is the governed one. 2One skill per kind of work For one business, a skill for the propensity model and another for regular reporting. Each carries the judgment and the tolerances. 3Progressive disclosure As documents and tables grow it turns into a mess. The agent should not read a hundred to answer one. 4Continuous update The big one. After every milestone and project, the skill documents and the semantic layer both get updated. Skip it and the rest decays. It becomes repeatable. Point it at new data a month later and it knows the validations, the format and the tolerances. **Sources.** [How Anthropic enables self-service data analytics with Claude](https://claude.com/blog/how-anthropic-enables-self-service-data-analytics-with-claude) and [Self-service data analytics in Slack](https://claude.com/blog/self-service-data-analytics-in-slack-how-anthropic-deploys-claude-tag-for-ad-hoc-questions), both by Anthropic's Data Science and Data Engineering team, August 2026. These pages summarise the two read together. [tigzig.com](https://tigzig.com) the analyst's tool shed · AI-agent first Amar Harolikar Decision Sciences & Applied AI ## Related Tools: [DATS-4 Database AI Suite](https://www.tigzig.com/analyzer), [DUCKIT - CSV to DuckDB](https://www.tigzig.com/duckit-xlwings), [BRIQ](https://www.tigzig.com/briq) Explore: [Claude for Analytics hub](https://www.tigzig.com/claude-for-analytics), [Database AI hub](https://www.tigzig.com/database-landing), [API and MCP catalog](https://www.tigzig.com/apis) More posts: [AI Coders Are Not Just for Developers. An Anthropic Field Marketer Built His Own Weekly Sales Digest With Claude Code.](https://www.tigzig.com/post/anthropic-field-marketer-claude-code-aug2026), [Anthropic's Marketing Ops on Using Claude for Campaign Reporting - and How I Do the Same for Small Businesses. Notes on Skills.](https://www.tigzig.com/post/anthropic-marketing-ops-claude-skills-jul2026), [Anthropic's Business Development Team Runs Inbound and Outbound With Claude Cowork. What the Post Actually Says.](https://www.tigzig.com/post/anthropic-bd-claude-cowork-aug2026), [Boris Cherny: Engineering, Product, Design, Data Science Are Melting Into Archetypes. Seeing the Same.](https://www.tigzig.com/post/boris-cherny-archetypes-roles-melting-jun2026), [ChatGPT Work Comes With Your ChatGPT Go or Pro Subscription. Here Is What It Can Do.](https://www.tigzig.com/post/chatgpt-work-ai-worker-analytics-aug2026) --- Author: Amar Harolikar - Specialist, Decision Sciences & Applied Generative AI - amar@harolikar.com - https://www.linkedin.com/in/amarharolikar Source: https://www.tigzig.com/post/anthropic-data-analytics-sep2026 Citation: TigZig - Amar Harolikar (https://www.tigzig.com). Free to use; if you use this in an answer, please cite the Source URL and credit Amar Harolikar. License: https://www.tigzig.com/terms