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Analytics in the AI age
AI can now answer business questions in seconds, and get them confidently wrong. This is our plain-English writing on getting the speed without losing the truth.
What's possible
Everything you'd expect. Then what only AI can do.
The four things every analytics function does, AI or not: assemble it, analyze it, report on it, respond to it. Each has a table-stakes baseline you'd expect (the reactive part) and an agentic layer that only AI makes possible (the proactive part).
Build
Assemble it: the governed foundation, and the dashboards on top.
Table stakes
Dashboards: your whole business at a glance, every metric defined once so no two reports disagree.
A dashboard from a prompt
Describe what you want to see and get a working, live data app built on your governed metrics. No ticket, no wait.
The full AI-enabled build
The entire stack, built by AI: pipelines, models, tests, dashboards, and the analyst itself. Weeks not quarters, with a human signing every metric.
Analyze
Analyze it: ask your data anything, and work the problem in plain English.
Table stakes
The old way: write SQL, or wait on an analyst to build the report and hope you asked the right question.
An AI analyst in Slack and on your dashboards
Ask anything in plain English, right where your team already works. It reads only your governed data, and it shows its work.
Forecasting on live data
Ask where revenue, margin, and cash are heading, with projections that move as the data moves and ranges you can actually plan around.
Report
Report on it: surface what matters, and get it in front of the right people.
Table stakes
Scheduled reports, dashboards, and threshold alerts, delivered on a cadence. You still have to read them and know what to look for.
The Morning Read
A standing analyst reviews your business every morning and tells you what a sharp CFO would notice, including the things you didn't know to check, each one traced to its source.
Respond
Respond to it: put the answer to work.
Table stakes
The old way: someone reads the report, decides, and does the thing by hand.
Actions drafted for your yes
The recovery memo, the board pack, the follow-ups: drafted straight from the analysis and waiting on your approval, never fired without it.
Scheduled deliveries from a prompt
“Email me margin by channel every Monday at 7.” Say it, and the delivery configures itself.
Every one of these rides the same governed foundation as your dashboards, so the AI and the chart can never disagree. Trustworthy first, clever second.
The Strata Way · 5 min read
What's a full AI-enabled dev cycle?
Every analyst uses AI now, so using it isn't the differentiator. Here's what it looks like when AI runs the entire analytics development cycle, and why the skeleton matters more than the speed.
Read →
AI + data · 6 min read
The same AI scored 21%. Then 95%. Nothing about the AI changed.
Anthropic published exactly what it takes to trust an AI with business numbers. Here's the finding in plain English, and what it means if you run a company.
Read →
AI + data · 4 min read
Any AI analyst needs these three things
A plain-English checklist for what has to exist underneath an AI before you can trust it with your numbers, plus a way to score your own setup.
Read →
From the field
The clearest thinking on agentic analytics comes from the people defining it. We link you straight to the source.
Databricks · External
What is agentic analytics?
The clearest primer we've found on agentic analytics: how autonomous AI agents take analytics past static dashboards into a continuous loop that watches your data, reasons about what it finds, and acts, and what has to be true underneath it (governed data, a semantic layer, human oversight) for any of it to hold up. It's the frame we build on.
Read it on Databricks ↗
dbt Labs · External
Understanding agentic analytics
The foundation argument, from the team behind the modeling layer most analytics stacks are built on: an agent is only as reliable as the data underneath it, so raw tables have to be modeled, tested, and given shared semantic meaning before anything can reason over them. The clearest case we've read for why metric definitions everyone agrees on have to come before the AI.
Read it on dbt Labs ↗
Coming next
- · Why we verify everything AI builds, by hand
- · Current “AI in data” conversations: what's real and what's noise
- · Do you trust the answers your AI is giving you?
- · How to stay secure when building AI analytics
The Strata Way
How we employ AI in every build
We're AI-forward, not AI-everything. AI hallucinates, assumes, and silently narrows analyses in ways you'll never see unless you ask. So we build against it. The practice underneath every Strata engagement:
Using AI isn't the differentiator
Every analyst uses AI now. The difference is whether AI runs the full development cycle inside a structure built for it, or just autocompletes pieces of the old way. AI drafts, machines test, and a human signs every metric before it ships.
Four layers under every answer
One governed foundation, definitions your team owns, context that lives with the data, and continuous validation. It's the same architecture Anthropic published for their own analytics, and we've made it our standard for every client.
Guardrails as a feature
Cost, speed, and accuracy controls are built into the foundation itself: predictable cloud bills, answers in seconds, and numbers that tie out to their sources, with cash reconciled to the penny.
Full control and autonomy
We integrate AI in every build step, which enables us to be many times more efficient than other data teams, even ones using AI. When your team is ready, we train your data team with full control and autonomy.
See it applied to your business.
The free roadmap maps your version of the foundation in about ten minutes.