Governed AI analytics
Letting people ask questions of enterprise data in their own words is no longer the hard part. The hard part is making the answer the same one the CFO would give, every time, for every person who is allowed to see it. These guides cover the machinery that makes a natural-language answer governed rather than merely fluent, and how to test whether a vendor's version of it actually holds.
How to verify an AI analytics accuracy claim
Vendors quote 95% accuracy. Five questions decide whether that number means anything for your data, and what a verifiable answer looks like.
7 min readWhat is a semantic layer?
What a semantic layer maps, why natural-language analytics fails without one, and how certified metrics differ from ad-hoc SQL.
6 min readMCP for enterprise analytics: letting agents query governed data
What the Model Context Protocol is, why it matters for analytics, and what has to be true before an AI agent should be allowed to query your warehouse.
6 min readRow-level security for AI analytics
Why row-level security has to move into the query path when AI agents start asking questions, and how to tell whether a platform enforces it there.
5 min readCross-source analytics without a warehouse migration
Most enterprise questions span a CRM, an ERP, and a warehouse. What it takes to answer them without first moving everything into one place.
6 min readWhat is agentic analytics?
Agentic analytics explained, how it differs from conversational BI and dashboards, what it changes about governance, and where it helps.
5 min read