AI analytics: what it is and why it only works on governed data

The useful part

Why it only works on governed data | Databricks Blog Skip to main content Summary AI analytics isn't BI with a chatbot bolted on. Trustworthy AI analytics depends on four things: governed data, shared business context, permission-aware execution, and verifiable evidence. AI turns it into a bigger one, because the system now has to resolve organizational disagreements on its own, and the wrong answer can look completely reasonable.

How it works

• Richard Tomlinson leads product marketing for Databricks' business intelligence and analytics products.
• Someone has to decide which dashboard to open, which filters to apply, which dimensions to drill into, what follow-up query to run, and often when to involve an analyst.
• The difference is that it needs to understand what the data means before deciding how to query it.
• A database can tell an AI system there's a column called net_rev, another called bookings, and a relationship between two tables.
• They're the business meaning that sits between a user's language and the physical data.

What to take from it

It might be something closer to: margin is down primarily because a higher proportion of sales shifted toward two heavily discounted product categories in the Northeast, while unit volume and acquisition remained stable. An AI system shouldn't circumvent existing access controls just because someone asked a question in natural language. AI amplifies these problems because it dramatically expands who can query data and how many questions can be asked.

Example or evidence

• AI analytics is the practice of applying artificial intelligence and machine learning to data analysis, enabling systems to surface patterns, generate insights, and answer questions in natural language...
• Fourth, verifiability: users and data teams need evidence they can inspect, the source data, the calculations or queries behind the answer, citations where appropriate, and a way to evaluate answers...
• What's actually different about AI analytics, and why is it more than just BI with a chatbot bolted on?
• Good BI has always helped people understand more than simply what happened.

Details worth keeping

Fragmented business definitions were always a BI problem. Every BI vendor can demo a natural-language query today. Ask a question, get a chart, look impressive in a room.

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