AI + Analytics
How to check an AI answer about your numbers before you act on it — and why DataLens always shows its work alongside the answer.
Articles in this collection
13 articlesText-to-SQL Isn't the Hard Part — Schema Context Is
Writing correct SQL syntax is the easy 20%. Knowing which of five tables holds "revenue" and how they join is the part that actually determines whether the answer is right.
How to Audit an AI-Generated Query in 60 Seconds
A fast checklist for scanning any AI-written SQL query before you trust its output — the joins, the filters, and the aggregation, in that order.
Why Every DataLens Answer Ships Its SQL
A product philosophy note: why we decided a number without its query attached isn't a trustworthy number, and what that decision costs us.
Bring Your Own API Key: What That Actually Means for Your Data
DataLens lets you connect your own model provider key instead of routing through ours. Here is what that changes about where your data goes.
Benchmarking an AI Analyst on Your Own 20 Golden Questions
Fluency is not accuracy and demos are not decisions. Building a verified question-SQL-answer suite that scores models, prompts and schema changes.
Anomaly Alerts That Do Not Spam: Static Thresholds vs Dynamic Baselines
Every false alarm trains readers to ignore the real one. Respecting data maturity, expecting seasonality, and the severity ladder that keeps channels trusted.
Least-Privilege Data Access for AI Tools: Views, Redaction, Row Limits
Exposing everything is a privacy surface and an accuracy problem at once. The analyst views, tokenisation and caps that narrow the world and improve the answers.
What an AI Analyst Can't Do Yet — an Honest List
Eight things an AI analyst genuinely cannot do, written by someone building one. Where the ceiling is, why it is there, and which limits are shrinking.
Semantic Layers vs. Prompting: Where Business Definitions Should Live
You can put "active customer means ordered in 90 days" in a prompt, or in a semantic layer. One of those is a definition; the other is a suggestion.
How We Keep an LLM From Inventing a Column Name
A model that has never seen your schema will guess `order_total` and be wrong. The four layers — retrieval, constrained context, dry-run validation, repair — that stop it shipping.
The Real Cost of an AI Analytics Question: Tokens, Query Bytes, and What Drives the Bill
Two costs stack on every AI-generated answer, and the smaller one gets all the attention. A line-by-line breakdown of where the money actually goes.
Scheduled AI Reports vs. Dashboards: When a Daily Narrative Beats a Chart
A dashboard waits to be opened. A scheduled narrative arrives whether or not anyone was looking. Which failure mode each one has, and how to pick.
Seven Questions to Ask Before You Trust an AI's Answer About Your Data
A syntactically valid query can still answer the wrong question. Seven concrete checks to run before you act on a number an AI gave you.