Tested, hands-on guidance on using AI across the analytics workflow, from text-to-SQL to dashboard design, written for analysts and data scientists rather than executives.
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Work out what an AI model actually costs per month from your token usage, and compare the major models side by side.
Free toolPaste any text to estimate how many tokens it uses, and see what that text would cost to send to each major model.
Free toolCompare a flat monthly chat subscription against the equivalent API usage and find the break-even point where one overtakes the other.
Free toolFour questions about your task, budget and experience — then a recommendation and the guides to go with it.
The mental models a data professional needs to use AI well, from how language models read a schema to where embeddings beat classic feature engineering.
3 guidesSetting up and trusting conversational and text-to-SQL assistants that let you and your stakeholders query a warehouse in plain language.
3 guidesUsing AI to turn model output and query results into memos, documentation, and summaries that survive stakeholder scrutiny.
3 guidesApplying AI to the visual side of analytics, from generating chart specs to catching accessibility and layout problems before a dashboard ships.
3 guidesThe automation need: prompting for correct SQL and pandas, and wiring LLM steps into pipelines that clean, triage, and self-heal.
3 guidesThe money and career side: costing AI-in-the-loop workflows, winning budget, and understanding how these tools reshape the analyst ladder.
A model does not read your database the way you do. Understanding what it actually sees explains most of its confident, wrong joins.
A practical architecture for letting analysts ask questions in English while the model never sees your raw schema, PII, or ungoverned joins.
Token prices are the smallest line item. Here is a cost model that accounts for retries, context bloat, human review, and the warehouse bill underneath it.
How to catch renamed and reordered upstream columns automatically without letting a language model silently corrupt your schema.
The failure isn't bad SQL syntax — it's confident, wrong numbers that look exactly like right ones. Here's where they come from and how to catch them.
A vision model won't tell you if your numbers are right, but it will catch the layout problems you stopped seeing three revisions ago.
Most AI coverage is written for everyone or for no one.
We write for the person staring at a broken join at 6pm, not the keynote audience. Every recommendation here comes from someone who has shipped a dashboard, defended a number, or debugged a pipeline that an AI tool confidently got wrong.
Read more about who we are and our editorial standards.