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13 articles
A document diff does not explain why an interpretation changed. Change Context ties formal asset changes to their stated reasons and evidence references, while preserving unknowns and confirmation status.
Read article →Python made analysis flexible, but rapid agent edits made scripts, dependencies, and intermediate outputs harder to follow. Why I chose SQL-first, and where Python still belongs.
Read article →AI can deliver results faster than people can absorb them. Here is how I preserve decisions and evidence so the person responsible can explain, challenge, and own the analysis.
Read article →How I let external Agents keep their general capabilities, use a built-in Agent for a deeper analytics workflow, and connect both to one control plane.
Read article →Once AI Agents find data, write SQL, run notebooks, and revise reports, chat history and file versions no longer preserve the full evidence chain. This is why data analysis needs Analysis Lineage.
Read article →A Skill can preserve an analytical method and package SQL or context from a specific case. Real analysis also depends on data versions, execution state, human decisions, evidence, and limitations.
Read article →An AI agent does more than produce longer answers: it uses tools based on state, advances multi-step work, and leaves verifiable deliverables. This article explains the differences, evaluation criteria, use cases, and adoption boundaries through data analysis.
Read article →The hardest AI analysis errors to detect come from definitions, denominators, joins, and time windows. This article presents four review layers: Plan, SQL, checkpoints, and limitations.
Read article →Natural language lowers the barrier to querying data, but it cannot replace SQL as reviewable evidence for grain, joins, filters, and aggregation. The better division of work is AI drafting, SQL preserving evidence, and people reviewing it.
Read article →A Plan aligns the decision question, denominator, grain, time window, and validation approach before data retrieval. Execute then becomes a clear human authorization boundary, preventing ambiguous assumptions from turning into official numbers.
Read article →Learn how reproducible AI analysis preserves inputs, SQL, assumptions, validation, and reports so teams can rerun results and explain every change.
Read article →Analysis agents, conversational BI, and traditional BI serve different stages. Quick answers, definition exploration, and stable monitoring should not be confused. This article provides a five-dimensional selection matrix and three practical scenarios.
Read article →Learn how local-first AI data analysis reduces data movement, queries local files, and preserves reusable work while clarifying cloud model boundaries.
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