Begin with the decision
Useful analysis starts with what someone needs to decide—not with whichever chart is easiest to produce. DeThink helps frame the metric, population, comparison, time window, and assumptions before calculations begin.
That framing makes it easier to detect missing fields, biased samples, inconsistent definitions, and questions the dataset cannot responsibly answer.
Use models for complementary passes
One model can inspect structure and propose cleaning steps, another can challenge the statistical interpretation, and another can translate the result for a non-technical audience. Shared context keeps definitions and caveats consistent across those passes.
- CSV and spreadsheet exploration.
- Cleaning plans and anomaly review.
- Metric definitions and analytical narratives.
- SQL, Python, formulas, charts, and executive summaries.
Keep analysis reproducible
Record transformations, preserve the original data, review generated code, and test calculations against known examples. Sensitive or regulated datasets require appropriate authorization, minimization, and security controls before they are shared with any AI service.