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Check join cardinality and aggregation grain to explain duplicate rows or double-counted measures before changing a reporting query.
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Compare datasets by agreed keys and tolerances, preserving duplicates and separating missing rows from value differences.
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Create a column-level data dictionary that separates documented definitions, observed properties and unresolved semantics.
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Investigate a sudden metric change by checking measurement quality, comparable baselines, segments, and evidence for competing explanations.
## 1. Technical Audit & Business Context | Column | Smell | Business impact | |---|---|---| | `churn_date` | Populated only for churned accounts | This is the label in disguise. Any model that sees it will score ~perfectly in training and be useless in production. | | `mrr` | Right-skewed, a long tail of enterprise accounts | Mean imputation would drag small accounts upward and understate churn risk in the SMB segment, which is where outreach capacity actually sits. | | `signup_date` | Mixed `Y
Audits a raw dataset for data smells, ties each issue to its business impact, then proposes an imputation, encoding and scaling strategy with a leakage-safe pandas and scikit-learn pipeline.