AI Data Analysis Starter

Problem

A common data-analysis failure is not missing charts; it is handing a spreadsheet to a model and accepting judgments that cannot be reviewed: field meanings are unclear, amount definitions conflict, missing values are filled silently, duplicates are removed without a business rule, and correlation is presented as causation. This skill turns that loose process into a checkable workflow that starts from the business decision and defines the metrics, data dictionary, and analysis boundaries before interpreting results.

How It Works

The skill follows a task brief → data profile → cleaning → analysis → conclusion path:
- Define the problem: convert “look at sales data” into a calculable question, such as comparing valid orders, net revenue, and refund rates by channel.
- Build a data dictionary: record field meaning, type, unit, missing-value rule, unique key, and join logic.
- Profile the data: check row count, missingness, duplicates, date gaps, category spelling, and join success rates.
- Clean with an audit trail: keep original columns, create cleaned columns, and log each rule’s affected rows and rationale.
- Use minimal analysis: choose counts, sums, medians, distributions, group comparisons, trends, relationships, and exception checks based on the question.
- Visualize and conclude: use line charts, sorted bar charts, stacked charts, or box plots when appropriate, and separate facts, interpretations, unknowns, and actions.

Boundaries

It fits structured business data such as orders, customers, channels, and regions, especially when the goal is to produce a defensible entry-level analysis. It is not a substitute for strong causal inference, especially with small samples or unclear field definitions. Sensitive fields should be masked or kept local, and key numbers should still be independently rechecked.