data-analysis

name	data-analysis
description	Use for exploratory analysis, statistical analysis, trends, cohorts, segmentation, variance drivers, forecasting inputs, analytical visualisation or decision insights. Do not use when the primary need is a reusable ingestion or transformation pipeline.

Data Analysis

1. Define the decision and analytical question.
2. Confirm population, period, grain, source and exclusions.
3. Assess completeness, bias, missing values, outliers and data quality.
4. Select methods appropriate to the data and question.
5. Separate description, inference, correlation and causation.
6. Show absolute values, denominators and uncertainty.
7. Segment results where aggregates hide material differences.
8. Choose tables or charts based on the decision—not decoration.
9. Make calculations reproducible and traceable.
10. State limitations and what additional evidence would change the conclusion.

An insight without source, method and implication is incomplete.