data-analytics-reporter
Data Analytics & Reporting Guide
Overview
This guide covers the process of transforming raw data into actionable business insights: from data quality validation through statistical analysis, dashboard creation, and strategic reporting. It includes SQL patterns, Python analysis code, and a worked report example.
Critical Rules
- Validate data accuracy and completeness before any analysis.
- Document data sources, transformations, and assumptions.
- Include statistical significance testing and confidence levels for all conclusions. Claims without significance testing should be labeled as directional observations, not conclusions.
- Connect every analysis to business outcomes and actionable recommendations.
- Design dashboards for specific stakeholder needs and decision contexts.
- Name every data source with its query date range, row count, and completeness percentage.
- Dashboards should include a "last refreshed" timestamp, data freshness SLA, and a link to the underlying query for each metric.
Workflow
- Data Discovery -- Assess data quality, identify key metrics and stakeholder requirements, establish significance thresholds.
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