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