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.
Analysis -- Build reproducible pipelines, apply appropriate statistical methods, calculate confidence intervals.
Visualization & Reporting -- Create interactive dashboards with drill-down, write executive summaries with findings and recommendations.
Impact Measurement -- Track recommendation implementation, measure business outcome correlation, iterate.

See SQL Patterns for executive dashboard and marketing attribution queries.

See Report Example for a full Q1 2026 worked report and Python RFM segmentation code.

Report Structure

Report Quality Checklist

All statistical claims include confidence intervals at a minimum 95% confidence level.
Every data source is named with its query date range, row count, and completeness percentage (e.g., "Snowflake analytics.orders, Jan 1 - Mar 31 2026, 142,300 rows, 99.7% complete").
Recommendations include projected ROI with explicit assumptions stated (e.g., "Assumes 12% reactivation rate based on historical win-back campaign performance of 7-15%").
Reports are reproducible: all SQL queries and Python scripts are included and can be re-run against the documented data sources to regenerate all figures and tables.

Reference

Capabilities Reference

Statistical analysis: regression, forecasting, A/B testing, segmentation, correlation
Dashboard and report creation (Tableau, Power BI, Looker, custom)
SQL query optimization and data warehouse management
Python/R for analysis, modeling, and automation
Customer analytics: lifetime value, churn prediction, RFM segmentation
Marketing attribution and campaign ROI measurement
Financial modeling and business performance analysis
Data quality assurance and GDPR/CCPA compliance in analytics

Scripts

The following scripts are available in the scripts/ directory for data analysis:

`scripts/analyze_csv.py`

Auto-profiles a CSV dataset: row/column counts, column types (numeric/text/date), null percentages, basic statistics for numeric columns (min, max, mean, median, stddev), and top 5 unique values for text columns. Outputs markdown or JSON.

python scripts/analyze_csv.py data.csv
python scripts/analyze_csv.py data.csv --json

`scripts/check_data_quality.py`

Checks a CSV file for data quality issues: duplicate rows, columns with >50% nulls, mixed data types, and outliers (>3 stddev from mean). Reports a quality score (0-100) and specific issues found.

python scripts/check_data_quality.py data.csv
python scripts/check_data_quality.py data.csv --stddev 2.5 --json

data-analytics-reporter