From Inconsistent Distributor Reports to Trusted Analytics

Executive perspective

Distributor teams often receive monthly CSV and Excel files with inconsistent headers, timestamps, missing SKUs and duplicate rows. Manually combining them creates uncertainty in sell-through, returns, inventory age and regional rankings.

Engagement and solution architecture

The engagement repository defines extraction and canonicalization in Python, JSON Schema and business-rule validation, segregated rejected records, Snowflake staging and analytics tables, and Power BI reporting with reproducible tests and setup guidance.

Data model and analytical method

Map disparate source fields to a canonical record schema. Detect duplicates and invalid quantities; validate products and distributors against reference tables; quarantine rejects with reasons; preserve batch identifiers; and reconcile accepted source totals against warehouse loads.

What business teams can investigate

Reporting questions include distributor sales and inventory movements, sell-through rate, aging, returns and regional under-reporting. The repo includes scripts, SQL structures, dashboard guidance and CI tests for repeatable runs.

Governance and measurement integrity

Do not place credentials or client data in public repositories. Test schema evolution, idempotent loads, rejection thresholds, SCD mappings, and evidence logging. Published sample files are synthetic, not original client invoices.

Evidence and outcomes

The GitHub materials evidence a structured pipeline and testing approach. Specific hours saved or accuracy gains have not been supplied as verified engagement outcomes.

Practical next steps

Operationalize data contracts, define issue ownership for rejects, implement batch-level monitoring and verify finance or sales totals before dashboard publication.

Explore the engagement

Explore the documented project on GitHub and request a private RUSA Analytics consultation to discuss an adapted solution. Source: https://github.com/RUSA-Analytics/rusa-distributor-intelligence