Stakeholder Update — Business Mail
© 2026 Stephen Adei. All rights reserved. Non-technical audience: Finance Manager, Product Owner. We use The Vault (raw data) and The Bookshelf (ready for reports); we say the pipeline cleans the data so the numbers add up.
Subject
[Update] Financial Data Pipeline Optimization: 98.5% Ready for Reports (Jan 2026 Run)
Executive Summary (TL;DR)
We have successfully deployed the new automated financial data pipeline. In our first production-like run (January 2026), 98.5% of records were ready for your reports; the rest are held for review so your numbers stay correct. This update removes manual file handling, cleans the data so your numbers add up, and gives you a single place to run reports — with a scalable foundation for 2026 analytics.
What we built (in plain terms):
- The Vault (raw data): Incoming transaction files are stored as-is. Nothing is thrown away; we keep a full record for audit and compliance.
- The Bookshelf (ready for reports): Only data that passes our checks is released for reporting. The pipeline cleans and validates every record — missing or invalid values are caught before they reach your reports.
- Scale: We process around 1.5 million records per month today and the system is built to handle far more as we grow.
What’s in it for you?
| Benefit | How we deliver it |
|---|---|
| Faster insights | Data is available for reporting within the same run — no manual handoffs. |
| High integrity | The pipeline cleans the data so the numbers add up. Records that don’t pass are held for review — they never reach your reports. |
| Self-service | You can run your own queries on the validated data in Athena. The system supports very large volumes (100M+ records). |
Key Findings & Metrics
Metrics below are from our first production-like run (January 2026).
Outcome at a glance
Metrics summary
| Metric | Value |
|---|---|
| Volume | Around 1.45 million records processed in the latest run. The system supports ~1.5M records per month and is built to scale to 100M+ for analytics. |
| Accuracy | 98.5% of records were cleaned and validated and are ready for your reports. 22,500 records had issues and were held for review — they do not appear in your reports. |
| Cost savings | Storage for the data we use for reporting is much smaller than for the raw files — we achieved a large reduction in storage costs. |
| Compliance | A full audit trail is kept for every transaction processed. |
What was held for review (latest run)
- Invalid currency: 1,800 records (e.g. codes we don’t support in reports).
- Missing key fields: 350 records (e.g. missing amount or ID — the pipeline stops these from breaking your reports).
- Invalid dates: 50 records (e.g. bad or future dates).
- Other issues: 20,300 records (e.g. type mismatches, schema or format issues — all held for review, none in your reports).
Summary of key findings
The "Details" Section (Technical Implementation)
For those interested in how this is implemented in code and infrastructure:
| Detail | Specification (from repository) |
|---|---|
| Pipeline logic | Python/PySpark ETL with automated schema validation. Validation: required columns, currency allowlist, type/timestamp checks. Loop prevention: max 3 attempts, duplicate detection, circuit breaker. |
| Architecture | S3-based data lake with Medallion layout: Bronze → Silver → Gold. Error-handling layers: Quarantine, Condemned. |
| Schema evolution | Additive-only; versioned paths (schema_v=v1, schema_v=v2). Glue Catalog; new columns (e.g. TransactionType) added as nullable. |
| CI/CD | GitHub Actions: CI (lint, pytest, sqlfluff, MinIO integration) → CD (OIDC, Terraform apply, Glue jobs). |
Questions? Contact the Data Platform Team for details or support.
Roadmap
Aggregated from current stakeholder communications for your review.
Key milestones
Phases follow the project diagram color convention: Kickoff (infrastructure), Build (ETL/Silver), Rollout (Gold), Optimization (success). Project name reflects this case's commissioning organization; the platform is reusable.
Summary
Immediate (before next run)
- Address completeness and reconciliation variance from the January run; validated dataset available for review and testing.
- If currency mapping is provided for invalid codes, reprocess affected records for future reports. Data Quality Team is reviewing quarantined records with source teams.
Near-term (pipeline)
- Alerts for critical data errors — Real-time alerts (e.g. Slack or email) so the team is notified immediately when something needs attention.
- Account Balance History report — Refining so it runs faster on large datasets; you get answers sooner when querying in Athena.
Platform (next quarter)
- Enhanced monitoring and real-time visibility (dashboards, key metrics).
- Gold layer and automated monthly reporting; schema versioning and governance in place.
- UAT, training, and go-live per project timeline; then performance tuning and cost optimization review.
Longer-term
- Additional data sources and advanced analytics as we scale.
- Production hardening, disaster recovery, and ongoing cost optimization.
This platform is designed as a reusable reference; the same controls and patterns apply for other institutions.
A technical one-pager (architecture and implementation summary) is available on request — ask if you’d like it attached or linked.