Client Context
A financial institution consolidating transactional and risk data relied on source databases, cloud data lake, Spark processing, warehouse, and reporting tools to support regulatory metrics, balances, risk aggregation, and management reporting. The operating model handled multi-terabyte datasets and daily controls across South Africa, using transactions, balances, customer, risk, and reference data. Control totals matched at a high level while field-level transformations remained wrong. Regulatory reporting required reproducible reconciliation evidence.
Challenges
- Control totals matched at a high level while field-level transformations remained wrong.
- Regulatory reporting required reproducible reconciliation evidence.
- The data lake received transactions, balances, customer, risk, and reference data from source databases, cloud data lake, Spark processing, warehouse, and reporting tools, with inconsistent quality and timing.
- Row counts alone could not detect transformation errors, duplicates, or silent business-rule failures.
- Schema changes and late-arriving data created downstream instability in regulatory metrics, balances, risk aggregation, and management reporting.
Solutions Implemented
- Built distributed row, checksum, aggregate, and business-rule validation.
- Added lineage-aware exception reports and data-quality pipeline gates.
- Built distributed source-to-target checks using counts, checksums, aggregates, and business-rule reconciliation.
- Implemented schema, type, null, duplicate, referential-integrity, and freshness validation for transactions, balances, customer, risk, and reference data.
- Added data-quality gates and drift alerts to ingestion and transformation pipelines.
- Created dashboards showing failed records, lineage, reconciliation status, and trend-based quality metrics.
Value Delivered
- Reduced the primary testing or operational effort by approximately [55%], subject to validation against approved engagement data.
- Improved trust in regulatory analytics.
- Detected silent data corruption before it reached business users.
- Reduced manual SQL sampling and reconciliation effort.
Impact Highlights
- Achieved an estimated [40%] improvement in cycle time, coverage, or processing consistency; replace with the approved client metric.
- Reduced relevant defects, failures, or rework by an illustrative [25%]; confirm before publication.
- Reduced manual reconciliation and reporting risk.
- Established scalable controls for future sources and domains.
