Case Study: Enterprise Data Quality & Observability Program

Banking

Enterprise Data Quality & Observability Program

The Challenge: Systemic Data Failures Driving Regulatory Fines

A global transaction bank processing over $2 trillion in daily payment flows had received three regulatory fines in 18 months — each tied to data quality failures in statutory reporting submissions. Incorrect counterparty classifications, stale reference data, and undetected pipeline failures were causing material errors in CCAR stress test outputs, liquidity coverage ratio calculations, and transaction reporting filings. The data engineering team was spending 40% of their working hours investigating and remediating data incidents, leaving little capacity for new development. Incident detection was entirely reactive: problems were discovered when downstream consumers — risk teams, regulatory reporting teams, or, worst of all, regulators — noticed anomalies in final outputs. With over 200 data pipelines feeding regulatory submissions and no automated monitoring in place, the team was effectively flying blind. The CIO had committed to regulators that systemic data quality controls would be operational within nine months.

Our Solution: Layered Data Quality and Observability Framework

Lydatum implemented a multi-layered data quality and observability framework across the bank's Snowflake-based data platform, covering automated validation, anomaly detection, lineage tracking, and incident response:

• Rule-Based Data Quality Validation: Using Great Expectations, we implemented over 800 data quality checks embedded directly in the pipeline execution layer — covering completeness, referential integrity, format conformance, value range validation, and cross-system reconciliation for all 200+ pipelines feeding regulatory outputs. Failed checks triggered immediate pipeline halts and automated incident tickets, preventing bad data from propagating downstream.
• ML-Based Anomaly Detection: Monte Carlo Data Observability was deployed across all critical datasets, learning baseline distributions for volume, schema, freshness, and field-level statistics. Anomaly alerts fired within minutes of deviation, giving engineering teams time to investigate and remediate before data reached reporting consumers. Coverage extended to 150 tables across eight data domains.
• Data Lineage and Impact Analysis: End-to-end column-level lineage was established from source systems through to regulatory report outputs using dbt and Monte Carlo's lineage graph. When an upstream data issue was detected, impact analysis identified all affected downstream reports and consumers within seconds — replacing the manual impact assessment process that had previously taken days.
• Incident Response and Governance: We defined standardized data incident severity classifications, response playbooks for each severity level, and escalation paths for regulatory-impacting incidents. A data quality scorecard by domain was published to a Grafana dashboard reviewed weekly by the CDO and regulatory reporting leadership.

The Impact: From Reactive Firefighting to Proactive Quality Management

Within three months of full deployment, the data engineering team reported a fundamental change in how they experienced their working week — from constant incident response to planned, predictable delivery work.

The bank submitted its next CCAR stress test with no data quality findings for the first time in four years. Regulatory examiners noted the observability framework as an example of leading practice in their examination report. With firefighting time reduced from 40% to 8%, the engineering team delivered five high-priority data products in the subsequent six months — output that would have been impossible under the previous operating model.

Services Delivered: Data Quality & Observability, Data Pipeline & Integration, Data Engineering & DataOps, Data Governance & Compliance

Technologies Used: Great Expectations, Monte Carlo Data, dbt, Apache Airflow, Snowflake, Grafana