Problem

A major financial services organization needed a scalable, automated way to monitor the quality of prioritized data assets across its Azure Data Lake. Data quality checks were inconsistent, issue identification was manual, and business units had limited visibility into the health of the data they produced or consumed. Without centralized monitoring, data issues often surfaced late and required significant effort to diagnose and resolve.

Approach

• Designed and built a custom in‑house Data Quality Framework tailored to the organization’s Azure ecosystem, enabling automated monitoring of high‑priority data domains.
• Developed rule‑based data quality checks using Databricks Notebooks, covering completeness, validity, timeliness, referential integrity, and business‑specific logic.
• Orchestrated daily execution through Azure Data Factory, ensuring consistent, reliable monitoring across all critical datasets.
• Captured detailed pass/fail results for every rule, including exception samples, thresholds, and metadata for downstream reporting.
• Integrated with ServiceNow to automatically generate incident tickets for failed rules, routing issues to the correct business or technical teams for remediation.
• Built a Data Quality Dashboard for each Business Unit, providing real‑time visibility into data health, trends, and outstanding issues.
• Held recurring touchpoints with Business Units to review data quality performance, address cross‑domain issues, and improve upstream data processes.

Outcome

• Established the organization’s first enterprise‑wide automated data quality monitoring capability across the Azure Data Lake.
• Enabled daily visibility into data quality for all prioritized datasets, reducing manual checks and accelerating issue detection.
• Improved cross‑team collaboration through automated ServiceNow incident routing, ensuring issues reached the right owners quickly.
• Increased trust in shared data assets by giving each Business Unit a dedicated dashboard showing the health of the data they produce and consume.
• Strengthened the foundation for long‑term data governance by embedding repeatable, scalable DQ processes into the organization’s cloud data platform.