Problem

After establishing a foundational Data Catalog for Critical Data Elements (CDEs), the private equity firm still lacked a way to measure the reliability of those elements. There were no automated data quality checks, no monitoring process, and no structured way to identify or resolve data issues.

As the business began using the catalog, they realized they needed a repeatable mechanism to validate the accuracy, completeness, and consistency of the CDEs they depended on for investment analysis, financial reporting, and portfolio insights. The organization needed a lightweight, scalable Data Quality Program that aligned with the CDEs defined during the catalog enablement.

Approach

• Developed a Python‑based Data Quality engine that executed business rules.
• Designed rule logic directly around the cataloged metadata, ensuring each rule aligned to a specific CDE definition, business rule, or data expectation.
• Automated the execution of DQ rules, enabling consistent monitoring of priority fields without requiring enterprise tooling.
• Held weekly Data Quality review sessions with Business stakeholders to:
  • Review issues detected by the rules
  • Validate whether issues were true data defects or expected business scenarios
  • Refine existing rules based on business feedback
  • Identify and design new rules as the business matured
• Iteratively expanded the rule library, allowing the program to evolve as the business gained familiarity with data quality concepts and saw the value of proactive monitoring.
• Aligned DQ findings back to the Data Catalog, reinforcing ownership, stewardship, and the importance of well‑defined CDEs.

Outcome

• Delivered the firm’s first Data Quality Program, tightly integrated with the CDEs defined in the Data Catalog.
• Enabled automated monitoring of high‑value data elements using a lightweight, Python‑based rules engine.
• Improved data trust by giving the business clear visibility into data issues, their root causes, and their impact on reporting and analysis.
• Established a weekly governance rhythm, strengthening collaboration between Business stakeholders and data stewards.
• Created a scalable rule development process, allowing the business to continuously refine and expand data quality coverage.
• Positioned the organization for future maturity, including stewardship workflows, issue management processes, and integration with more advanced DQ tooling if needed.