The Data Governance Podcast Data Quality Rules and Reporting with Daniel Donahue
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Aug 26, 2025 In this engaging discussion, Daniel Donahue, co-founder of DQ Pursuit, dives into the crucial world of data quality. He shares insights on how financial institutions evolved their data governance frameworks during economic downturns. Daniel also emphasizes the importance of clear visions when implementing data quality rules and managing change. He explores the balance between data quality management and AI integration, addressing challenges like underutilized data and budget constraints, all while promoting the significance of data safety and integrity.
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Banks Are Data Businesses
- Banks are fundamentally data businesses because core products are information-driven rather than physical goods.
- Focusing governance on high-value, high-risk processes yields the most impact for financial institutions.
Begin With High-Value Processes
- Start by identifying high-risk or high-value business processes to bring under data quality management instead of trying to fix everything.
- Use a consistent standard to determine criticality so regulators and stakeholders see a defensible approach.
Map Ownership And Downstream Impact
- Data quality work often fails without a clear three-year vision and mapped business ownership across many-to-many data relationships.
- Knowing who owns upstream data and who relies on downstream processes is essential to manage change safely.