Why Data Quality Is So Hard to Get Right

Vipin Kumar, Head of CUSO IB Data Strategy and Analytics at Deutsche Bank, joins me to unpack one of the toughest problems in financial services: managing data quality in a highly regulated industry. From the outside, it might look like a box-checking exercise. In reality, it’s a complex mix of legacy systems, global frameworks, regulatory controls, and the constant push to balance defensive compliance with offensive business value. Vipin makes it real with examples that connect directly to how we all experience data in daily life.

Key Takeaways

Data quality isn’t just about accuracy—timeliness, completeness, and consistency all matter, especially when billions are on the line.

Regulations push banks into “defensive” strategies, but there’s growing opportunity to apply “offensive” strategies that use data for prediction, analytics, and competitive edge.

Measuring effectiveness requires agreement between data producers and consumers, with preventive and detective controls working together.

AI and machine learning are starting to automate checks, spot patterns, and even strengthen anti-money laundering defenses.

Timestamped Highlights

00:45 What data quality means in a regulated industry

03:15 The challenges of managing fragmented legacy systems

06:40 How producers and consumers measure effectiveness of frameworks

09:30 The pizza delivery analogy for making sense of data quality

14:20 Why accuracy is harder than timeliness or completeness

16:50 The role of AI and machine learning in improving governance

19:20 Shifting from defensive compliance to offensive strategy in banking

22:40 Regulators testing AI-driven approaches to anti-money laundering

Memorable Quote

“Producer has preventive controls. Consumer has detective controls. True data quality happens only when both align 100%.” — Vipin Kumar

Call to Action

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