Session Description

We all recognize that high-quality data is essential for driving value in analytics, AI, and business operations. Yet, in reality, not all data is good—some is bad, and much of it is just plain messy. While organizations acknowledge the importance of data quality, choosing the right approach to improve it remains a challenge. How can you systematically turn messy, unreliable data into a trustworthy asset?

In this session, we take a fresh approach to data quality management. Instead of tackling issues in isolation, we start downstream—examining the real-world symptoms of poor data quality as experienced by customers and employees. From there, we trace problems back to their upstream root causes and explore practical solutions to address them. However, resolving data quality issues is rarely straightforward—it’s like battling a seven-headed beast, where fixing one issue often reveals several others. To tackle this effectively, we introduce data quality management strategies tailored to different levels of organizational maturity.

What You’ll Learn:
✅ The Data Quality Triangle: Symptoms, Root Causes, and Solutions
✅ Why solving data quality issues feels like battling a seven-headed beast
✅ Practical data quality management strategies for different maturity levels