2025-06-17 –, Frannz Salon
Not all data is good—some is bad, and much is messy. Poor data quality affects customers, employees, and decisions. This session traces issues from symptoms to root causes and explores strategies to fix them. Managing data quality is like battling a seven-headed beast, but with the right approach, you can turn chaos into clarity.
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
Operations, Stories, People & Community
Level:Beginner
Jan Meskens is a seasoned data consultant with over a decade of experience in various data consulting roles. Through his consulting firm, Sievax, Jan has been pivotal in helping companies successfully integrate and implement data-driven strategies.
In academia, Jan shares his expertise with students at University College, where he teaches courses focused on artificial intelligence and data-centric topics. Beyond his consultancy and teaching, he actively contributes to the broader data community by writing insightful articles
on Medium and presenting on data-related subjects at numerous conferences, meetups, and workshops.
Holding a PhD in Human-Computer Interaction, Jan brings a unique perspective to the fields of data and artificial intelligence. His guiding principle is clear: making data usable and understandable for everyone within an organization leads to valuable insights and
outcomes.