Debunking Data Quality Myths

When 20 cars race at 213 mph, even the smallest error can have extraordinary consequences. That’s why drivers depend on “scrutineers” who check cars for safety and compliance before and during the race.

This ebook shows you how to spot bad data and debunks the 6 most common data quality myths.

As the total volume of data around the world continues to increase, so will the volume of bad data. Poor data quality costs companies an average of $15 million per year (and rising), according to Gartner.*

In the past, achieving better data quality was perceived as too lengthy and complicated. However, improvements to data quality tools and procedures have made solving the data integrity problem easier than ever.

Download this ebook to see how 6 organizations took proactive steps to reduce bad data and achieved great business outcomes.

Please fill out the form to receive the document via email.

*Harvard Business Review, “Only 3% of Companies’ Data Meets Basic Quality Standards,” Tadhg Nagle, Thomas C. Redman, David Sammon, September 2017


For information about our collection and use of your personal information, our privacy and security practices and your data protection rights, please see our Privacy Policy.