Data quality was once a little-known field that was a fledgling part of the internet age, according to Dylan Jones in a recently post for The Data Roundtable blog. In the early 90s, it was still exciting to receive emails, he writes, and many people were not yet aware of the importance of cleansing the information in their databases. Now, analysts have developed a community that continually addresses the importance of data quality and provides tips for implementing best practices.
However, Jones explains that like any important change, improving data quality requires that users fundamentally alter their behavior. True adaption takes place one habit at a time, he advises. Moreover, companies need accountability. People must be held responsible for the accuracy of information through every stop of the process.
To ensure they understand the significance of strong data quality, advocates must demonstrate the value that can be gained by dutifully cleansing information, according to Gartner analyst Ted Friedman, who recently spoke with IT Business Edge. Perhaps more effectively, they can demonstrate the losses that are incurred if data quality falters, Friedman adds. Losses in efficiency and productivity are two of the biggest consequences, while others might be more affected by higher risks and lost opportunities for growth.