Data should not be good for goodness sake


As this is the week of Christmas, many, myself included, have Christmas songs stuck in their head. One of these jolly jingles is Santa Claus Is Coming To Town, which includes the line: “He knows if you’ve been bad or good, so be good for goodness sake!” The lyric is a playful warning to children that if they are not going to be good for goodness sake, then they should at least be good so that Santa Claus will bring them Christmas presents.

This, like most things, made me think about data quality. Should data be good for goodness sake? Clearly good data quality is preferable to bad data quality, and if data is not of good quality then it will not be much good to the organization. This line of reasoning often motivates the organization to strive to maintain the best data quality possible with the same zeal of children being on their best behavior in order to get Christmas presents.

But before you worry about whether your data quality is naughty or nice, you should verify your data usage.

Improving data creation processes and implementing data cleansing routines fights the good fight for good data on both fronts, ensuring high-quality data is created and correcting data quality issues in data that was created externally. The latter is becoming increasingly common in an era dominated by big data and the cloud. Good data is essential for good business. However, good data comes at a cost. And paying that price for data that’s not being used, or that’s not being used anymore, or that you’re not sure will be used, is bad business.

Santa may know if you’ve been bad or good, but your data should not be good for goodness sake.


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Jim Harris

Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ)

Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.

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