The effort to maintain data quality is a constant, never-ending struggle. No matter how much time and money businesses sink into the endeavor, they will never be able to achieve 100 percent success - there will always be new problems to solve. That being the case, if any data specialists are pessimistic, they will always have something negative to fuel their fire.
But according to data quality specialist Jim Harris, there's a need for more positivity regarding companies' data initiatives. If people only look at the negative aspects, they will lose sight of the reasons for improving their data in the first place, but if they're more optimistic, it will inspire them to keep working for more quality in the future.
Harris believes that people's questions about data quality often fit one of two types - they're either negative inquiries like "Who's to blame for the 20 percent of our data that's wrong?" or positive ones like "How can we celebrate that the other 80 percent is accurate?"
"I am not suggesting that we abandon the first set of questions, especially since there are times when a problem-seeking mindset might be a better approach (after all, it does also incorporate a solution-seeking mindset - albeit after a problem is identified)," Harris wrote. "I am simply wondering why we often never even consider asking the second set of questions."
Celebrating specific successes
According to Information Management, the key to celebrating data quality success is to focus on specific endeavors that have gone well. If data analysts can point out the tangible results of their efforts, it will encourage their employers to continue embracing data-driven initiatives.
For example, has data helped reduce costs or increase revenues for the company? If so, it's important to illuminate the specific financial gains brought about by analytics. If using some data is making money, then it's reasonable to spend some of that profit on more data.
The results of data initiatives might come in less tangible, but still equally important, forms. For example, think of mitigating legal risk for a company. This is a laudable goal, but it's more difficult to measure. Companies should be willing to study their results carefully to find these hidden benefits of data analysis.
The shrewdest analytics departments are those that are willing to question their data from both angles - asking why it's not better, but also why it's not worse. True success comes from accentuating both the negative and the positive.
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