When Data Quality Goes Wrong…
Whether you are a data steward or not, we’ve all experienced the unfortunate consequences of data quality gone terribly awry. Multiple catalogues to the same name and address. Purchasing a product through an online retailer only to find you have three different accounts with three different user names. Long, frustrating phone calls with customer service who can’t help you because they don’t have access to all the relevant info.
As the Director of Marketing for a data quality company, it brings me exceptional pain to see bad data quality in action. Such inefficiency is what gives marketing a bad reputation. It can ruin brands, destroy customer loyalty, waste opportunities and…let’s face it, it also kill trees.
Indeed, throughout our entire company, the water cooler occasionally buzzes with stories of bad data quality. So what’s a data quality company to do with all these DQ “blunders”? Call them out!
So this summer we’re going to dig through our box of examples and showcase a few #dataqualityblunders. We’ll try to be nice about it of course but the important part is that we’ll also highlight the ways that a good data quality strategy could have addressed these indiscretions. Because where there is bad data, there is also a clean data solution.
Have a #dataqualityblunder you’re just dying to spill?
We know that you’ve seen your fair share of data quality blunders. Send them in and win a $10 Starbucks gift card! Just email email@example.com!