A Data Quality Strategy: On Everyone’s Wish List
One would think when Big Data comes together with contact data; when offline and online data are integrated; when algorithm performance and CMO dashboards and marketing automation triggers all become vitally important; when unstructured meets structured information in the customer file; when customer centricity, preference and engagement become priority; and when we hold up the marketing databases as the center of customer truth, that DQ would demand its rightful place atop enterprise and brand investment.
Well, finally, perhaps it has.
I appreciate eMarketer — and Experian, Loudhouse Research, and Ascend2 — giving this topic necessary attention.
Veterans of customer relationship management, database marketing, segmentation and attribution models, and the like have well understood that a data-driven marketing strategy is useless without a data quality strategy as its lead component. Everything hinges on clean, accurate, timely data.
We all may know the shibboleths and variations: garbage-in, garbage-out; what you don’t know can hurt you; collect what you need and nothing more. It’s incumbent on all of us to recognize that as we crave the right data of our prospects and customers, we need to be able to stage, parse, score and correct it — before we analyze, apply and move it into the customer file.
Is your current data quality strategy keeping up? Take some time to read up on data quality software solutions in the marketplace. Gartner offers this periodic report (just announced 2016 report for sale here – its 2015 report freely available here); Forrester’s version (December 2015) is freely available through Oracle here. These reports offer expert observations on marketplace application and trends.
And I wish for everyone clean data this holiday season — and a data quality strategy every day of the year.