Whether mandated by regulatory considerations, driven by executive dashboards, or meant to enable personalized targeting of marketing messages to consumers, the rapidly increasing reliance on analytics has made Data Quality a higher priority than ever before. In turn, this new status has reshaped the very meaning of Data Quality. There was a time when Data Quality really meant one thing: a simple, binary assessment of the accuracy of data. That was the beginning and end of the Data Quality discussion. Today, however, the questions have grown more complex.
From “Is my data correct?” to “What does my data actually mean?,” the questions surrounding Data Quality are undergoing a rapid transformation. This change has been driven by four major factors: