The metrics that define data quality
As technology “buzz phrases” go, data-driven has to be one of the most prevalent of the past 20 years. It’s a lofty ambition, but like so many technology-related panaceas of the past, it is far easier in theory than in execution. One major reason for this is that most enterprises are completely unaware of the quality of their data. If you want to be data-driven, you must have quality data. If you don’t know how good, bad or ugly your data is, you can’t fix it. Anyone attempting to fix data without first assessing the damage and then assigning the appropriate metrics and benchmarks isn’t going to get very far. One of the first experiences our customers have is exposure to the quality of their data – warts and all. We use up to 19 different metrics to determine data quality, and the results can be jaw dropping.
When they first see their data quality scores, most customers ask us what dictates accuracy. Accuracy is open to interpretation, and our quality scoring system will deliver our interpretation of it. Of course, we allow you to change or extend this, but based on the work we have done so far we believe that it is a sold place to start.
Here’s a brief description of all19 metrics available to measure data quality...