How To Measure Data Quality - Issue 185
Does data quality have ROI? Ways to measure and quantify data governance metrics
Welcome to the Data Analysis Journal, a weekly newsletter about data science and analytics.
Typically, I avoid data engineering and data governance topics in my newsletter. While the success of analytics is directly linked to data governance initiatives, measuring data quality often falls outside the primary responsibilities of data scientists. Also, the subject of data quality is well-covered today, and there is no need for yet another how-to-improve-data-quality publication.
However, a few weeks ago, during the ThoughtSpot webinar on data trends and data quality, a question arose about whether there is a common criteria for defining poor-quality data vs. good-quality data. To my surprise, Sonny Rivera's response was that (a) there is no industry standard to define criteria for data quality, and (b) it has to be “good enough.”
I respectfully disagree with (a), and the only context where I’d settle for “good enough” is when I am preparing my homemade fajitas.
That being said, I haven’t seen actual KPIs related to data quality. So, I embarked on a quest to find data quality metrics and KPIs I could use to scale the data governance initiatives and measure data quality. Below, after long hours of research, I share what industry leaders have to offer on this subject and my consolidated list of the top metrics you can use to measure data governance ROI and the state of data quality.
What data quality experts offer
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