By: Dan Myers (DQmatters.com)
After attending a number of data management conferences where I heard a number of professionals use terms such as Integrity, Accuracy and Currency/Timeliness to mean different things, I began to look for a detailed and agreed upon standard set of definitions for the dimensions of data quality. What I found was a few great academic studies on the topic that have proposed a list of dimensions1, a series of author’s lists2, and a few sets of dimensions documented by professional organizations3. What I discovered was that no two sets of dimensions were in agreement on what should be included within a set of dimensions and most lacked complete and verbose descriptions.
In June of 2013, I wrote a series of six articles reviewing four authors and two organization’s list of the dimensions of data quality. This effort did reveal similarity in some areas, Completeness and Validity and the need for a conformed version that brings together the best of each, called the Conformed Dimensions of Data Quality (CDDQ). In 2016, I enhanced my conclusions made during the prior work and published them at http://dimensionsofdataquality.com/ for others to review, leverage during future research/study, and most importantly use on a day-to-day basis to communicate data quality issues.
Since the release in 2016, I have presented on this topic at conferences in the USA, Japan and individual organizations around the globe. I also conduct an annual survey4 on the topic of the dimensions of data quality in order to foster further development and understanding. I owe a debt of gratitude to Danette McGilvray, Laura Sebastian-Coleman and Tom Redman for their input on the definitions and explanation of their own works on this topic over the years. My expectation is that this article will stir you to: discuss this topic among your peers, consider writing on this and related topics for the IQ International Journal, and contribute to the Conformed Dimensions going forward.
Initially, before considering publication of this work, the primary purpose for conducting this comparison of the CDDQ to the ISO/IEC 25012:2008 Data Quality Model’s Dimensions of Data Quality was to identify how comprehensive the CDDQ is if used as an organizational standard and even an industry standard. After my review, it is clear to me that the CDDQ is robust and should be considered a candidate for use as an organizational standard and consideration for the basis for an industry standard.
Read this article in the December 2017 IQ International Journal
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