DQ-Tip: “There is no such thing as data accuracy…”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“There is no such thing as data accuracy — There are only assertions of data accuracy.”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“There is no such thing as data accuracy — There are only assertions of data accuracy.”

This DQ-Tip came from the Data Quality Pro webinar ISO 8000 Master Data Quality featuring Peter Benson of ECCMA.

You can download (.pdf file) quotes from this webinar by clicking on this link: Data Quality Pro Webinar Quotes – Peter Benson

ISO 8000 is the international standards for data quality.  You can get more information by clicking on this link: ISO 8000

 

Data Accuracy

Accuracy, which, thanks to substantial assistance from my readers, was defined in a previous post as both the correctness of a data value within a limited context such as verification by an authoritative reference (i.e., validity) combined with the correctness of a valid data value within an extensive context including other data as well as business processes (i.e., accuracy).

“The definition of data quality,” according to Peter and the ISO 8000 standards, “is the ability of the data to meet requirements.”

Although accuracy is only one of many dimensions of data quality, whenever we refer to data as accurate, we are referring to the ability of the data to meet specific requirements, and quite often it’s the ability to support making a critical business decision.

I agree with Peter and the ISO 8000 standards because we can’t simply take an accuracy metric on a data quality dashboard (or however else the assertion is presented to us) at face value without understanding how the metric is both defined and measured.

However, even when well defined and properly measured, data accuracy is still only an assertion.  Oftentimes, the only way to verify the assertion is by putting the data to its intended use.

If by using it you discover that the data is inaccurate, then by having established what the assertion of accuracy was based on, you have a head start on performing root cause analysis, enabling faster resolution of the issues—not only with the data, but also with the business and technical processes used to define and measure data accuracy.

 

Submit DQ-Tips

Please submit your favorite data quality tips via the DQ-Tips Forum Topic in the Data Quality Symposium.