NOV 1, 2012 7:03am ET

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Are the Dimensions of Data Quality Real?

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A recurrent theme in data quality is the idea of "dimensions," such as accuracy, consistency and timeliness. The fact that these dimensions actually exist is rarely, if ever, questioned. Indeed, the benefits of the dimensions are regularly discussed, and are commonly thought to include:

  • Allowing the complex area of data quality to be subdivided into areas, each of which has its own particular way of being measured;
  • Being able to correlate dimensions with specific impacts on business areas;
  • Having specific remediation approaches, rather than a one-size-fits-all methodology.

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Comments (8)
A similar topic regarding "What Dimension of Data Quality Is Most Important" recently appeared on a LinkedIn forum thread. For sometime I have advocated that accuracy, completeness, validity, consistency, timeliness, yada yada are all "qualities" of data, not necessarily measures of "data quality," albeit important qualities, perhaps proxies of, but not data quality. Data can have all of these "dimensions" in spades and still not be high quality.

Without someone of importance wanting and then doing something useful with the data, the data will not have "high" quality. For a primer of sorts on this point, watch the movie "Dead Poet's Society" to get the difference between data quality and qualities of data. Contrast the character John Keating - played by Robin Williams - and his definition of quality. Keating rips out a section of the fictitious Pritchard's text on poetry - and the "Pritchard Scale" a measure of a poem's "quality."

In the end, any so-called data quality dimension is only reasonably "governed"...once the data itself is determined to be valued and useful to start with.

Posted by Peter P | Thursday, November 01 2012 at 12:50PM ET
I would say that the term Dimension in the context of Completeness; Conformity; Consistency and Coverage is not the appropriate term. I would say that it is more Realms of the data within their Container - in the universe that they exist and are related.

Dimensions of the data, particularly in the Data Warehouse models are the context of the data such as: Time; Geographic Region; Calendar days, etc... not whether they are Complete or they are Consistency or whether they are volatile. There are data, for some applications, hat exist for an instant; they cannot be stored because they are unreliable over time. Take for example, storing someone's age; when stored it has to be recalculated and updated if plays a critical role in its application. It could mean that you qualify for a Senior's benefit - 65+ or you're not there yet.

Posted by Constantine L | Thursday, November 01 2012 at 12:51PM ET
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