Data quality is abour fitness for purpose

A series of posts by Henrik Liliendahl Sørensen, most recently this one, have posed questions to the common definition of quality data as being data “that is fit for purpose.”

Henrik’s concern is that this definition may allow data to be of acceptable quality for one application, but may not be easily adapted to meet additional purposes at a later date. At some point, he suggests, it will become more cost effective to simply model the real world object, rather than have to constantly adapt it to address new uses.

“Fitness for purpose” is a legal term prescribing that something is good enough to do the job it is intended to do. The principle of “good enough” is critical to our goal of managing data by value – it is not cost effective to over-engineer a solution.

If we are building a house, it is reasonable to agree that it should be fit for purpose – have a bathroom, a kitchen, bedrooms, be water tight, heated, have running water, etc. Depending on the size of my family, my budget and my specification the final building may vary from person to person.

But in very few cases would the modern family home include a manufacturing centre or a surgery. It would not be pragmatic to plan for every possible use of a building, nor would it be cost effective.

Similarly, when working with data we need to balance what is cost effective against what is necessary.

Where I do agree with Henrik is that poorly thought, tactical solutions may not scale to meet enterprise needs. At some point, as Henrik suggests, a break even point may be reached where an enterprise view of data may require a redesign of tactical data solutions – this does not conflict with the “fit for purpose” definition.

A value driven approach to data management seeks to leverage multiple uses of data across the enterprise to minimize costs and maximize reuse. Data governance principles can be used to plan projects to meet tactical goals cost effectively without compromising on the ability to address additional goals later.

But ultimately, the quality of data is in the eye of the beholder. There is no global benchmark to reach towards – if its good enough for your requirement then it is of good quality!

This post was first published on the dataqualitymatters blog!

One thought on “Data quality is abour fitness for purpose

  1. The term ‘Fit for purpose’ is a legal term, more relevant to Courts and Lawyers than to quantifying Data Quality. It is used as a test to settle a legal dispute, whereas data quality management is an ongoing process aimed at improving the quality (and thereby, the VALUE) of data.
    I agree that improving data quality has to bring real world benefits and not just be an end in itself.

    Nor is the term ‘good enough’ an appropriate way of viewing data quality. “Good enough” suggests a minimum acceptable quality level; a level aimed at mimimising costs rather than maximising value.
    The more relevant question is: “Can we realise benefits through improving data quality, that are worth more than the investment costs”?

    If the expected benefits outweigh the costs, we should improve the data quality.

    The ‘fitness’ of data (or ‘value of data’ as I prefer to view it), is not directly measurable; it is an interpretation of the metrics objectively measured across all data quality dimensions, (Completeness, Validity, Conformity to Business Rules, etc.), using standard data quality software. Only when the results of these measurements have been evaluated, and a cost/benefit analysis of possible improvement actions has been made, can a view be taken on whether the data is ‘good enough’, or not. (i.e. the anticipated benefits are not worth the investment costs).

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