When talking about data quality, it is usual to consider different aspects or ‘dimensions’ of data quality – validity, completeness, uniqueness, consistency, timeliness and accuracy. These six dimensions were agreed as the most relevant and representative of data quality as part of work led by DAMA UK that I contributed to in 2013 and published in this White Paper.

Some recent work and discussions suggest that there may be another dimension to consider – continuity. This blog post explores things in a bit more detail.

Continuity will not be relevant as a data quality dimension in all situations. Some situations where it may be relevant include:

  • Time series data – for example, temperature measurements taken every minute
  • Data on a progressively changing situation – for example, an evolving building design where rooms may be added, removed and resized across different iterations of the design
  • A complete history – for example, a full employment history in a CV or the complete record of the journey of a freight truck
  • An audit trail of transactions – for example, all purchases against an account and not just the final balance

Each of these circumstances will require relevant data quality criteria to test the consistency of the data:

  • A full set of temperature readings with no missing values
  • All versions of the design model, even those that may not have been submitted for formal review
  • Checking for gaps in dates between roles or sections of a vehicle route
  • A complete list of all the transactions against an account that, in total, represent the difference between the opening and closing balance on the account

It could be argued that continuity is the same as consistency, however, we believe that consistency is more about checking that an entity has the same representation across different systems, rather than determining whether you have a full ‘history’ of an entity. So continuity should, therefore, be considered as another data quality dimension.

Do you agree? What examples of data where continuity is important are you aware of?

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2 thoughts on “Continuity – the new data quality dimension

  • 22nd May 2019 at 3:26 pm
    Permalink

    Hello Julian,
    Interesting idea. Prior to it, that would generally have fallen in the facet of “Completeness”, would it not have?
    Martin Storey
    Perth, Australia
    mstorey@welldataqa.com

    Reply
    • 31st May 2019 at 1:41 pm
      Permalink

      Martin,
      Thanks for the comment. A comment I’ve made elsewhere to distinguish between the two relates to the fourth dimension – time!
      Completeness could be assessed at a point in time, irrespective of what has happened in the past
      Continuity is checking that you have, say, a full event history for an asset, or a full audit trail for a decision making process.
      They do relate to each other, but by prompting users to think about continuity, it may be a trigger to consider data quality assessments that they might otherwise miss.

      Reply

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