Common Enterprise Wide Data Governance Issues: #5 There is little understanding of what “Data Quality” means

This post is one of a series dealing with common Enterprise Wide Data Governance Issues.  Assess the status of this issue in your Enterprise by clicking here:  Data Governance Issue Assessment Process

When asked what does ‘Data Quality’ mean, senior management respond along the lines of ‘the data is either good (accurate) or bad (inaccurate)’.  There is little understanding of the commonly used dimensions of data quality.

  • Completeness
    Is the data populated ?
  • Validity
    Is the data within the permitted range of values ?
  • Accuracy
    Does the data represent reality or a verifiable source ?
  • Consistency
    Is the same data consistent across different files/tables ?
  • Timeliness
    Is the data available when needed ?
  • Accessibility
    Is the data easily accessible, understandable and usable ?

Impact: Without a shared understanding of what “Data Quality” means:

  • It is practically impossible to have a meaningful discussion about the existing and required data quality within an Enterprise.
  • Senior management are not in a position to request specific Data Quality metrics, and if you don’t measure, you can’t manage.
  • Business users are not in a position to clearly state the level of data quality they require.

Solution:
Agree and implement the following policy:

In discussing Data issues and requirements, data quality will be assessed using a standard set of quality dimensions across the Enterprise.

Your experience:
Have you faced the above issue in your organisation, or while working with clients?  What did you do to resolve it?  Please share your experience by posting a comment – Thank you – Ken.

Update:  
In October 2013, following an 18 month consultative process, DAMA UK published a white paper explaining 6 primary data quality dimensions.

1. Completeness
2. Uniqueness
3. Timeliness
4. Validity
5. Accuracy
6. Consistency

For more details see my blog post, Major step forward in Data Quality Measurement.