Major step forward in Data Quality Measurement

How tall are you?
What is the distance between Paris and Madrid?
How long should one cook a 4.5Kg turkey for – and at what temperature?

Quality data is key to a successful business. To manage data quality, you must measure it


We can answer the above questions thanks to “standard dimensions”:

Height: Metres / Feet
Distance: Kilometres / Miles
Time: Hours & Minutes
Temperature: Degrees Celsius / Farenheit

Life would be impossible without the standard dimensions above, even though the presence of “alternate” standards such as metric Vs Imperial can cause complexity.

We measure things for a reason. Based on the measurements, we can make decisions and take action. Knowing our neck size enables us to decide which shirt size to choose. Knowing our weight and our waist size may encourage us to exercise more and perhaps eat less.

We measure data quality because poor data quality has a negative business impact that affects the bottom line.  Rectifying data quality issues requires more specific measurement than anecdotal evidence that data quality is “less than satisfactory”.

The great news is that 2013 marked a major step forward in the agreement of standard dimensions for data quality measurement.

In October 2013, following an 18 month consultative process DAMA UK published a white paper called DAMA UK DQ Dimensions White Paper R3 7.

The white paper lists 6 standard data quality dimensions and provides worked examples. The 6 are:

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

The dimensions are not new. I referred to 5 of them in a blog post in 2009 There is little understanding among senior management of what “Data Quality” means.
The good news is that this white paper pulls together the thinking of many DQ professionals and provides a full explanation of the dimensions. More importantly, it emphasises the criticality of assessing the organisational impact of poor data quality. I include a quote below:

“Examples of organisational impacts could include:
• incorrect or missing email addresses would have a significant impact on any marketing campaigns
• inaccurate personal details may lead to missed sales opportunities or a rise in customer complaints
• goods can get shipped to the wrong locations
• incorrect product measurements can lead to significant transportation issues i.e. the product will not fit into a lorry, alternatively too many lorries may have been ordered for the size of the actual load
Data generally only has value when it supports a business process or organisational decision making.”

I would like to thank DAMA UK for publishing this whitepaper. I expect to refer to it regularly in my day to day work. It will help me build upon my thoughts in my blog post Do you know what’s in the data you’re consuming?

Hopefully regulators worldwide will refer to this paper when considering data quality management requirements.

Some excellent articles / blog posts / videos referring to this whitepaper include:

Nicola Askham – Data Quality Dimensions

3-2-1 Start Measuring Data Quality ()

Great Data Debate (2) Danger in Dimensions, Kenneth MacKinnon

How do you expect this paper will affect your work? Please share your thoughts. 

2 thoughts on “Major step forward in Data Quality Measurement

  1. Pingback: Data is the new oil – what grade is yours? | Ken O'Connor Data Consultant

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