In a recent post on OCDQ, Jim Harris talks about how the availability heuristic causes business owners to ignore the impact of poor data quality. The post, about Acknowledging Errors in Data Quality, discusses how the human brain will take shortcuts with decision making based on the ease with which examples come to mind.
Studies show that listing multiple examples requires more thought, and avoid the shortcut that may cause business to write a specific data quality problem off as an isolated incident. In the worse case they will ignore it, or assume that this will be addressed using existing budgets.
I believe that this serves an additional important purpose. Once off data quality projects of the nature frequently approved by business (or sold by consultants looking to deploy manual fixes) typically provide only temporary relief, as they do not address the causes of the problem and data will revert to its natural state of chaos as discussed here.
If business people are asked to list numerous data quality issues then the broader impact of the problem should become more apparent. In my experience, many data quality issues are related and/or interdependent.
A top ten list is a good starting point for a business case for a more proactive approach. The real value, however, will come when analysing the responses of more than one person or business area. This should show broad trends across the business and facilitate the business case for managing data at an enterprise level – the fundamental of a pragmatic data governance approach.
A holistic approach to data quality management may start as a single project for a key business area, and, in most cases will pay for itself just from this initial requirement. By setting the scene for enterprise use you will maximise your investment in both technology and process by addressing multiple business problems over time.
So what are your top ten data quality issues?