There is a growing body of evidence to demonstrate that poor data quality (DQ) has a major economic and reputational impact on organizations.
There is a growing body of evidence to demonstrate that poor data quality (DQ) has a major economic and reputational impact on organizations. So it always strikes me as strange that when, as part of my day job as a DQ consultant, I talk to many DQ specialists many say the number one barrier to improving data in their organizations is proving the business case for action, adding that getting their business and IT people to support data improvement activity is constantly an uphill struggle.
So what’s going wrong? There are a plethora of reasons why businesses fail to accept cases for action and change, whether they are data related or not. Often these can seem to fly in the face of logic, but as always in decision making, logic is not the only determinant of outcome. There are many other factors – human and other – that can derail a good argument.
In the sphere of DQ improvement data professionals need to focus on a range of things to create a persuasive case, especially as many in organizations still regard DQ as something of a dark art. The most important thing to do is to make a direct link between DQ and business outcomes – how does poor data affect the attainment of strategic corporate goals? How does it relate to the operational efficiency of business processes? What organizational risks result from poor control and management of key data?
But this is not enough in itself. DQ specialists and advocates need to actively sell the benefits of improved DQ to a range of people within their organization, ranging from senior executives to front end employees. This is not an easy thing to plan for and do, especially as most DQ people have never formally been trained to sell. Yet despite this many have succeeded, so it can be overcome with the right strategy and execution.
At the end of the day DQ people must be able to relate their sphere of expertise and activity to the bigger and wider corporate picture. In DQ it’s essential to look beyond the data horizon.