For most organisations, issue management is seen as an administrative chore. Scattered across the organisation, data workers diligently resolve issues often via their own local issue management process.
With silos of data comes silos of maintenance, and this is a real shame because the data these systems possess is a vital tool in your data quality armoury – but only if you can pool this resource.
Another problem with conventional data defect management is that it is often a wasteful activity. I once met a diligent data analyst who worked tirelessly for several months fixing data issues in a data warehouse, only for the same issues to be repeated week after week. Such is the nature of upstream data defects. They will continue to flow downstream unless someone sees the bigger picture.
Data quality leaders need to have visibility of this big picture. They need to understand the various issue management systems in their scope of control and develop a transitional strategy to get them onto the data quality radar. Whilst the data quality team may not be on the front-line for issue management, they still need to have visibility of the metadata surrounding these issues because it can help them dramatically improve the impact that data quality management has on the organisation.
Quite often it only requires simple changes to the issue management system to make it more relevant for data quality. For example, adding attributes that clearly identify the system, user, application function and performance metrics can help you make more sense of where issues are being created and why.
It’s also critical to start adding cost-specific information to the issue management process.
In one company we hooked up the issue management system to payroll information so we could start to highlight the high costs in repeatedly fixing issues. It also helped us prioritise which issues impacted the most expensive staff.
If you have a data quality tool platform in place, you can go one step further and start to build data quality monitoring rules to detect whether specific types of issues are finally resolved. Defects can be complex in nature, often spanning many systems, so having a data quality tool to work in tandem with the issue management process is advisable. If you can give your issue management team access to the data profiling and data discovery aspects of the tool, all the better as they’ll become far more effective.
Over time, data quality leaders should work with the issue management teams and system owners so that faster resolution and longer-term defect prevention becomes a reality through far greater data quality enforcement.
How are you approaching data issue management? Have you incorporated data quality metrics into your existing issue management systems? Are you integrating issue management systems into a consolidated view of data quality performance? Welcome your views below.
