Recently I had a discussion on the question if the goal of data management would be to achieve good data quality or if data quality is an enabler for the most different business purposes.
Data quality (DQ) is, in my personal opinion, an extremely interesting topic. When I started to learn about data quality more than twelve years ago, it was a sort of a mind opener to me. I learned about the different data quality dimensions and was able to associate the DQ dimensions to current data quality issues. Suddenly I was able to describe the data quality challenges and to define some metrics how to measure them. This was awesome. At this stage, yes, data quality was the ultimate goal for me. Because my task was to understand the data issues and I wasn’t really aware of the business objectives behind those data. Fortunately, this stage took only a short time.
After this I was confronted with business challenges. Those challenges were mainly located in the area of sales or marketing. Or sometimes in more IT based issues like data migration and data integration. There, the goal wasn’t to have good data quality. The goal was, to make better business decisions, to increase efficiency, to reach the right customers as well as more customers, to provide the right content, to integrate data properly to add value to master data, to ensure compliance with the GDPR and succeed in numerous other data related topics. In all these cases, data quality was an enabler to reach the business goal.
Another argument for data quality as enabler, instead of goal, is that with pure data quality no money can be made. Of course, bad data quality may lead to revenue loss. But because of good data quality alone no single euro or dollar is directly made. Data quality is expected, and most business processes are designed for perfect data.
Here’s how you can estimate the cost of poor data quality in 5 simple steps.
My opinion is that data quality is an enabler. Even more, it is THE enabler to succeed with data-based processes, decisions and business goals. It doesn’t matter which area, but good data quality is the key. The challenge is to understand the business goal and with this understanding to prepare relevant and necessary data to the required data quality level to fit its purpose of usage.
What do you think? Do you see data quality as a goal or an enabler?