By Elliot King
Or a problem may have percolated to the surface. Perhaps a direct mail campaign failed to yield the anticipated results or customer service representatives find themselves with incorrect information during critical interactions. So what do you do then?
With most rude awakenings, people want to act right way. After all, the data is broken, so let’s get it fixed. With data quality, however, the impulse to act immediately may be a mistake. Indeed, the first question to ask is, does it really matter? The sad fact is that we live in a world of inaccurate and incomplete data.
Data sets will never be perfect. Inaccurate data may have little or no impact on ongoing processes and the investment required to remediate the data may be more than the return better data will provide. Identifying the impact of the data quality is essential. Have the problems resulted in lost revenue? Has customer service been compromised? Have the issues driven up costs? And so on.
Once the impact of the problem has been isolated, the next step is to better understand the nature and scope of the problem. What are the processes through which incorrect or poor data is entering the system? As most data professionals know, often data problems have more ways into your system than a freeway has on-ramps. Can the sources of incorrect data even be fixed? If they can, how much investment will be required and how much improvement can be expected? Finally, what will be the expected return on investment?
Though it seems a little counter-intuitive and perhaps even a little uncomfortable, the first step after data quality issues are discovered is to think. You may not want to act at all.