Many businesses focus on data elements that are easy to see and understand like company and contact information. However, as obvious as some of these elements may be, they don’t always lead to the highest bang for the buck. Data elements have priority levels within processes depending on the desired business outcome. In addition, data elements have dependencies outside of how the information comes into the system. You need to take this into account as you conduct your business analysis and map your data across your business processes.
During business analysis it pays to establish a foundation that validates recommendations and shows ROI through case studies. You can do this through data analysis and pilot programs. Data analysis can be applied through meta data segmentation within processes where you look at the existing state of the data. You can also improve portions of the data and perform the segmentation and analysis.
These steps will prepare your case but will also help establish dashboards to allocate resources for future projects.
1) Identify the processes you think are most impacted by poor data quality. The processes should be tied into key business functions. For instance, in marketing you may want to look at lead qualification and management. Processes that are well defined and have a tangible link to businesses objectives work best as they are most likely mature and revenue has been tied to them.
2) Pinpoint smoking guns in the processes. There are bound to be several points in a process that are key indicators of success where data quality has negatively impacted the outcome. Your business analysis will or should show this clearly. These smoking guns should be called out clearly in the processes. What you should determine is which data elements are impacting the most and can be easily focused on or addressed.
3) Select data quality issues that you can segment the process into influence tracks. This step is critical to measurement. You need to dissect the process to create scenarios of what good vs. bad looks like in process outcomes. In the lead management process suggested earlier, it could be the point where you would qualify a lead to move into the sales pipeline.
4) Measure performance success with good quality vs. poor quality data. At this stage you should be able to run an analysis that shows the difference in process outcomes and performance when you run scenarios between good quality data and poor quality data.
The real benefit is that at this stage you’ve provided the dashboard to measure improvements to the business. Rather than wait until the data quality projects are completed, this provides the foundation for predicting where you will get the most impact from your investments. Instead of focusing solely on metrics that measure the completeness, accuracy, and uniqueness of records, you can focus on how these metrics within processes influence business outcomes. Now you have a case for linking data quality with ROI.
