In my blog post “Measuring data governance programs,” I discussed four distinct categories of measurement that apply to data governance programs: level of policy compliance (addressed in “Data Policy Compliance: Beyond Crime and Punishment”), level of data quality, impact on business performance, and performance of data governance processes. By measuring your program across multiple dimensions, you can better ensure that your organization reaps the greatest benefit from your investment in data governance.
To continue the discussion, I’ll focus on what we can learn by measuring data quality – beyond the blatantly obvious. Data quality measurement is certainly not a new concept. Formalized data quality monitoring programs (and the tools to support them) have existed for quite some time. So, why is it so important to measure quality in the context of data governance?
First, it’s important to understand that data governance is a far-reaching program that covers the entire business process of managing your enterprise data assets. Your data quality initiatives, along with your master data management and data warehousing initiatives, should (and eventually will) be subject to data policies and procedures spawned from your data governance program. As we see many organizations begin to adopt a structured, business-process approach to managing data, we see more and more silos of data management technologies coming together under the guidance and management provided by of data governance programs.
In examining what data governance actually does, we recognize that one core element is the ability of data governance to bring business meaning to data. If we relate that understanding to the concept of measuring quality, it becomes clear that we need to monitor and measure data within its business context to get a better understanding of how the quality of that data impacts enterprise performance. Data “at rest” has no real value. Value comes when data is used to fuel a business process or to enable decisions to be made. When data governance policies are implemented, and the resultant data quality monitoring capabilities deployed, we can now begin to assess the impact that data quality has on operational performance.
As an example, let’s say you know that your customer data is at a 92% quality level. But because you measured data quality within the context of the business processes, you gain more important insight: you know that the increase in the quality of customer data that flows into the “service excellence” business process is up from 74% to 92% in the two months since implementing new data policies. As a result, you have seen an increase in customer satisfaction ratings, which in turn has yielded a 5% increase in customer retention. Now data quality becomes real, becomes relevant, is easy to value and calculate a return on investment.
There is a lot to learn when we combine business process and data. Looking beyond the obvious (the initial output of DQ monitoring) by providing a business context to the results brings greater value and visibility than simply monitoring on its own. If we assume the same measure of a 92% quality level for customer data, but we don’t see a corresponding increase in customer satisfaction and resulting uptick in customer retention, then we may have uncovered a flaw in the business process. If we understand fundamentally that process and data are equally important parts of the foundation of enterprise performance, then we can act more quickly to correct any variance from predicted performance. A good process with bad data is equally as damaging to performance as a bad process with good data.
So, when you are planning your data quality initiative, consider the business impact that data has on the enterprise. This is readily achieved by bringing data quality into the sphere of influence of the data governance program. Data policies become the focal point to unite business processes, data management procedures and data quality metrics. Quality for the sake of quality is of unknown value. Quality in the context of business process execution is the key to optimal business performance and enterprise agility.
In my next blog, I’ll continue the theme of data’s impact on business processes by diving deeper into the third dimension of data governance measurement – impact on business performance.