Impact of Data Governance on Business Performance

I’ll be continuing the discussion started in my post Measuring data governance programs in which I highlighted four distinct categories of measurement that apply to data governance programs: level of policy compliance, level of data quality, impact on business performance, and performance of data governance processes. Driving the greatest return on the investment in data governance is dependent on transparency and ongoing assessment of its impact across these dimensions.

As data governance programs and practices mature, the relationship between these four dimensions comes into focus.  If we subscribe to the concept that data and business processes are equal partners in optimized business performance, it becomes obvious that measuring both data quality and business process performance in concert are necessary to fully understand how an organization operates.  An enterprise that strives to constantly improve cannot afford to look at only one side of the coin.  The foundational knowledge of how processes produce and consume data as well as a clear understanding of the impact that data has on the execution of a process is imperative.

Once a company measures the quality of its data in the context of how that data is actually being used as it flows through the company’s core business processes, the company can then augment those measurements with some key process performance metrics that set the stage for a holistic view of corporate performance. By following this line of measurement to its completion, they are better positioned not only to more quickly identify when something goes off track, but also to take the appropriate action required to correct it. This change in reaction time delivers critical competitive advantage for the organization

Establishing business-oriented data policies as the cornerstone of your data governance program means defining, implementing and enforcing policies regarding the quality and usage characteristics of data in the context of the real world.  Data quality is not defined as a system-dependent characteristic about data.  Quality is a process-dependent characteristic about data.  Using this principle as a launching pad, we can now begin to assess data’s true impact on enterprise performance.

Without the tight linkage between data and process, there exists a fundamental disconnect between the transactions which fuel a business and the end result.  In many organizations today, someone at the executive level owns corporate KPIs.  For any given KPI, that KPI is the result of the execution of business processes owned by someone else in the company that may or may not even have a direct reporting relationship to the executive that owns the KPI.  The data used to fuel those business processes is owned by a more tactical role within the company and is produced somewhere upstream in the processes – yet another step removed from the people responsible for producing the result.  As an example, the CFO may have a KPI for “Days Sales Outstanding”.  That KPI is the result of several business processes such as the sales process, inventory and warehouse management, pick-pack-ship, invoicing and collections.  Each of these areas is owned by a different business process owner and is executed by completely different groups within the company.  What happens if the sales person enters an incorrect billing address?  The process breaks down; collections get delayed; Days Sales Outstanding increases, and your executive dashboard begins to show red instead of green – your KPI, my processes, someone else’s data.

A business process owner may receive reports on business process performance such as process cycle time, throughput, resource utilization or slack time, and the IT department may run data quality checks to determine whether or not a set of data elements is within defined tolerance.  Taken separately there is some value that can be determined. However, if we measure based on business data policies, and visualize data quality performance and business process performance side by side, we can understand the interrelatedness of data and process and take preventative steps to ensure that we are operating at peak performance.  Measuring process performance alongside the quality of the data that flows into that process enables us to assess the impact of data on that process.  When we identify the data and the quality threshold that have the greatest impact, we now have the basis for an early warning system.  If we can demonstrate the correlation and impact between the correctness of an address used for billing and the overall performance of our collections process, and then establish the subsequent reduction in the Days Sales Outstanding KPI, we suddenly have real economic value.  At what point would the IT data quality team and the business process owner conclude that by defining a relationship between the two sets of measures, they can prevent the process breakdowns that result from bad data infecting the business process?  This insight cannot be gained without assessing these different measures in combination.

This is where data governance programs can have a tremendous impact.  With data policies as the rallying point between business owners and data management practices, we now have the glue to bind the two disparate measurements together.  An assessment of data quality over time combined with the associated business process performance over time allows us to gauge impact, propose new process standards, and define alerts to intervene as early in the process as possible to prevent downstream inefficiencies.

Separating the two sets of measures is a common practice.  After all, we created a data management practice on the IT side of the house to manage data and assigned process ownership to operational line-of-business managers, shouldn’t they be measured based on their domains?  The answer is “yes”, but that doesn’t mean that the measure must be kept in isolation.

So, when your data governance operational group is defining policies, keep in mind more than just pure data quality metrics.  Know how that data is produced and consumed and develop the procedures for aligning business process and data policy compliance metrics.  This unified view of enterprise performance will enable you to better understand the impact of data on business processes, and ensure that your data governance program is a sustainable, high-value piece of your overall corporate performance strategy.

In my next post, the conclusion of my measurement series, I’ll discuss measuring the data governance process itself.

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