How effective is your data governance program

In this, the conclusion of my blog series on “Measuring data governance programs,” I’ll be exploring the need to measure data governance process performance and techniques you can use to ensure that those processes are operating optimally.

The last few posts expressed the need to measure data governance programs from various perspectives to ensure that the program was having its intended effect on the enterprise. Is data quality improving (Data Quality Monitoring – Looking beyond the obvious)? Has the level of compliance to data policies increased (Data Policy Compliance)? Have we seen the requisite improvement in business performance that we expected due to higher quality data (Impact of Data Governance on Business Performance)?  But, what about the data governance process itself?  It’s time for some introspection.

Effective, enterprise data management can only be driven by a robust and structured data governance program. Without that program – or with a sub-optimal data governance program – your organization ends up behind the eight ball. If a business data issue is raised, and the data governance operational team does not investigate, rate, prioritize and implement a policy to address the issue in a timely manner, the organization bleeds costs, misses out on revenue opportunities or bears unnecessary risk which could result in fines or even jail time for company executives. Conversely, if you implement an agile business process to rapidly address new data issues or change existing policies, you gain competitive advantage with your ability to reduce operating costs, take advantage of dynamic market conditions and mitigate risk by ensuring regulatory compliance requirements are met.

We all understand the need to manage performance of our business processes. Strategic KPIs, such as “Days Sales Outstanding” or “Inventory Turns” rely on the output from well-managed business processes to ensure they are calculated accurately and reported correctly.  Supply chain processes, financial processes and manufacturing processes are all managed and continuously evaluated to ensure that they are operating in a predictable manner. When we treat data governance as the core business process that it truly is (“the business process of defining, implementing and enforcing data policies” by the Kalido definition), we put it in the class of these other core business processes. In that frame of reference, we can begin to consider some standard process measures such as cycle time, throughput, resource utilization and wait times. These basic measures can tell us a lot about process performance. Of course that means we have to measure them!

When we look at successful (and scalable) organizations, we see a high degree of automation in support of their core business processes. So, why are companies still trying to run one of their most fundamental processes, data governance, manually? The structure and efficiency that come with operationalizing a business process allows companies to follow a prescribed path and execute in a predictable manner. The ability to capture data about the execution of a process opens up the door to measurement, assessment and optimization. Inspecting the “who did what and when” in regard to policy authoring or change management brings visibility to how well your data governance process is being executed, much like tracking the financial close process at the end of a quarter. What’s the longest part of the process? Where can it be streamlined? Are there non-value-add tasks within the process? Where are the bottlenecks, and how can we avoid them?

Any process improvement initiative requires accurate data. That holds true whether you are trying to improve your ability to introduce a new product to market or to produce a new data policy to ensure that your marketing campaign process has the right data to operate effectively. But, you can’t manage what you don’t measure. When it comes to core business processes – and your data governance processes are core to your business – measure early – measure often. Automate the parts of the process that can be automated. Use purpose-built applications such as the Kalido Data Governance Director to provide structure, automation and measurement capabilities and continuously evaluate and improve your data governance process performance.

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