Making Sense of Data Governance ROI

Data governance has become a critical focus for any company that values data as an asset.  According to a recent Enterprise Benchmark Study, there has been a 140% increase, in just a few years, of companies who view data as a corporate asset.[1]  In addition, research and advisory firms such as Gartner call out that governance is a top priority now for Chief Data Officers.[2]

However, data governance poses a bit of a challenge to companies in terms of justification. While an organization may realize they need to adopt a governance plan, how do they go about proving the value to be achieved from it?

Most people involved with data governance today know that defining return-on-investment from their projects is not only the holy grail, but it is also one of the most difficult objectives to define and manage.  

In-depth research on this has uncovered a couple of metrics, various user experiences and a lot of hand-wringing. Mostly due to the fact that while the investment in either master data management or data governance solutions has been extensive, the returns have been ambiguous. 

To be fair, data governance is based on a combination of people, process and technology, not all of which lends itself to easy measurement. However, with some guiding principles and a narrow focus on key business goals, governance can be an incredibly useful and valuable tool to achieve and prove business success.

In order to make sense of data governance ROI, you must first come to agreement with all relevant stakeholders, on what those business goals are. Until the goals are stated, refined and tested to be measurable, the work to address ROI cannot be initiated. 

Key Data Governance Business Values

One way to begin your journey in data governance is to look at key business values that can and should be achieved through your governance programs, and that will result in business growth:

• Address areas such as sales pipeline, operating margins, partner and channel revenue, data accuracy and spending.

• Focus on inventory accuracy, inventory turns and cycle time, storage costs, data management costs, costs of resources and systems, redundant data and efficiencies in supply chain.

• Evaluate customer satisfaction, customer churn, loyalty, accuracy of customer communications and customer data, improve sophistication of customer interactions, reduce support costs and improve omni-channel programs and effectiveness.

• Improve productivity, focus on effectiveness in onboarding, recruiting, knowledge transfer, training and maintaining valuable employees. Evaluate reallocation of employee time to higher value activity and reduce hiring costs.

• Improve security practices to prevent breaches; address regulatory actions, penalties and financial misstatements, data failures and brand damage; optimize data for conformance to legal or contractual requirements.

Each of these business values can be measured, but the association between the metrics and governance activities is key. In other words, just stating a higher level of cash flow without directly showing the association to the governance activities that enabled it, would be misguided. 

If you examine each of these business values, it is clear that there is a common thread of data accuracy – but not just accuracy, instead it is actually “contextual data optimization” that is key to achieving business success.

Look to our future posts that will provide details on the key areas of focus, the suggested metrics and the expected results.