Measuring Data Governance: Lies, Damned Lies, and ROI
The next facet on the tour of our data governance framework can be one of the most frustrating for those driving data governance efforts within their organization: Measurement. The concept of data governance has experienced a boost in credibility over the past few years. Many business leaders finally acknowledge the need to transform their organizations into information-centric entities to combat explosive data growth, complex regulatory edicts, and tackling emerging commerce channels like mobile and social. But justifying the rhetoric that data is a critical corporate asset is a whole lot simpler than justifying and prioritizing actual funding, headcount, IT investments and process transformations that the management and governance of this data requires.
In 2001 I met a VP of data management for a large global tech company who told me, “It took me three years and millions of dollars to recognize that data management is an enabler supporting the business, but [it] does not in and of itself reduce costs or deliver revenue.” This was an important a-ha moment for me – and I believe a necessary one for any data management professional. This realization helped shift my focus away from the data-centric perspective alone and encouraged me to really focus on the business processes, decisions and interactions that the data was enabling. This is where the value can be measured most effectively. I discuss tips on how to short list these business opportunities in more detail in my vision and business case post.
Earlier in my career, when I was in the driver’s seat responsible for measuring the value of my organization’s data quality and MDM investments I often joked about the phrase popularized by Mark Twain, “Lies, damned lies, and statistics.” As someone with a finance background, I liked to think everything was quantifiable one way or another, so was often disappointed that it was so difficult to truly quantify the value of high quality data. So what did I do? I quantified what I could and built as strong a story as possible with qualitative examples in other areas. This was occasionally perceived as “lies” to some and “damned lies” to others, but when done right – it offered a qualitative and compelling ROI story to the influencers that mattered most. I’ve come to learn that this is – for better or worse – a best practice, and one that data management evangelists should learn and practice.
But the business value and return on investment (ROI) is just one of the three core types of measurement you should focus on when building out a data governance program or function. Here’s how I recommend building your measurement strategy:
- Start with data governance program effectiveness to satisfy your sponsors. Early in the life of a data governance initiative, the biggest struggle is often getting business and IT stakeholders to pay attention. An important measure of success is level of engagement, participation and influence the data governance program is having. So you should measure the number of lines of business, functional areas, system areas, project teams and other parts of the organization that have committed stewardship resources or sponsorship. In addition, categorize and track status of all issues that come in to the data governance function, and capture all other types of value-added interactions such as training, consulting and project implementation support. While these metrics may not demonstrate business value, it will help early stage data governance efforts show progress to its sponsors as they work to operationalize data management efforts.
- Develop operational data quality and policy auditing metrics to focus your data stewards. Your business case and ROI is not about the data – but the resulting business rules, policies, processes and standards ARE about the data. Your business and IT stewards alike are responsible for ensuring compliance with these standards, and when necessary are required to mitigate or reconcile a data quality, privacy or security issue. Your data stewards need visibility to both proactively monitor and reactively mitigate any data-related issues that are routed to them through your predefined stewardship workflows. Themes for these operational metrics include data accuracy, completeness, integrity, uniqueness, consistency, standardization, and audits ensuring compliance with privacy and security policies. Visibility to these metrics must be developed and made available to your stewards. This includes the metadata and data lineage analysis capabilities to enable stewards to perform root cause and impact analysis, as well as provide necessary auditability and transparency for compliance.
- Model business value and ROI measures to maintain momentum with business leadership. Here’s where the rubber meets the road – and will mean the difference between data governance as a one-off IT project versus data governance as a sustainable part of how you do business. Can you quantify the return on your data investments? Business value from data governance investments can range across a variety of benefits and can include, among others, reducing penalties by ensuring regulatory compliance; reducing enterprise risk (e.g., contractual, legal, financial, brand); lowering costs (e.g., business, labor, software, hardware); optimizing spending (e.g., procurement, supply chain, services, labor); improving operational efficiencies (e.g., employee, partner, contractor); increasing top-line revenue growth; and optimizing customer experience and satisfaction.
Some business opportunities are “easier” (being a relative term) to quantify than others. But if your data governance team can provide a balanced report that blends at least some transparent quantitative business value on certain targeted processes, the senior leadership at most organizations will be fine with more qualitative benefits for others – so long as enough anecdotal support is presented that show how key influencers believe the data governance efforts are helping. And in the end, you at minimum want your ROI measures to pay for resources you’re dedicating to it. If you can’t demonstrate that you’re breaking even, your data governance efforts can’t last for long.