Data governance is the practice of managing the availability, usability, integrity and security of enterprise data to maximize return. The challenge lies in how it must work, through collaboration between IT and business, and across business units. This challenge is one of implementation: creating the necessary communication, power sharing, accountability and joint investment.

In October 2012, an Economist Intelligence Unit survey revealed a clear link between financial performance and use of data, and found that 11 percent of respondents felt their organizations make substantially better use of data than their peers. But more than one-third of that group was comprised of top-performing companies. Conversely, of the 17 percent of executives who said their companies lagged peers in financial performance, none felt their organizations made better use of data than their peers.

Enterprise data is non-fungible – it’s not a commodity. It’s unique to the organization and irreplaceable. There’s no third-party that can deliver superior replacement data if yours is worn out.

Data governance is a response to the growing complexity of the enterprise information infrastructure and the attendant business challenges. Complexity has also increased in the logical (applications) dimension, with the proliferation of commercial solutions for everything from personal productivity to business process automation. Each application has its own constraints and design goals and its own distinct representation of critical enterprise data.

Data governance initiatives are often the result of compelling events. Events such as security breaches, regulatory actions, financial misstatements or systems failures can have a significant and measurable cost after they occur. If you’ve read this much of an article on the ROI of data governance, I suspect you’re actually planning ahead, examining a broad array of business drivers for data governance and looking at enterprise information as an important and valuable asset – one to be carefully managed for optimum return.

To gain executive support on a data governance initiative you’ll need a strong business case. This can be a a challenge if you’re not operating in the wake of a compelling event. It is helpful to look at the gross value-creation categories and some of the mechanisms for measurement and estimation.

Value creation opportunities related to data governance may be usefully divided into three tranches: primary, secondary and tertiary, as shown in Table 1.

What and How to Measure

In choosing return metrics, look for economic metrics and objectives that are specific, measurable, attainable, relevant and time-bound. For example:

  • What’s the economic measure of the loss of reputation associated with a financial restatement? This calculation depends on the size and nature of the restatement and could include access to or cost of capital, loss of sales, loss of customers and damage to key partnerships. It is almost certainly significant and almost impossible to predict. Additionally, it’s is pretty difficult to run the numbers and hard to justify an investment.
  • Improved campaign targeting and qualifying is key. If the quality of your customer data is affecting the deliverability and conversion rates of your direct mail campaigns, you may be able to build a business case. The potential savings is easy; the cost of the each mail piece is known, reductions in misdirected or undeliverable pieces go directly to reduced costs. The question is, what quality improvements are attainable?
  • Always be mindful of reduction in time to find and acquire information. This is interesting because in most enterprises, knowledge workers occupy a broad cross-section of the organization. How many knowledge workers are in your organization? A number of studies have pegged knowledge workers as spending 30-40 percent of their time (2.5 to 3 hours per day) searching for information. What if you could substantially improve information awareness in your organization?

So we’ve seen value creation opportunities that are impossible to measure, those that are easily measured, but with narrow benefits, and those that can positively affect the organization as a whole, but require upfront investment or a leap of faith.
Getting Started

Many organizations have found success by “thinking globally and acting locally.” Sometimes referred to as guerilla governance, this may not be the fast path, but is likely the pragmatic path to enterprise data governance. This is not a shortcut – there’s no substitute for the broad-based communication, collaboration, coordination, executive sponsorship and investment required to obtain sustainable governance. It’s merely a way to align with the principles and practices of data governance and obtain some small, quick wins cheaply to build the evidence and confidence required to support greater investment. Getting started with this strategy requires at least three foundational steps.

1. Best Practices.

Get acquainted with key issues and best practices in your industry. Avail yourself of Web resources and professional advice and training to get oriented.

2. Lay of the Land.

Next, take inventory. Make sure you understand the scope and scale of your information assets. Where does customer information appear across all of your systems? How does the definition of customer vary across the organization?

Although most financial organizations can track all of their monetary assets and most IT organizations can provide a server roster, despite the huge investment in enterprise information it’s the rare organization that can on demand produce a useful information inventory. Doing so requires a metadata management infrastructure integrating business definitions, technical metadata, business processes and deployment information.

3. Quick Wins.

Finally, with a clear understanding of best practices and armed with an understanding of the lay of the land, identify tactical success opportunities. These opportunities will be:

  • Amenable to the technique of data governance.
  • Of limited organizational, temporal and technical scope.
  • In resolution, able to deliver measurable economic impact.

Communicate your successes early and often, and use them as validation that data governance can work in your organization and is deserving of executive sponsorship and incremental investment.