Data Governance Primer

 
 
By Jill Dyché  |  Posted 2011-07-28
 
 
 

Standard industry intelligence says that corporate data volumes double every 18 months, but mobile and online data are growing at an even faster clip. From corporate cost containment, regulatory compliance, strategic “voice of the customer” initiatives and beyond, the need for businesses to manage proliferating data has never been more urgent.

Enter data governance. The term has become as buzz-worthy with businesspeople as it is with their IT colleagues. Nevertheless, both sides continue to struggle with how to define data governance and how to formalize it as a sustainable set of practices.

The term “governance” has bounced from the political world to corporate boardrooms, landing squarely in the laps of IT executives, who have been busy formalizing their IT governance initiatives. Despite the term entering the lexicon, there is still some confusion about what data governance actually means, what work is involved with it and who should own it.

IT organizations have begun to recognize the need to define terminology and business rules around data that is increasingly shared across business processes and organizations. As part of efforts to rein in costs, CIOs are starting to understand the enormous and often-replicated efforts involved in finding, gathering, annotating, consolidating and deploying data to support a growing project portfolio.

Unaware that this work has already been performed for other projects, well-meaning developers roll up their sleeves and integrate the data yet again. The expense of this duplication is buried, but it can cost a large company millions of dollars in excess labor hours.

Increasingly, data governance is coming to the attention of businesspeople, many of whom have begun to develop workarounds due to the lack of available data that’s meaningful, integrated and easily accessible. It’s a phenomenon that’s become rampant across industries, resulting in enormous costs.

“I’ve developed my own database to protect myself from other people’s databases,” one health care provider says wryly. “I can’t trust other people’s interpretation of data about my patients. If I have to stay late and maintain my own Access database, that’s what I have to do.”

Multiply this clinician’s efforts by the number of clinicians in the hospital network, and you’ll get an idea of the costs—and the risks—involved.

 

What Is Data Governance?

Part of the general wariness about data governance is due to the lack of a clear definition. Consider Figure 1, which includes the components of data governance.

The top portion of the figure conveys business ownership of corporate data, which encompasses the overarching definition of data governance, namely, the business-driven policy-making and oversight of corporate information. As such, data should be owned by the business, which circumscribes its definitions, rules, access and usage policies.

But IT has a role to play as well. The bottom portion of the figure, while conversationally part of data governance, is really data management: the tactical execution of data governance policies and decisions. Just as data governance is business-driven, data management is owned by IT.

“In health care, there’s a special urgency around information,” says Mike Nauman, corporate director and CIO at the Children’s Hospital and Health System of Wisconsin. “This is amplified in academic health care organizations that operate at the forefront of medicine.”

Nauman and his team recognized that data governance would be an organizational discipline, driven by the business but enabled by IT. “We knew that information was a shared asset, so we introduced a data management function in IT,” he explains. “The function encompasses new roles, new processes and specialized skills. As data governance evolves, we’ll be ready to support it.” vTogether, these two functions inform a workflow that defines, tracks, manages and deploys information across its life cycle at a company. After all, the extent to which data is shared across business processes and organizations is the extent to which it mandates formal management and policy decisions.

But how do you kick off a new data governance initiative? And how do you sustain it?

Launching Data Governance

A few years ago, when the early-adopter companies launched data governance, there were few examples to follow. Many of these organizations relied on a smattering of vendor and consultant advice, which admonished them to secure executive sponsorship and to “manage data as a corporate asset.”

That didn’t necessarily help deliver results. Often a visionary manager would convene like-minded people on both the business and IT sides to agree that data was an asset, that data quality was poor and that someone needed to clean it up. The natural next step was to convene a data governance council, the de facto decision-making body for data governance.

Then things got really quiet.

The main hurdle facing these pioneers is translating the all-too-real phenomenon of meaningless, unavailable, duplicated, siloed data into an information-cleansing, integration and deployment strategy that serves the greater good.

Absent a tactical plan for addressing key data needs, meetings of the data governance council degenerated into complaint sessions: The data on the ERP system is unusable. Marketing won’t share its data. No one in the C-suite understands how bad the company’s information really is. Who will fund the project?

The result? Data governance never transcends ownership discussions and priority debates. It is, instead, relegated to one more intellectual exercise at the company, with plenty of people sharing opinions but no one claiming accountability for fixing the problem.

At many of these companies, data governance has become a dirty word. An insurance executive recently invited us to help relaunch a moribund data governance effort. “Just don’t call it ‘data governance,’” he warned us. “You’ll lose credibility.”

 

Best Practices to Consider

The good news is that companies embarking on data governance for the first time are learning from those that have gone before them. Here’s a list of best practices to consider before launching a data governance initiative:

Find the need, pain or problem. Sure it sounds trite, but if data governance isn’t addressing an acknowledged business problem (such as fines for noncompliance or duplicate customer records eroding marketing ROI), it won’t stick.

Know your road map. If you know your problem, you can scope your project. And that leads to definable measures that can prove the value of data governance.

Get an executive sponsor—if you need one. Companies—often large ones with consensus-driven cultures—require executive buy-in before launching cross-functional initiatives such as data governance. Others, however, need to show value quickly before enlisting senior-level support.

“We enlisted our CIO as the sponsor at the very beginning of data governance,” says Karen O’Dell, director of business systems for Station Casinos, based in Las Vegas. “He was a champion on behalf of corporate information and kept saying, ‘We really need to do this.’ Involving him ultimately got everyone else on board pretty quickly.”

Start small. You can build a data governance charter and craft some guiding principles. Indeed, these are great mechanisms to support data governance. But make sure these apply to an actual project that addresses an identified business problem.

And ensure that the project solves at least a portion of the business problem, while at the same time introducing new processes and job roles. This is the proof of concept that will give data governance the visibility it needs to have staying power.

Proselytize your success. A successful project is the platform on which you’ll make the data governance pitch to a broader audience of business constituents and executives. Show how implement-ing data policies, cleaning up a subset of data, and deploying it to support a business process helped lower costs or drive new revenues. People will be lining up to be next.

Think transformation. Data governance involves more than buying a data-quality tool or hiring a data steward. Clear policies for the definition, access and use of corporate information raise the probability that data can be leveraged to streamline business processes, generate new revenue and even drive innovation.

When data governance works, it not only meets one or more business needs, it also cultivates an awareness of specialized skills, processes and tools needed to define, maintain and provision data across the enterprise. It removes needless manual rework by businesspeople who have day jobs, and it reduces the overreliance on human relationships that many businesspeople still fall back on.

Good data governance ensures consistent and meaningful information to support strategy and streamline operations. In short, it’s what the “data-driven organization” is all about.

Jill Dyché, partner and co-founder of Baseline Consulting, is the author of several books on the business value of IT, the latest of which is Customer Data Integration: Reaching a Single Version of the Truth (Wiley).