As far as organizational strategy goes, the one that has been the focus of late is data governance. Ever since data analytics, as a technology and technique, was added to an Enterprise’s competitive armory, data governance has gained importance as well.
Yet, many Enterprises are found to be faltering on this front. Surveys from the past have shown that about 80 percent of organizations that have implemented data governance have either failed or are struggling with it.
Lack of understanding, communication gaps, and failure to align the program with an enterprise’s overall strategy are some of the common reasons for such failures. Before delving into the reasons in length, let’s first understand what data governance really entails.
Often, organizations confuse data governance with data quality. The latter is the “How” of the data analytics process, while governance is the “Who and What”. Data quality is the maintenance of quality standards at an individual project level. At the same time, governance refers to the overall data management in an enterprise, from data availability to data usability, integrity, and security. Data quality must be a part of the overall data governance system.
According to Stewart Bond, Director of Data Integration and Integrity Software Research at IDC, data governance is not a technology but a process supported by technology. Organizations that believe there is a magic-box IT solution will fail, but organizations that use an approach in which business resources work with IT to make tough data decisions within a data governance structure will be successful, he adds.
Clearly, data governance is policy, not a solution. It nests within any organization that has deployed business analytics as part of its overall strategy – in fact, one of the reasons for data governance failure is that it is not being aligned with an enterprise’s business strategy. Governance is about ensuring the proper implementation of business rules and controls around your organization’s data. It involves the wholehearted participation of all company departments, especially IT and business. Any attempt to run it in a vacuum or silo means it’s imminently doomed.
In an article by the Society for Information Management, Michael Goul, an associate dean for research and professor of information systems at Arizona State University’s W. P. Carey School of Business, makes an interesting point. While agreeing that data science has the potential to revolutionize commerce, he opines that many companies are rushing headlong into the field without putting proper governance systems in place, which, in some cases, could lead to disaster.
Obviously, any organization that has started dealing in data to derive insights needs a data governance program in place. So where does it all begin to go wrong, then?
What Leads to the Failure of a Data Governance Program?
The nature of data is such that it is a shared asset, consumed by many, and so needs ownership. Security, too, is of importance here.
A well-thought-out data governance plan must have a governing body and a defined set of procedures with a plan to execute them. To begin with, one has to identify the custodians of an enterprise’s data assets. Accountability is key here. The policy must determine who in the system is responsible for various aspects of the data, including quality, accessibility, and consistency.
Then come to the processes. A set of standards and procedures must be defined and developed for how data is stored, backed up, and protected. To be left out, a good data governance plan must also include an audit process to ensure compliance with government regulations.
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Even if an enterprise falters in one of the above areas, its data governance plan will fail. But even if an organization gets it right and the governance program takes off, it does not end there. It is an ongoing project, not a one-off. Chances are it will falter down the line.
Here are some of the reasons why:
- Lack of metrics
- Absence of documentation
- Loss of impetus
As any good analyst will tell you, metrics are the key. If an Enterprise does not know where it’s headed with its data governance plan, reflected in black and white, it’s bound to stutter. Things like targets achieved, dollars saved, and risks mitigated need to be measured and recorded.
Failure to record is one of the leading contributors to the loss of momentum in any data governance program, say experts. Continuous tracking of the achievements and failures, and updating the data governance council on these, will allow data governance to be a living, breathing creature within an organization. The first sign of success must not be interpreted as a time to slow down, but instead, be used to add to the momentum.
