Surveys, news reports and countless vendor and analyst firm research papers all point to a pervasive business culture...
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that almost seems programmed against successful data governance strategies.
Common obstacles to implementing a solid data governance strategy include things like a lack of senior-level executive support, a lack of funding and the difficulty of getting various business units to cooperate on governance policies. But according to end users and consultants who have dealt with data governance in the real world, the news isn’t all bad. Some organizations, they say, have implemented high-quality data governance strategies despite the obstacles.
SearchDataManagement.com recently spoke with several of those consultants and end users with the goal of identifying some practical advice to help ensure data governance success. Here are the top five data governance strategy tips they had to offer:
1. Conduct a readiness assessment and define the impact of data governance on the business
Before moving forward with a data governance strategy, organizations should conduct a readiness assessment and develop a clear understanding of how governance will benefit the business, according to Richard Ordowich, a senior partner with Princeton, N.J.-based data governance consulting firm STS Associates Inc.
The readiness assessment should measure the organization’s current data quality and data management capabilities, Ordowich said. It should also identify whether the organization is ready for enterprise-wide change or whether the business units operate in silos that will make for a difficult transition.
Questions to consider when defining the impact on the business include: Will the organization sell more products and services or reduce costs as a result of data governance? How many more sales? What is the magnitude of cost reductions?
“If your organization is not ready or you cannot identify measureable impacts, you should revisit why you are adopting data governance,” Ordowich said in an email interview. “Maybe all you need are improvements in data cleansing [and] de-duplication of data, or perhaps the problems are not so much with the data but with the business processes that are impeding improvements in sales and costs.”
IT has had in my view a very temporary role in this whole chain. But because the database typically sits in IT, people seem to label it an IT problem.
C. Lwanga Yonke, advisor to the International Association for Information and Data Quality
2. Stop associating data with IT
While the most successful data governance strategies boast support from high-level executives, it’s also important to get rank-and-file business users on board, according to C. Lwanga Yonke, an information quality professional and an advisor to the International Association for Information and Data Quality.
The responsibility for almost everything that fits under the data governance umbrella -- i.e., data quality, data stewardship, etc. -- is often placed on the IT department when really it is the business users who should be held accountable, Yonke explained. Business users generate the data, and business processes use the data. And when information is erroneous, it is the business that suffers. Therefore, working with business users to emphasize their role in the data governance process is paramount.
“Accountability for the data asset has been misplaced and placed on IT,” Yonke said. “IT has had in my view a very temporary role in this whole chain. But because the database typically sits in IT, people seem to label it an IT problem.”
3. Remember that data has different properties than most assets
Data management professionals, analysts and consultants often say that an organization’s data should be treated like an asset. The logic behind this popular refrain is based on the notion that businesses run on information, and therefore data, like money or capital equipment, is an asset that must be managed and cared for properly.
“If you’re going to treat [information] as something that is valuable, then you need to have some governance over it as well,” said David Loshin, president of Knowledge Integrity Inc., an information management consulting and development firm.
While this is good advice to consider when implementing a data governance strategy, it’s also a good idea to remember that data has significantly different characteristics than most assets.
For example, Yonke said, data is basically intangible or at least largely out of sight, whereas other assets, like money, people and equipment, can be readily seen. Data is also easily copied and shared horizontally across multiple business units, whereas something like a pump or a compressor cannot be easily shared between two facilities or plants. Meanwhile, data stores can grow incredibly fast, but not all information is equal.
The special characteristics of data suggest that information cannot be controlled as easily as typical assets, Yonke said, and those traits must be kept in mind when crafting a data governance strategy. In other words, treating information like any other asset simply isn’t enough.
“Data governance is about providing control over something that people don’t want to control because they think they can have their own copy of it and do whatever the heck they want with it,” Yonke said. “We are asking folks to do something that is counter-intuitive, but most organizations don’t focus on that.”
4. Consider enacting a ‘going forward’ data governance strategy
It’s well known that implementing a data governance strategy can be a difficult, expensive and time-consuming process. But organizations can alleviate some of those burdens by taking an incremental approach with an eye to the future rather than focusing on the past, said Jay Cline, president of Minnesota Privacy Consultants, a Maple Grove, Minn.-based firm that helps multinational corporations and government agencies enact data governance policies.
“I’m a big fan of the ‘going forward’ strategy,” Cline said. “Going forward, all new vendors will be treated in such a way; going forward, all new systems will adhere to the data architecture; [and] going forward, users will handle files in a certain way and classify them in a certain way.”
5. Learn to effectively lead change
Change management is critical to successful data governance strategies, according to Yonke. But it’s not the documentation-oriented change management that is often recommended when an organization launches a new technology or business application. Rather, this type of change management is focused on helping organizations deal with the cultural changes that come as a result of transitioning to new data governance policies.
“I’m talking about educating people,” Yonke said. “Six months before you send a data governance policy to be approved, you need to have educated people on what the problems are that [governance is] trying to solve. Being able to effectively lead change is a critical thing.”