Guide to managing a data quality assurance program
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The way Rob Corrao sees it, companies need to think big when crafting a data quality strategy.
Corrao, chief operating officer at information and asset management consultancy LAC Group, said information is being created and collected in such large quantities nowadays that it behooves organizations to combine an enterprise data quality program with broader initiatives, such as master data management (MDM) and data governance. "You can deal with this in a piecemeal way, under the radar, but that is inefficient," he said, adding that disjointed efforts can cost "hundreds of thousands of dollars in wasted resources" at large companies.
While well-managed data quality improvement projects certainly can succeed on a standalone basis, tying them to broader MDM and data governance programs can help ensure that data remains accurate and consistent across an organization, according to Corrao and other consultants.
Some IT and data management teams mistakenly look at data cleansing as a one-time event and develop a false sense of security about the quality of their data, said Rob Sturgeon, COO at ServiceSource International Inc., a software developer and managed IT services provider. "They think they can clean the data once and they're all set. But data can get out of control again if a process isn't put in place."
All-in on data management
Gartner Inc. analyst Ted Friedman also said that organizations should be wary of half-measures. In the not-too-distant past, standalone data quality projects were seen as realistic investments with achievable goals, Friedman said. But since the worldwide economy crashed in 2009, "the picture has changed," he added. Now Gartner recommends that companies link data management projects together in order to improve their ability to meet business requirements.
For example, Friedman said an MDM program that creates a consistent set of master data on customers or products can serve as a central and unifying element that helps justify specific investments in data quality tools and processes to support the effort. The same goes for data governance programs, which pull together representatives from business units to develop policies and procedures on the use of data -- and they're being adopted more widely as issues such as risk and compliance management become a higher priority for corporate executives. "We see data quality as one piece of that governance initiative," Friedman said.
In addition, some companies are linking data quality measures to customer engagement programs or initiatives aimed at driving new revenue, such as efforts to enter new markets, Friedman said. He has even seen organizations tie data quality processes to application modernization projects; in those cases, they want to make sure the data contained in legacy systems that are being decommissioned is accurate before it's moved to new platforms.
Larger initiatives such as the ones cited by Friedman cost more and take longer to complete than more straightforward data quality projects. But the potential rewards are much greater -- and in large companies, he said, there might be no choice but to take a full-scale approach to fixing data issues and then trying to ensure that they stay fixed.
First things first: enterprise data quality
On the flip side, Nathaniel Rowe, an analyst at consulting and market research company Aberdeen Group Inc., said data cleansing efforts are often precursors to deployments of MDM systems. "If you put MDM in place but you're using old, substandard data, you won't see much value from the effort," he said. "You'll have issues with the data if it isn't standardized."
Budget limitations can get in the way of a more comprehensive approach to enterprise data quality, at least initially. "If you only have the budget to do data quality, that's more important, but keep looking toward the horizon for the next step," Rowe said, adding that IT managers should try to get approval for an MDM or data governance program "before data is allowed to get bad again."
In such situations, he said, citing the business benefits gained from improved data quality can help to justify the broader investments. Rowe's advice: Tell corporate executives to imagine what more could be done with a centralized process for keeping data clean, consistent and accurate.
Alan R. Earls is a Boston-based freelance writer focused on business and technology.