10 Critical Myths and Realities of Master Data Management
Prevalent myths surrounding MDM alongside an explanation of the realities.
The Information Difference recently surveyed 192 large organizations about data quality in master data management projects. One intriguing finding: Only 24 percent described their MDM projects as "successful or better."
In a CIO UK column about the survey, the research firm's founder, Andy Hayler, contends many that fail at MDM do so because of two core reasons. First, they didn't get buy-in for data governance. Second, they underestimated the importance and cost of data quality.
Yesterday, I shared two key factors experts say are critical to success with master data management: Determining whether you really need MDM and excelling at data integration. Today, I continue that discussion with a look at how inadequate data quality and data governance can undermine MDM.
You would think organizations would intuitively know that since so many organizations tend to have a low opinion of their data quality, as shown by numerous surveys and studies. For instance, only 12 percent rated their data quality as good or better in The Information Difference's recent survey.
Hayler says many organizations fail at MDM because "they have failed to take data quality seriously as an integral part of their master data project, nor budget properly for it." In one survey, he points out, data quality consumed 30 percent of master data initiatives, but organizations had only funded it at less than 10 percent of the cost.
It is actually a bigger problem than you might at first think. The Information Difference survey found that while most companies see data quality as critical for MDM, a third confessed to having no data quality program. Most - 70 percent - don't even try to measure the cost of poor data quality, prompting Hayler's grim prognosis:
Since only a minority of firms measure data quality at all, let alone what it is costing them, it is hardly surprising that they struggle to get senior management to take the problem seriously. Data quality is truly the forgotten child of master data management, and while it remains so, master data projects will continue to have the mediocre track record that is indicated by these results.
David Loshin, president of Knowledge Integrity Inc., says data quality, supported by data governance, is one of three critical preliminary tasks companies should put in place before embarking on MDM:
No master data management program can be successful in the absence of data quality management overseeing via a data governance program instituting MDM without having clearly defined roles and responsibilities for data stewardship, data governance and data quality management is likely to result in an environment that may have a successful migration and consolidation, but will be subject to increasing data quality 'entropy' as time goes on.
Factor 4: Start a data governance program. As you can tell from Loshin's comment, data quality and data governance go hand-in-hand. MDM is about maintaining a correct version of your master data. While data quality corrects inaccuracies in the data, data governance allows you to maintain it by addressing the underlying issues that created the data quality problems in the first place.
Therefore, experts say data governance is a foundational component of any MDM initiative.
"Without data governance, effective use of data is always going to be limited, and any kind of conflict resolution in regards to master data will be a dead-end effort," cautioned Philippe Profit, who is leading an MDM project at Air France-KLM, in a recent Tech Target article.
Data governance requires the business side to define the roles, responsibilities, policies and processes for how data is handled across an organization, Forrester Research analyst Rob Karel told Tech Target.
Karel has long and often evangelized about data governance as a foundational component of MDM success. In 2008, I asked Karel what organizations needed to consider before they invested in MDM technology.
"Adoption of a data governance strategy to identify business ownership and stewardship roles and responsibilities should always be the first step," he said. "MDM initiatives without effective governance are much more likely to deliver below expectations."
Tomorrow, I'll look at the remaining factors that can determine whether you succeed or fail with MDM.