Intuitively, we all know we spend a ridiculous amount of time finding information these days.
Personally, I believe part of the problem is that we have the power, literally at our fingertips, to look at almost anything that springs into our brain-and just 20 years ago, that wasn't possible without a good deal of work, time and open records requests. For instance, gone are the days when my husband and I had to drive to the video store to determine whether the actor who played "Booger" in "Revenge of the Nerds" is the same guy who played Agnes' love interest in the last season of "Moonlighting." (He is, by the way-I looked it up just now on IMDB.)
But what's more immediately relevant to organizations today is the other part of the information/time-suck equation: Data and information are outpacing our ability to manage them.
The Aberdeen Group recently surveyed 176 organizations about MDM. Inaccurate decisions due to bad data and too much time searching for information were the top two drivers for investing in master data, according to the research firm's report, "Turning Pain into Productivity with Master Data Management," which is available for free download through March 29.
That's why it's impressive that companies with master data management save, on average, the equivalent of three and three-fourths weeks per employee each year. What's more, MDM means less time correcting bad information-an average of 200,000 fewer man hours than non-MDM users, the equivalent of one employee working for seven decades.
Those stats aren't just fodder for PowerPoint presentations-they're also a good reason why business divisions and end users should care about and be involved with master data management.
Earlier this week, I focused on the report's first two required actions for those who want to achieve Best-in-Class results from master data management:
Today, let's look at the last two recommendations:
As I noted yesterday and written in previous posts, experts say business users must be involved if MDM is to succeed. The question is, how do you convince them of that?
"This is where selling data programs to the business and highlighting key business value and implications for each line of business, becomes important," wrote Kelle O'Neal, managing partner for the MDM-focused services firm, First San Francisco Partners, in a response to a recent IT Business Edge blog post. "So although IT can start this process, the goal is to create a sense of urgency that will enable a transfer of ownership and leadership from IT to the business."
IT can do this in three ways, O'Neal wrote:
If you need help identifying how MDM can address business issues, check out "What Your CEO Should Know About Master Data Management ," an executive brief by Informatica. This short paper outlines MDM's business use case and provides specific examples of ways MDM can help sales, marketing and those who work with channel partners.
Another tactic you can use is to start with a small, critical set of data, according to Winston Chen, vice president of strategy and business development for MDM vendor Kalido. Chen calls this the "federalist approach to MDM," and its advantage is that it allows MDM to take root without the departmental turf wars that are so often the downfall of data-related initiatives.
Finally, Aberdeen recommends you use automated data quality tools so you can maintain the data's accuracy and completeness. The problem here is that too often, organizations think MDM is a project that they do once. Obviously, master data isn't stagnant; to maintain the quality of the data, you have to (duh) have a data quality plan. As data quality consultant Jim Harris told me recently, the biggest mistake with data quality is to be reactive instead of proactive:
The analogy I like to use there is like it's if your house is on fire, it's not very difficult to get people to say, "Hey, maybe we should put the fire out." But, it's very difficult to get people to practice fire safety. That's what proactive data quality approach is: We should practice fire safety so that things don't burn down.
That's true for master data, too. Alas, this is an area where even the best-in-class tend to falter; Aberdeen's survey found that only 43 percent had invested in data quality tools, but less than a quarter "had invested in automated or system assisted data cleansing or auto-discovery of changes to data structure."