Managing your institutional data effectively is no mean feat. Dozens of factors come into play, from your choice of reporting tools to the makeup of your institution’s strategic plan. With all those moving parts, it’s little wonder that mastering your data management can be overwhelming.
A big part of that, suggests Brian Parish, president of IData, Inc., has to do with some confusion over what data management actually is—and what it isn’t. In the 18 years Parish has worked in higher education technology, he’s run into quite a few incorrect assumptions about both the nature of data management and the best way to approach it. Parish shared six of the most common misconceptions he’s seen, as well as some thoughts about how to get past them.
Misconception #1: Data management and Master Data Management are the same thing.
Although they may sound similar, data management and Master Data Management (MDM) are two entirely different practices. MDM is concerned specifically with maintaining a single, authoritative source of institutional data. While MDM is a critical aspect of data management, it’s only one piece of the puzzle.
Data management is much broader, having more to do with an institution’s culture than its systems. Good data management is all about making sure people understand the data, trust that it’s accurate, and use it in their decision-making. “People think of data management as a technical project, and I actually think it’s not at all a technical project,” says Parish. “The bigger problems with how people manage data are people problems, not technology problems.”
Misconception #2: Data management is IT’s problem.
Unfortunately, even if an institution accepts that data management is a people problem, often the assumption is that it’s a problem specific to IT people. The trouble, says Parish, is that lack of understanding and trust on the functional side can kill any reporting project, no matter how perfectly it’s executed on the IT side.
The only way to ensure that your functional staff understands and trusts the data they’re seeing from the technical staff is to get both sides talking, early on in the process. The better your functional and technical staff communicate the easier it will be to build up transparent, institution-wide data management processes.
Misconception #3: Data management is all about data quality or data security.
Oftentimes, when data management initiatives are formed, it’s to address specific issues, like data quality or data security. “And that’s good,” says Parish. “Data quality and data security are really important, and they’re often clear drivers for forming these committees.” But they’re only two aspects of implementing a successful data management strategy.
If data quality or data security are particular pain points at your institution, then by all means, form a group to work on them—but don’t stop there. Leverage that momentum, says Parish, and put the group to work on broader changes. Make sure the committee includes not only IT and IR, but also members of the institution’s business units and administration. The more inclusive you are, the easier it will be to identify and address your data management challenges, institution-wide.
Misconception #4: “We don’t need data management.”
This mindset tends to occur, says Parish, in two situations. First, there’s the institution that has great reporting, without a formal data management strategy. In this scenario, a team of talented folks produces puts out lots of information that the rest of the institution trusts and relies on. Great, right? Well what happens when the head of that reporting team wins the lottery and retires to Fiji? Without good data management practices, all of their knowledge and skill is lost, and suddenly, the institution’s IR team is crippled.
The other common fallacy is a grass-is-always-greener view of reporting tools. Oftentimes, people are convinced that their reporting would be perfect, if they could just find the right software. Choosing a good reporting solution is certainly important, but even the best tools won’t spontaneously generate a good reporting environment. Regardless of your institution’s situation, the bottom line, says Parish, is that “without good data management, reporting will continually fail.”
Misconception #5: “My institution is not ready for data management.”
An unfortunate truth about good data management is that it can often seem unattainable. So many things need to fall into place that it sometimes seems easier to decide your institution just doesn’t have the resources to commit. But even though the entire institution may need good data management doesn’t mean it has to implement it all at once. “It could start with just one person,” says Parish.
Say a single individual is producing great reports. If that person takes the time to document their processes and define their terms, and then shares that information alongside the reports, you suddenly have a great example of how it should be done. Even if it doesn’t catch on immediately, you still have one more person who’s doing it right—which means you’re better off than you were before.
Misconception #6: “Data management is easy.”
“It’s not,” says Parish. “It’s hard. It’s the diet and exercise of reporting. We all know it works, but no one wants to do it.” The trick to getting started, though, is to have a good understanding of what data management is and to get your staff invested in—and maybe even excited about—what it can do for your institution. It will be well worth the effort.
Evisions is proud to have IData as a partner. We encourage you to visit their website for information about their data management tools, in particular the DataCookbook, a data dictionary solution designed to make fostering good data management practices easier than ever.