The Top 10 Issues of 2018 for higher education technology were announced at October’s Educause conference in Philadelphia. Each year I share my thoughts on the issues, from #10 to #1. Feel free to review the most recent entry on #8 (TIE) Digital Integrations here. And now, let’s move forward!
#9 (TIE) Issue: Data Management and Governance
Implementing effective institutional data governance practices.
How do I ask this? Is data management and governance truly an IT issue? Happy to play along, but in my opinion there is a crucial step prior to it becoming an IT issue and, unless I’m mistaken, that step rarely occurs. The management of data is a university discussion. Sure, from within IT we can build and house and protect and carve out and serve data all day long. Heck we’ve been doing it in some form or fashion for decades (think: data warehouse). But data and how it should be used requires a much larger conversation with all the top players around the table. FIRST. Before anything else happens. Definitions and vision need to be set and agreed upon prior to IT even thinking about conceptualizing building, managing, governing.
It’s been about ten years since data driven decisions became the buzz phrase du jour. Since that time I’ve had the pleasure of attending several sessions across various conferences focusing on the topic of data management, data storage and how to most effectively use data to drive decision-making. In other words, identify via data what our next steps should be, how we should prioritize tasks/projects and basically make more targeted, thoughtful decisions. One such session stands out. It was a CIO session at Educause in Anaheim a couple of years ago where, via some form of clicker/iDevice polling software, the question was asked, “How would you rate how well your institution is managing data?” The room was packed to the gills with leaders from nationally recognized universities, the biggies, the tiny unknowns, and everything in between. This was, again, just two to three years ago. The results trickled in. No one rated themselves in the top two categories – excellent or whatever one notch beneath that was. About 85% ranked themselves in the lowest ranking. When questions were asked of those who rated themselves mediocre-ish, it was primarily a couple of community colleges. And the examples cited were confusing. Because: CART BEFORE THE HORSE. Higher education has grasped onto the phrase of ‘data-driven decision making’ without defining what this means. Of course we don’t want to make ad hoc, vapid decisions. But…huh?
Is data management and governance truly an IT issue?
The issue for IT is getting clarity throughout institutional conversations on how data will play a role in the business bottom line. Effective IT leadership should most certainly be present for the conversations and, in many cases, the CIO might very well be the best person to help guide the conversations, but in order to ensure that the consumer and crafter is pleased with their data interaction, we all need clarity. I will certainly define for you what enrollment data you want to compile and what the next student engagement steps should be, but will that counsel be coming from the best seat around the table? (No.) Otherwise it’s like building a house for a couple before having any discussion about floorpan, features and number of bedrooms. Just assuming what that couple might want in a home.
Once we get past the strategy and vision, the actual IT part is really a non-issue. Unless you’re saying you want IT to manage the strategy and vision then let’s do it! But you all have to sit around a table with us first.
Advice: Don’t avoid the University-level conversation. Don’t start building something to impress without all the insight and don’t be swayed to ‘just make it happen’. A few years down the line you might as well have built nothing over having built a clunky, nonsensical pile of useless data and who will be eyeballed as ineffective and short-sighted? The data conversation should be transformational, not operational.
Next issue: IT Staffing and Organizational Models, #7.