Change in Higher Education Data and Analytics

The impacts of covid-19 on postsecondary institutions will be profound and lasting. Now, more than ever, institutions confront a critical need to build capacity to effectively manage and extract value from their data assets.

Every higher education institution has an institutional research function, or IR. As I described in a chapter on “Higher Education Decision Support,” in The Analytics Revolution in Higher Education, the IR role has evolved as needs, contexts, and technologies have changed. In that chapter, I touched on some ways in the coming decade or so that such evolution should continue, perhaps accelerate. A few years after writing it, and in the context of a nearly year-long pandemic and all of its impacts to date and yet to come, what do I expect the next decade to look like now? Perhaps not so different from prior forecasting, if accelerated and more urgent. For certain, changes are afoot.

Already a Changing Field

Higher education institutions have for decades confronted changes that have been catalysts for the evolution of their IR roles:

  • Shrinking public budgets, which reduce appropriations available to colleges and universities and contribute to rising tuition rates;
  • Growing public skepticism about the higher education value proposition and concurrent pushes for greater accountability; and
  • Ubiquitous technological change, which drive expectations of what higher education can and should be doing to take advantage of data assets and new technologies.

Of course, there’s more. Higher education is complex, diverse, and faces a multitude of pressures and opportunities. And common to all colleges and universities is a lot of data and endless questions to answer, problems to solve, and actions to inform with those data.

IR offices are most often the go-to resource to address that multitude of questions and data needs. These offices historically did a lot of surveys and fact books and have, in many cases, broadened their scope and sophistication over time. They can range size and complexity from a DBM who assembles federal compliance reports on the side to purposeful teams of programmers, statisticians, and data viz specialists. This function has been changing with pressures and trends described above and will continue to do so. I summarized some of this evolution in “Higher Education Decision Support,” and you can find discussions of these trends and predictions elsewhere, such as a panel at the SAS Global Forum in 2019 or a 2016 article by Swing and Ross in Change.

We’re seeing and will continue to see IR offices be renamed (e.g., “data and analytics,” “decision support”) as a reflection of their morphing roles, or alternatively seeing the two paths split, with IR continuing to handle many of the more traditional aspects of the office and separate analytics teams tackling the new stuff. I still see these collectively as the “IR function.” They’re an evolving set of people and technology resources that use institutional and external data sources to provide information to decision makers in support of institutional goals (e.g., student success, faculty retention, regulatory compliance, operational efficiencies).

Covid-19 Will Amplify Pressures and Accelerate Changes

Crisis situations tend to create spikes in information needs. This has been a crisis year, and for higher education it is looking like a crisis decade, or a sea change that will almost certainly shape information needs for years to come. In that regard, I expect the following trends will be part of institutions’ reconceptualization of the IR function and higher education data and analytics.

1) Chief Data Officer and Chief Analytics Officer and various positions that approximate those roles. Although a CDO is becoming more common in many industries, in higher education it is still a nascent phenomenon. Immediate information needs and acute budget pressures are, for many institutions, shining a spotlight on the need for a data strategy and the implications of poor coordination and integration of processes, people, and technologies across the enterprise.

2) Expanding data available and data used. There is growing recognition of the available data and a growing data monetization mindset, representing significant shifts for colleges and universities. Effectively utilizing data systems requires coordination of people, processes, and technologies. Extracting value from “dusty” or “dark” data assets is a concept that has been evolving generally, and in education spaces that evolution has been a bit slower perhaps than in other sectors. Responses to the protracted crisis created by covid-19 will likely include better combination and use of data from the CRM, LMS, ERP, and other ingredients in our higher education digital alphabet soup. And institutions will look at the potential value of previously unexplored data sources, whether that be regarding movement and gathering of people and uses of physical spaces, various aspects of virtual traffic (e.g., wireless usage), online instructional activity, social media activity. The IR function that stays relevant and fully engaged will develop expertise in the wider arrays of data and deepen its familiarity with big data analytics.

3) Increasing potential of technologies and service providers to make the complex data environment more accessible. As higher volumes of more diverse data sources are recognized as valuable, tools and services for storing, integrating, and analyzing those data will also be increasingly important. Higher education has mostly dipped its toes into data lakes or stood in the doorway of data lake houses, and (at risk of overdoing an already hackneyed analogy) more institutions and systems are going to dive headlong into cloud computing, integration platforms, etc. More institutions will develop and refine their cloud strategies and data strategies, and the prevalence of “as-a-service” products will grow more rapidly. These trends necessitate coordination between IR/analytics and IT business areas more than ever. IR functions will be called on to evaluate rapidly changing products and services and to develop competence with new tools and processes.

4) Decentralization of traditionally IR activities. We see this occurring at many universities. What was traditionally handled by IR units is increasingly moving to other parts of the institution. Among other implications, this points to the growing value of governance, which becomes particularly tricky when standing up environments for broader data access and self-service analytics. Of course, this is a problem that nearly every company pursuing broad access to analytics is also grappling with, not just a higher education challenge.

5) Growing prevalence of distributed workplaces and managed services. Regardless of where institutional analytical capacity resides, for institutions to “keep up” in the analytics space is going to require more than just picking around the edges at “telecommuting.” It will require them to look at the models of intentionally (pre-covid) distributed companies like Automattic and Elastic – and now we have varying experiences at nearly every company and institution since March 2020 – and to develop appropriate policies, effective tools, and productive cadences of communication. Post-covid workplace changes also will spur further discussion about managed services in the data and analytics space. What have until now been perceived usually as positions and offices necessarily housed on campus and physically proximate to other campus business units may increasingly be outsourced.

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