Building an Enterprise Analytics and BI Practice in Higher Education

This is a tough period for higher education. The industry is faced with financial, political, and pedagogical challenges; however, there are also many opportunities, including the incredible innovation occurring in the data analytics and business intelligence (BI) field. Innovation has long been the heart and soul of institutions, but they are now faced with the idea that innovation should be applied to the institution itself, not solely to the object of its teaching, research, and inquiry. Responding to this situation has been difficult for every institution. As a result, institutions are experiencing increased pressure to better understand themselves in a way that has already been applied aggressively in many commercial organizations and drives the strategy and growth of the most dynamic companies in the world.

How can institutions that have been focused on cost containment in administrative processes shift focus to the investment required to become data savvy, even data driven, given their culture? There are some broad answers and some specific answers to these questions. Let’s tackle the harder questions first.

Building a data driven, inquisitive, and adaptive culture in higher education is not simple. Until quite recently, institutions were not rewarded for being quick and adaptive. Instead, institutions have been rewarded by reputation and long-standing success built on past achievement of faculty, alumni, and donors. But the world has begun to change. Student bodies are less traditional, state governments are in budget crises, research is increasingly privately funded, students are often more tech savvy, and the world is online and is certainly a smaller place. This has led to new modes of instruction, more collaborative research, and increasing competition for both.

All of these changes have directly confronted the antiquated, internally parochial, and slow pace of change common in higher education. Universities and collaboratives in which they participate have begun to find ways to align and analyze data for action. As the data emerges, it will be up to institutions themselves to adapt to what they find.

How can institutional leadership prepare to pivot without limiting academic freedom or shared governance? This requires quality data and analysis, trust, and decisiveness. Is there a roadmap for this journey?

There are some basic activities and methods that can bring institutions forward. Outlined below is a recipe that has worked in several institutions but, as any good recipe does, varies from kitchen to kitchen.

What burning question at your institution brings varied answers to the decision table? This a good place to start, by focusing on one important question and expanding the scope from there. An example might be one of the metrics that drive budget allocation. Can leaders agree on one set of metrics and source of data to drive that process? Can these leaders gather a team to build the infrastructure together in a sustainable way? This is a place to start.

Partnerships can begin with a small group of leaders that cross academic leadership, analytic, and technical areas of the institution. It is important for these leaders to have a keen focus on outcomes rather than ownership. When institutional leaders partner on the critical components of success and start small, there is a real change to build success. Institutional research, academic leaders, and IT must bring their skills to the table, agree on an investment, and plan to produce the most critical analysis to drive institutional decisions.

It is important to note that, for many, the start of this process does not require deep data engineering and data science skills. Basic data management and analysis skills can provide results that drive action while building the cultural trust of a wider program. Skills can be augmented over time as scope expands and the complexity of questions increases.