Higher education needs to modernize data governance and structure before using data and analytics to guide decisions effectively, Educause researchers found in a recent report.
The higher education IT organization released Monday the first data and analytics edition of its “Horizon Report” series. A panel of higher education experts agreed on a list of technologies and practices that are “key” to the future of higher ed, many of which centered on staff getting a handle on where data is recorded and stored and who manages the information.
“Institutions are really starting to be critical about their own practices and think about what the goals of their data and analytics practices are and figuring out ways to measure whether or how they’ve reached those goals,” Educause researcher Jenay Robert told EdScoop.
The report also lays out current challenges and trends, including that many institutions’ data structures are “outdated and disorganized.” That poses a problem since institutions are also prompting their staff to use data to make crucial operations decisions, researchers wrote.
“This reliance on data, however, requires extensive investments in institutions’ data infrastructures and
governance, and meaningful engagement with data across the institution requires intentional and coordinated transformation in institutional culture and operations,” the report reads.
Some institutions are making those investments, creating chief data officer positions to manage data governance and launching institution-wide data initiatives. Edtech companies are also focusing on products designed to lure in institutions looking to bridge the gaps between existing information systems or to parse through their data faster.
“[Institutions] may benefit from these technologies in the form of enriched decision-making capabilities, but not before they improve their internal processes and resources for supporting, governing, and using those technologies,” researchers wrote.
Building a structure around data analytics
The technologies and practices experts selected in the report fall into two categories: creating or updating data governance and systems and guiding staff in using data. Unifying data sources — or connecting information systems that previously were “siloed” in different departments or offices — was rated by experts as the approach that could best support institutional goals and business practices.
“I would argue that that extends beyond just unifying data sources, but also unifying the people involved in collecting the data, storing the data, protecting the data, querying the data, analyzing it, communicating about it,” Robert said. “So these silos really impact pretty much every aspect of the data and analytics processes that we see in higher education, and have inhibited progress for quite some time.”
Experts also recommended developing a system to comprehensively assess how the institution uses data and analytics, including “methods for collecting, storing, and analyzing data, as well as processes for dissemination of insights.” Those assessments can feed into one of the other practices identified in the report — scanning for potential bias in collecting and using data.
“Collectively, the field is reexamining who makes choices about what data get collected, how they are
collected, what they are used for, and what implicit biases are baked into every step,” the report reads.
The report cites a project at Michigan Tech in Michigan’s Upper Peninsula, which used data from various sources to examine faculty demographics and historical trends in hiring, promotions and salaries.
Other institutions are developing policy around crunching institutional data for predictive analytics and other technologies. The University of California released a report last year on “ethical artificial intelligence,” describing how the institution plans to assess and counter historical bias.
AI for analytics was also one of the technologies Educause researchers highlighted in its teaching and learning Horizon Report in April.