administrative staff, as data tends to “inform” (not drive) decision making. Such decisions involve, among others: recruiting (targeting geo-markets, what triggers enhance acceptance, etc.) and using data to support teaching and learning (identifying classes with most impact, allocating resources,, improving the student experience, supporting at-risk students, identifying and providing successful interventions, enhancing digital skills, personalized and adaptive learning, improving
teacher and program effectiveness, predicting student success, career services, etc.). It also involves generating operational efficiencies (improving energy use, estimating project cost, setting tuition, fundraising, calculating program ROI, scheduling classes and use of rooms, calculating proper class size, establishing performance metrics for faculty and staff, boosting retention, producing audit and accreditation reports, asset management, visitor management, etc.).
Equally, institutional research involves more than quantitative analysis. Qualitative analysis – with data acquired through quick check-ins, pulse surveys, and annual surveys of faculty, staff, and students – is a critical tool institutions can deploy to better understand behavior, processes, and practices. Understanding this need internally and society’s need more generally is why larger institutions are making major investments in teaching and conducting data science research.
Consider, for example, the investments made by the following institutions:
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One can only assume such initiatives will expand at other institutions given the emergence and role that machine learning and generative artificial intelligence (AI) will play not just in society generally, but higher education specifically.
As such, higher education will not be immune from the need to hire data scientists. Data scientist roles have grown over 650 percent since 2012; and data scientists are currently in the top 20 fastest-growing occupations in the U.S., with 31 percent projected growth over the next 10 years and about 11.5 million new jobs being generated in the field by 2026.29 As such, institutions of higher education must ready themselves to confront the costs of that competition (data scientists were earning an average annual salary of $152,279).30
Systems support and integration, when combined with the need for more and more data to inform decision making, is why the role of information technology IT departments within higher education has and will continue
to expand. Already, it has come to the point where the IT department now connects with
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