Co-published with and In this era of “Big Data,” institutions of higher education are challenged to make the most of the information they have to improve student learning outcomes, close equity gaps, keep costs down, and address the economic needs of the communities they serve at the local, regional, and national levels. This book helps readers understand and respond to this “analytics revolution,” examining the evolving dynamics of the institutional research (IR) function, and the many audiences that institutional researchers need to serve.Internally, there is a growing need among senior leaders, administrators, faculty, advisors, and staff for decision analytics that help craft better resource strategies and bring greater efficiencies and return-on-investment for students and families. Externally, state legislators, the federal government, and philanthropies demand more forecasting and more evidence than ever before. These demands require new and creative responses, as they are added to previous demands, rather than replacing them, nor do they come with additional resources to produce the analysis to make data into actionable improvements. Thus the IR function must become that of teacher, ensuring that data and analyses are accurate, timely, accessible, and compelling, whether produced by an IR office or some other source. Despite formidable challenges, IR functions have begun to leverage big data and unlock the power of predictive tools and techniques, contributing to improved student outcomes.

What does a culture of evidence really look like in higher education? The use of big data and the rapid acceleration of storage and analytics tools have led to a revolution of data use in higher education. Institutions have moved from relying largely on historical trends and descriptive data to the more widespread adoption of predictive and prescriptive analytics. Despite this rapid evolution of data technology and analytics tools, universities and colleges still face a number of obstacles in their data use. In How Colleges Use Data, Jonathan S. Gagliardi presents college and university leaders with an important resource to help cultivate, implement, and sustain a culture of evidence through the ethical and responsible use and adoption of data and analytics. Gagliardi provides a broad context for data use among colleges, including key concepts and use cases related to data and analytics. He also addresses the different dimensions of data use and highlights the promise and perils of the widespread adoption of data and analytics, in addition to important elements of implementing and scaling a culture of evidence. Demystifying data and analytics, the book helps faculty and administrators understand important topics, including: • How to define institutional aspirations using data • Equity and student success • Strategic finance and resource optimization • Academic quality and integrity • Data governance and utility • Implicit and explicit bias in data • Implementation and planning • How data will be used in the future How Colleges Use Data helps college and university leaders understand what a culture of evidence in higher education truly looks like.

Webber, Henry Y. Zheng, Ying Zhou

How data-informed decision making can make colleges and universities more effective institutions. The continuing importance of data analytics is not lost on higher education leaders, who face a multitude of challenges, including increasing operating costs, dwindling state support, limits to tuition increases, and increased competition from the for-profit sector. To navigate these challenges, savvy leaders must leverage data to make sound decisions. In Big Data on Campus, leading data analytics experts and higher ed leaders show the role that analytics can play in the better administration of colleges and universities. Aimed at senior administrative leaders, practitioners of institutional research, technology professionals, and graduate students in higher education, the book opens with a conceptual discussion of the roles that data analytics can play in higher education administration. Subsequent chapters address recent developments in technology, the rapid accumulation of data assets, organizational maturity in building analytical capabilities, and methodological advancements in developing predictive and prescriptive analytics. Each chapter includes a literature review of the research and application of analytics developments in their respective functional areas, a discussion of industry trends, examples of the application of data analytics in their decision process, and other related issues that readers may wish to consider in their own organizational environment to find opportunities for building robust data analytics capabilities. Using a series of focused discussions and case studies, Big Data on Campus helps readers understand how analytics can support major organizational functions in higher education, including admission decisions, retention and enrollment management, student life and engagement, academic and career advising, student learning and assessment, and academic program planning. The final section of the book addresses major issues and human factors involved in using analytics to support decision making; the ethical, cultural, and managerial implications of its use; the role of university leaders in promoting analytics in decision making; and the need for a strong campus community to embrace the analytics revolution. Contributors: Rana Glasgal, J. Michael Gower, Tom Gutman, Brian P. Hinote, Braden J. Hosch, Aditya Johri, Christine M. Keller, Carrie Klein, Jaime Lester, Carrie Hancock Marcinkevage, Gail B. Marsh, Susan M. Menditto, Jillian N. Morn, Valentina Nestor, Cathy O'Bryan, Huzefa Rangwala, Timothy Renick, Charles Tegen, Rachit Thariani, Chris Tompkins, Lindsay K. Wayt, Karen L. Webber, Henry Y. Zheng, Ying Zhou

Long an afterthought of the American higher education ecosystem, multi-campus systems have become more important than ever. In recent years, leading higher education systems have engaged in transforming the way they work, scaling best practices, leveraging data and analytics, and creating platforms to optimize and personalize these systems for increasingly diverse students. In Higher Education Systems Redesigned, leaders of these efforts share their insights into "systemness" and how to facilitate sustainable change in a system setting while navigating and leveraging tensions between campus and system priorities. Highlighting examples of successful realignment of these priorities with a focus on contextualized design and implementation, the book charts a shift in the aim of systems. Rather than perpetuating existing norms as they have traditionally done, systems are taking measures to spark innovation across campuses and use evidence-based practices to foster student access and completion rates, better serve communities, and drive social mobility and economic growth. Each chapter concludes with a list of takeaways to guide other system leaders and administrators. One of the few recent examinations of higher education systems, Higher Education Systems Redesigned offers a theoretical and practical framework for how systems can continually evolve.

Internal and external pressure continues to mount for college professionals to provide evidence of successful activities, programs, and services, which means that, going forward, nearly every campus professional will need to approach their work with a data-informed perspective.But you find yourself thinking “I am not a data person”.Yes, you are. Or can be with the help of Amelia Parnell.You Are a Data Person provides context for the levels at which you are currently comfortable using data, helps you identify both the areas where you should strengthen your knowledge and where you can use this knowledge in your particular university role.For example, the rising cost to deliver high-quality programs and services to students has pushed many institutions to reallocate resources to find efficiencies. Also, more institutions are intentionally connecting classroom and cocurricular learning experiences which, in some instances, requires an increased gathering of evidence that students have acquired certain skills and competencies. In addition to programs, services, and pedagogy, professionals are constantly monitoring the rates at which students are entering, remaining enrolled in, and leaving the institution, as those movements impact the institution’s financial position.From teaching professors to student affairs personnel and beyond, Parnell offers tangible examples of how professionals can make data contributions at their current and future knowledge level, and will even inspire readers to take the initiative to engage in data projects.The book includes a set of self-assessment questions and a companion set of action steps and available resources to help readers accept their identity as a data person. It also includes an annotated list of at least 20 indicators that any higher education professional can examine without sophisticated data analyses.

Lead your organization into the industrial revolution of analytics with The Analytics Revolution The topics of big data and analytics continue to be among the most discussed and pursued in the business world today. While a decade ago many people still questioned whether or not data and analytics would help improve their businesses, today virtually no one questions the value that analytics brings to the table. The Analytics Revolution focuses on how this evolution has come to pass and explores the next wave of evolution that is underway. Making analytics operational involves automating and embedding analytics directly into business processes and allowing the analytics to prescribe and make decisions. It is already occurring all around us whether we know it or not. The Analytics Revolution delves into the requirements for laying a solid technical and organizational foundation that is capable of supporting operational analytics at scale, and covers factors to consider if an organization is to succeed in making analytics operational. Along the way, you'll learn how changes in technology and the business environment have led to the necessity of both incorporating big data into analytic processes and making them operational. The book cuts straight through the considerable marketplace hype and focuses on what is really important. The book includes: An overview of what operational analytics are and what trends lead us to them Tips on structuring technology infrastructure and analytics organizations to succeed A discussion of how to change corporate culture to enable both faster discovery of important new analytics and quicker implementation cycles of what is discovered Guidance on how to justify, implement, and govern operational analytics The Analytics Revolution gives you everything you need to implement operational analytic processes with big data.