In June 2019, Minnesota State set a critical goal: By 2030, eliminate the educational equity gaps at each of the 30 colleges and 7 universities that comprise the system. To achieve the goal of Equity 2030, and empower actors at every level of Minnesota State, system and campus leaders need to fully embrace data democratization. Without access to…

While many campuses have access to and publish equity-focused data, it can be difficult to share that data with the campus community in a way that engages multiple offices. This article outlines the Equity Liaison Initiative at the University of Wisconsin--La Crosse, which engages individuals from most offices on campus with equity-focused data to…

As institutions seek to shift into more advanced analytics and data-based decision-support, many institutional research offices face the challenge of meeting the office's current demands while taking on more intricate and specialized work to support decision-making. Given the great need organizations have for information that supports real-time…

Building a culture of data governance at a higher education institution involves collaboration across the entire institution. Before the creation of formalized roles to perform data management and data governance functions at colleges and universities, these functions were performed by traditional institutional research personnel. Documentation…

Evidence-based decision-making requires access to good and reliable data. In recent years, there has been a tremendous increase of data available to institutions, from internal as well as external sources. With this increase in data and its analytics, new challenges and questions arise. One of the most pertinent of which is the ethics and best…

As institutions of higher education increasingly look to data as evidence to support planning, allocate resources, and inform teaching and pedagogy, ethical considerations regarding learning analytics have evolved from being on the margins to more central in the conversations surrounding institutional uses of student data. After outlining this…

Ethical issues are structurally present in the design and choice of data; the interaction between problem, model, and intervention; and the scientism that supports data analytics' claims to authority. In this chapter, I argue against three social assumptions behind common implementations of learning analytics: data realism, technological…

Data continues to grow in both importance and quantity within colleges and universities. IR professionals need to know ways data can create potential legal issues in order to be proactive rather than reactive to these situations.

Human resources (HR) represent the most significant investment on most of our college campuses in terms of time and financial resources. The importance of good data about the workforce begins with establishing a need for a new position. That need for data continues with recruiting, hiring, training, promoting, retaining (or not), and retiring…

This chapter describes how data analysis and data-driven decision making were critical for designing, developing, and assessing a new academic program. The authors--one, the program's founder; the other, an alumna--begin by highlighting some of the elements in the program's incubation and, subsequently, describe some of the components for data…

This chapter utilizes the duties and functions of institutional research to outline the skills needed by institutional research professionals in an evolving digital institution. The concept of future-proofing, explained and considered in this context, encourages professionals to develop skills for the present with the future needs of data-informed…

This chapter discusses principles that support using data about teaching and learning and offers several strategies particularly well suited for use by institutional researchers at small colleges in helping faculty, staff, and administrators use data on student learning for improvement.

Strong relationships with senior administrators, effective cross-campus collaboration, and efficient use of technology are central to impactful IR in small colleges.

This chapter presents various definitions of big data and examines some of the assumptions regarding the value and power of big data, especially as it relates to issues of equity in community colleges. Finally, this chapter ends with a discussion of the opportunities and challenges of using big data, critically, for institutional researchers.

To whom should higher education institutions provide data, and what is the impact of transparency on institutions?