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…

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.

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

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…

Despite revolutionary changes in technology, the challenges facing institutional research professionals remain remarkably the same as they were a decade ago. They are still meeting disparate stakeholder demands, developing new skills, adopting new roles, and finding ways to communicate complex information through clear presentation of data. Then…

In today's accountability climate, regional accrediting bodies are requiring colleges and universities to develop and sustain a culture of evidence-based decision making and improvement. But two-thirds of college presidents in a 2011 "Inside Higher Ed" survey said their institutions are not particularly strong at using data for making decisions.…

This chapter serves as a beginner's guide to some essentials of human resource faculty data and cost data and their integration into products to facilitate institutional decision making. It begins with a brief overview of general higher education cost data concepts, followed by a similar synopsis of relevant higher education human resource data.…

Putting data-driven decision making into practice requires the use of consistent and reliable data that are easily accessible. The systematic collection and maintenance of accurate information is an important component in developing policy and evaluating outcomes. Since 1982, the National Collegiate Athletic Association (NCAA) has been collecting…

This chapter presents a sophisticated approach for tracking student cohorts from entry through departure within an institution. It describes how a researcher can create a student tracking model to perform longitudinal research on student cohorts. (Contains 3 tables and 2 figures.)

"Building Institutional Capacity for Data-Informed Decision Making" is the third installment of the Cutting Edge series, which aims to help colleges engage faculty, scale successful interventions, and create a strong culture of evidence through use of data to strengthen their institutional change and student success efforts. Though the…

This chapter describes several examples of institutional use of state- and college-level tracking data to benchmark performance, improve student success, and enhance program effectiveness.

This chapter describes the National Student Clearinghouse and discusses opportunities and challenges for tracking community college students. It also presents a system perspective on using clearinghouse data to promote more comprehensive student and graduate tracking. (Contains 2 tables.)

The accuracy and reliability aspects of data integrity are discussed, with an emphasis on the need for consistency in responsibility and authority. A variety of ways in which data integrity can be compromised are discussed. The following sources of data corruption are described, and the ease or difficulty of identification and suggested actions…

Models for college and university Interinstitutional data sharing are redefined by examining the organization and activities of existing data-sharing groups across 11 dimensions: data-sharing purpose; nature of data-sharing structures; definer of process; calendar; scope of surveys; participant characteristics; source of data-sharing initiative;…