In times of financial exigency, institutional leaders need to scrutinize and justify all major expenditures or investments. Fortunately, making genuine commitments to deploy and leverage the investment in analytics in particular ways can be shown to generate substantial return on investment.
Return on investment (ROI) measures the tangible, measureable outcomes that result from an investment such as technology. Productivity gains, reduced or deferred costs, reduction in staff resources applied to particular processes, and increased revenues are examples of tangible returns that can result from technology investments.
An even more expansive concept is value on investment (VOI), which measures both the tangible and intangible outcomes that result from leveraging technology. Our experience has shown that most of the tangible outcomes come from productivity gains. On the other hand intangible value comes from a combination of new collaborations and innovations that change the dynamics of the institution and its relations with students. This can lead to enhanced approaches to strategic enrollment management, reorganization and restructuring of administrative units, and reshaping job responsibilities. These can enhance the institution’s competitive position and capacity to attract and retain students. Such intangible value often results in tangible outcomes, like increased enrollment and revenues, within a period of time.
We first deployed the concept of value on investment at Eastern Michigan University, in 2002-2004. We measured the impact of utilizing an aggressive combination of strategic planning, the implementation of an integrated ERP suite, leveraging the ERP system to reinvent processes and practices, and enhancing data governance/stewardship. In particular, we demonstrated the new ERP and related process reinvention generated millions of dollars in annual savings and very favorable ROI and VOI. The basic approach is described in a white paper for the EDUCAUSE Center for Applied Research (ECAR) Value on Investment in Higher Education (http://net.educause.edu/ir/library/pdf/ERB0318.pdf).
In considering today’s higher education’s analytics environmnet, the killer app for analytics is retention and student success. By making an analytics-driven improvement of a few percentage points in freshmen-to-sophomore success rates and four- and six-year graduation rates, institutions can justify significant investments in analytics and in retention-support services.
Virtually every institution has some sort of serious program to improve student retention and success. While all use information to measure outcomes, many do not use a full range of reporting and intervention tools to actually scrutinize and reinvent the processes and practices that affect success. On the other hand, leading institutions are using analytics in predictive modeling and policy making to improve student performance. The excellent article in EDUCAUSE Review on “Academic Analytics” by Campbell, DeBlois and Oblinger offers many examples, including Baylor University, Purdue, the University of Alabama, Sinclair Community College, and Northern Arizona University of institutions using predictive analytics in recruitment and shaping policy. http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume42/AcademicAnalyticsANewToolforaN/161749
Some leading-edge institutions have taken an even more aggressive approach to analytics. Cuyahoga Community College has deployed a sophisticated analytics application, which they call institutional intelligence, to provide standard reports, ad hoc reports, query, interventions, and statistical analysis capabilities that can be used to improve the success of at-risk students. CCC has used a home-grown solution, built with the help of data warehouse consultants ans using Microsoft Sharepoint/PerformancePoint to provide low-cost “analytics for the masses.”
In a similar vein, institutions like University of Maryland Baltimore County and University of Maryland Eastern Shore have deployed iStrategy’s pre-packaged analytics application to enable dynamic viewing of at-risk students and actively drill down and intervene when conditions warrant. Such applications enable institutions to deploy analytics to measure student retention and success, predict which students are likely to be successful, shape policy to deal effectively wiuth at-risk students, and actively monitor and intyervene with students whose performance or level of engagement suggests they are at risk.
Put simply, most institutions use improving retention and student success as their analytics killer app. Such applications can deliver strong value propositions. In future blogs we will offer more examples of institutions taking an aggressive approach to retention. We will also offer broader examples of how other types of analytics can yield Value on Investment.