Predictive analytics is the process of forecasting future courses of action by analysing historical and current facts. It's now a priority for most organisations because it can suggest the most favourable future planning by letting decision makers combine data about the four W's--that is, what, who, where, and when--to analyse the why and how. Apart from business, it plays a vital role in higher education planning. Higher Education is now playing a critical role in Australia and New Zealand’s socioeconomic development and indeed in other areas of the world as evidenced by OECD investment numbers. As an educational management tool, predictive analytics can help improve education quality by letting Administrators analyse critical issues in education such as enrolment management and curriculum development but more importantly, for Estate Managers and Facilities Managers, the impact that future enrolment and curricula will have on Campus infrastructure needs.
The momentum building behind analytics for Higher Education is hardly surprising, considering the explosion of technology tools available to make sense of institutional data.
What might surprise you, though, is who on campus is responsible for predictive analytics. Rather than leaving the task to IT (as it been traditionally done), assigning it to Institutional Research or creating a new department, there is a trend toward “self-reliance”; the ability for individuals to create their own reports – from designing, implementing and integrating their own predictive analytics – and this is putting the focus clearly on the software industry to come up with intuitive tool sets that make the job simple.
Internal and external pressures also make it impossible for Universities to ignore the potential benefits of analytics. Financing new building on the basis of projected incoming academic numbers that have unacceptable margins or errors of more that 5% can cost at University hundreds of millions of dollars in lost capital and operating expense over time.
Internally, institutional leaders often want to move from “best guesses” about budget impacts to more accurate predictions about issues that may range from financial aid levels to course enrolment to retention. Space planners and capital planners must move away from the model that assesses future growth based on projected enrolment and apply predictive rigour tests to reduce the margin of error; financing new building on the basis of projected incoming academic numbers that have unacceptable margins or errors of more that 5% can cost at University hundreds of millions of dollars in lost capital and operating expense over time.
Externally, there’s growing government pressure to reduce attrition levels and improve retention (we are not discussing the poor admission standards or lowering of ATAR rankings here!). If retention is improved and admission levels also increase, additional pressure is placed on planners to accurately forecast course type and enrolment numbers over time with space requirements; this requires complex predictive model to avoid costly errors down the track.
There is no one right answer in selecting a predictive analytic solution – no one plug and play tool that does it all. It’s not about chasing the "shiny object" the solves all your problems in one go, but about finding the system with effective outcomes for your Institution. That' not a one-size-fits-all option. It’s about the right fit.
BeyondFM provides advisory services to the Higher Education industry in areas of Infrastructure, Operations and Technology. We specialise in providing Campus Planners and Strategists and Finance Managers with the analyses they need to make the right decisions, first time, in relation to Campus Optimisation. Contact us today to discuss your needs.