How to Optimise your Campus with Analytics

February 8, 2018


For Universities that want greater insight into their Campus Portfolio performance, along with a better way to use information for improvement, knowing how to utilise data more effectively in decision-making can present a challenge – especially when focusing on the Student and Staff experience around timetabling and scheduling.


Universities need an analytics solution that can extract meaning from huge volumes of data to help improve decision making, handle wide varieties of data and data sources from within and outside the institution, and keep up with the rapid velocity of data in motion. They need capabilities for analysing historical and real-time data, as well as forecasting the future, to distill what’s valuable, detect patterns and reveal insights they may not even have thought to ask about. With such solutions, they can achieve benefits ranging from increased revenue to lowered operating expenses, enhanced service availability and reduced risk.


To take advantage of the opportunity to transform the dynamic environment of a campus portfolio, however, Universities need analytics capabilities that can identify operating anomalies in real time, predict outcomes and deliver optimal solutions – in real-time.

The goal of any analytics solution is to provide the University with actionable insights for smarter decisions and better business outcomes. Different types of analytics, however, provide different types of insights. So it is important for Leaders and Managers to understand what each analytics type delivers and to match analytics functions to the University’s operational capabilities across its Portfolio functions. Analytics solutions are of three principal types:

  • Descriptive - which uses business intelligence and data mining to ask: “What has happened?”

  • Predictive - which uses statistical models and forecasts to ask: “What could happen?”

  • Prescriptive - which uses optimisation and simulation to ask: “What should we do?”

These three types build on one another, with descriptive analytics being the most common and prescriptive analytics the most advanced. Yet they share goals for improving real estate, facilities and asset operations with capabilities that help provide an understanding for an event or action, uncover relationships in data, develop what-if scenarios and simplify business decisions.


Descriptive analytics


Asking “What has happened?, descriptive analytics mines data to provide trending information on past or current events that can give Facilities Leaders the context they need for future actions. Characterised by the use of key performance indicators, descriptive analytics drills down into data to uncover details such as the frequency of events, the cost of occupancy and the root cause of failure (of timetable clashes, etc). The most common type of analytics used by Universities, it typically displays information within a report or dashboard view. Solutions can be automated to issue alerts when potential problems arise that fit data patterns the solution has discovered.


By examining key metrics and key performance indicators of student attendance, classroom utilisation or distance between classes, for example, descriptive analytics can produce indicators such student attrition rates due to poor campus planning, overall portfolio metrics with cost factors, and decay curve fitting based over semester/ year showing campus space attrition rates. By combining information from different, often disconnected sources and then comparing and contrasting data, descriptive analytics can provide a comprehensive view and context for what has happened, as well as current Campus Portfolio status.


Predictive analytics


Asking “What could happen?, predictive analytics provides answers that move beyond using historical data as the principal basis for decisions. Instead, it helps Leaders and Managers anticipate likely scenarios—so they can plan ahead, rather than reacting to what has already happened.


Using descriptive data accumulated over time, predictive analytics utilises models for predicting events. It does not, however, recommend actions. Predictive capabilities such as forecasting and simulation provide enhanced insight that Leaders and Managers can use to make more informed decisions. Characterised by the use of trends of time-series data and correlations to identify patterns, predictive analytics applies advanced statistical analysis and data mining—as well as sophisticated mathematics to validate assumptions and test hypotheses—to provide a solid, data-based foundation that can raise the Institution’s confidence in conclusions. Leaders might use these results to identify conditions for potential timetable clashes and develop automated resolutions. They might also use them to evaluate the portfolio in terms of capital requirements and develop exacting capex strategies to minimise external funding requirements/ requests.


Studies shows, in fact, that organisations using analytics to determine why and what they need to be doing are twice as likely to outperform their industry peers. These organisations apply predictive analytics for all types of decisions, from daily operations to major business actions.


Prescriptive analytics


Asking “What should we do? - prescriptive analytics explores a set of possible actions and suggests actions based on descriptive and predictive analyses of complex data. Though the final decision is up to University Leaders, prescriptive analytics solutions can provide a reliable path to an optimal solution for business needs or resolution of strategic and tactical operational problems.


Characterised by rules, constraints and thresholds, prescriptive analytics makes use of advanced capabilities such as optimisation and mathematical models to reveal not only recommended actions but also why they are recommended, along with any implications the actions might have. Universities might use these results to identify buildings that can be re-planned “in situ”, with new learning and academic space, or can be disposed of, or redefined as new revenue producing space. Prescriptive analytics takes uncertainty into account and recommends ways to mitigate the risks that can result from it. Its ability to not only examine potential outcomes but also make recommendations helps Leaders and Managers make decisions when the data environment is too large or complex to be understood without the help of technology.


Deploying the right analytics type for your capabilities and needs


From basic to advanced capabilities, analytics can yield dramatic results. One study found that an organisation that uses basic automation to expand its reporting capabilities could improve its return on investment (ROI) by 188 percent. But adding additional capabilities such as data management, metadata to ensure uniform data interpretation, and the ability to gather and analyse data from outside the University, can boost ROI to as high as 1,209 percent. In many cases, the processes and data needed to support these advanced levels of analytics may not be in place. But that does not mean the University should wait. An effective strategy is to select a meaningful problem—such as Timetable (clashes, lock) & the resulting Campus Optimisation opportunities and attack it on a scale small enough to deliver rapid results. The Universities can then begin expanding its capabilities.


What’s important at this stage is not a perfect outcome but a path to iterative results. It is important, therefore, for the University to match its infrastructure, technologies and processes—its level of analytics maturity—to the stage of analytics it is able to perform and the goals it wishes to accomplish. The University should begin with solutions that work with existing data to gain immediate insights while it puts into place the technologies and processes to support more complex analytics.


Reaping the business and operational benefits of the “right” Analytics


Using advanced analytics Universities identify opportunities and quickly optimise their portfolio, increasing customer (student, staff and faculty) service, reducing operating costs and increasing return on assets. Effective facilities and asset management uses data analytics to proactively manage and maintain facilities, optimise utilisation, lower occupancy and operational costs, and extend asset life. Using the right analytics can even monitor energy intensive equipment across the facilities portfolio, identify operating anomalies in real time, and generate corrective work orders can dramatically reduce energy consumption. Identifying issues early helps Universities deploy limited resources more cost-effectively, maximising uptime and improve customer service levels.


For more information


To learn more about how analytics can help optimise your portfolio contact BeyondFM today and tell us your needs or call us at +61 (0) 403 842 480


Share on Facebook
Share on Twitter
Please reload

Featured Posts

Asset Management Technology and You | moving beyond today's systems

April 3, 2019

Please reload

Recent Posts

March 11, 2019

Please reload

Please reload

Search By Tags