Different types of analytics provide different types of insights, so it is important for facilities managers to understand what each type delivers. Then, you can match analytics functions to the operational capabilities across portfolio management functions.

Three principal types of analytics solutions exist:

Descriptive analytics uses business intelligence and data mining to help you determine what has happened. Using descriptive analytics, facilities leaders can mine data to provide trending information on past or current events that can give them the context they need for future actions.
Predictive analytics uses statistical models and forecasts to help you determine what might happen. With this information, you can plan ahead, rather than react to events after they occur.
Prescriptive analytics uses optimization and simulation to help you determine what to do next. It takes uncertainty into account and recommends ways to mitigate the risks that might result from an action. The ability to not only examine potential outcomes but also make recommendations helps executive managers decide when a data environment is too large or complex to be understood without the help of technology.

These three types of analytics build on one another. Descriptive analytics is the most common, and prescriptive analytics is the most advanced. Yet all of them can help you improve real estate, facilities and asset operations. They each have capabilities that help you understand an event or action, uncover relationships in data, develop what-if scenarios and simplify business decisions. Today, what is in place is mostly basic descriptive capabilities from building automation systems. To achieve the potential enterprise value from predictive and prescriptive modeling, broader data sampling is required across a wider array of sources and processes. Hence, most organizations are in the awareness and evaluation stage of these methods. New comprehensive data management solutions can help them move into the adoption and application stages.

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Figure 2: Types of analytics

Adapted from: Competing on Analytics, 2007.

UMass Amherst integrates space-use data
The University of Massachusetts in Amherst needed to automate and integrate previous manual processes and disconnected systems that hampered facilities planning, space management and indirect cost recovery. Implementing workplace management system software for provided space planning decision support, aided in indirect cost recovery and automated space-use information management. They were able to reduce the schedule for a 250 building space management project from three years to one – a 67 percent reduction in time to occupancy.