As organizations strive to transition to more complex, higher performing and energy-efficient buildings, it is apparent that traditional building automation systems are not up to the task of complex data management and analytics. Optimal performance for today’s building operations and facilities management requires deeper insights across systems and the enterprise. Yet, as with many organizations, building management is done on a proprietary system-by-system and localized building-by-building basis. Older building automation systems suffer from limited or no integration capabilities, inadequate analytic tools, proprietary data interfaces, and a cacophony of unintegrated software applications and user interfaces.

Today, many organizations are limited in what they see and know by proprietary systems. The new paradigm shifts the power of information to your control.

New data and analytics tools offer a more holistic analysis of overall performance. If you integrate data and analyze it across the many spaces and systems of a building over time, you can gain insights that lead to better building management.

Integrating all that data across a wide area such as a city, a university’s campuses, an airport, or a medical center complex provides even greater opportunities for improvement.

For example, in many buildings, rooms are equipped with motion sensors that switch of the lights when they are unoccupied. Motion sensor information can be analyzed across an enterprise to lead to better strategic facility planning. Occupancy data can reveal that many conference rooms are always empty, so the space can be used for something else, subleased or consolidated. Traffic monitoring on elevators or hallways can reveal patterns to discover underutilized space or to synchronize daily space conditioning with work station assignments for employees working remotely. Finding ways to do the same work with a smaller footprint and greater efficiency is one of the best ways to cut facility costs.

Data analytics can help you normalize data across systems, buildings and practices. The problem is that every building is different, depending on its purpose and space types, when it was built, and other specifics like the type of equipment it has in the mechanical room and the air handling units on the roof. This variability leads to different levels of operating integration and efficiency. Plus, facility operations are as variable as buildings are. In one building, you might have a team that has the expertise to maintain and repair most of their systems, but in another, the work may be outsourced. It is rare for anyone to analyze variables in space, equipment and processes across the portfolio of buildings, except by anecdote. Instead, it is essential to make a shift to the verifiable proof made possible by data analytics.