The techniques and advantages of occupancy analytics are valuable when used in a project context: to help with the space planning for a restack; to establish demand for a relocation; to evaluate the feasibility of a consolidation.
But imagine having that kind of detailed, robust and reliable occupancy information at your fingertips continuously?
Operationalizing analytics is the process of increasing the quality of the inputs to your occupancy analysis by incentivising your people to use the systems appropriately.
A simple example would be disabling multiple-badge-in on your physical access control system. This would prevent one person using their security badge to let others into the building ahead of themselves, and in turn help to ensure that the badge data gives a more accurate picture of who is in the building.
These techniques can be applied to many sources of data typically used in occupancy analytics, and can range from the simple example above, to more sophisticated integrations that would – for example – enable unused neighborhoods or floors to run on low-power settings when unoccupied.
By operationalizing the data in this way a virtuous circle is created: people using the systems appropriately receive a better customer experience in the building and provide more accurate occupancy information, which in turn allows better decisions to be made about what the people really need.
And this ultimately means you can continuously rely on the data quality underpinning your analytics without having to constantly invest in data quality checking and remediation, transforming occupancy analysis from an adhoc exercise to a continuous part of your workplace management information.