At work, my team has been working on an extremely intriguing project to see whether or not we could devise a solution to allow us to accurately track the usage of our meeting rooms. The initial project was intended to simply help individual contributors find a currently open space in which to meet with a coworker for an impromptu meeting, but it has grown into much more. As the data has started rolling in, we are discovering the ability to analyze room usage and actually advice facilities management on which areas could stand some additional optimization.
Charted above is an example of the data we have been able to start visualizing. While I cannot share actual internal Disney data, this sampling is representative of the type of metrics we are gathering. Interestingly enough, when we overlay the data collection with the bookings of the rooms in Outlook, we can start identifying trends with people who might book rooms on recurring schedules but seldom use their allocated time.
Our project is still in its infancy, and in fact, has only been rolled out to the first few floors in our building, but we are already seeing some pretty amazing data coming in. It’s always exciting when you see a concept embodied and deployed, and this is no exception. However, I have been re-learning a valuable lesson with this task: when trying to solve one particular problem, don’t allow yourself to be blinded to other possibilities within the same space!
So, what was the big project that led to the data we’re gathering? Let’s just say it’s my first foray into the IoT (Internet of Things) space. We built a motion detection system with some interpretive learning algorithms using Raspberry Pis and Node.js. I may write more specifics on that topic later, but in the meantime, I’ll be presenting the techniques we leveraged at Nodevember 2016. Hope to see you there!