One of the blessings of doing data analytics on real estate/workplace data is that you get to see some truly unique datasets and trends. One of the curses of doing data analytics on real estate/workplace data is that you have to figure out how to explain what those trends mean to people who don’t love data as much as you do.
One of the best examples of needing to explain real estate data is around the concept of “peak day.” When designing a workplace, you design for the theoretical “peak day” which is the day of highest occupancy. Depending on the nature of the work in the office and the region of the world you are in, this day will vary in both occurrence and magnitude. Many offices I’ve studied have a peak day that is 20 to 30% greater than the average occupancy and it occurs every 2 to 3 months. Meaning, if you design for average you will dramatically come up short on seats. If you design one seat for every person that could be there on a peak day, your average occupancy rate will appear low. It’s seemingly a no win data analytics problem.
Here’s the thing about data, it doesn’t provide answers on its own. Data is a tool that can help you test hypotheses, predict operational behaviors, and measure solution risk. Data cannot tell you the “right” answer. Two people looking at the same dataset could easily draw opposite conclusions on how to move forward. It happens every day with every dataset. Some believe you design for the worst day, others believe you design for just shy of the peak day, still others say to build “flex” seats to accommodate the peak day. The data can justify any of those directions.
The real test is in the detail of the solution and the processes in place to help make the solution a success.