Data-driven solutions.
That is the goal of almost every real estate transformation plan these days. Of course, it has also been the plan for every business-oriented program for the past twenty years or more. It is the goal that gave rise to the roles of business analyst, data scientist, and operations researcher. If we are going to make data-driven decisions, we need both data and people to analyze it.
Sometimes logic leads us to an answer we are not able to deliver. Real Estate data is some of the most difficult to analyze within a corporation and also the most difficult to gather. A data analyst without basic real estate knowledge could very easily look at the data and draw the wrong conclusions. Data about office occupancy is a reflection of nothing if not human psychology.
The way employees use an office is most often a function of their preferences as people. It is often easy to think about employees as functions of their business activity. Marketers do this. Salespeople do that. Admins like to work like this. While the business role affects how people work, it is their core preferences that most come through.
If you step back, this conclusion is inevitable. Most big vacations take place around July and August because it is the height of good weather and children are out of school. It has nothing to do with the business cycle, it is down to personal preference. If anything, the business cycle adapts itself to these preferences. Parents who are responsible for dropping a child off for school are often going to be later to work than others regardless of their business function. Weather often plays a role in the general behavior around occupancy.
However, none of this behavior is inherently obvious in the data collected around our offices. Occupancy data does not indicate demographics or personal preference. It might be indicative of these things, but only indirectly. This is because no two people are the same. An office