The Occupiers industry is flooded with occupancy sensors solutions, but is it right? In the last few years technology industry has spent a vast amount of time and energy to preach that monitoring desk usage to the 1/10th of a second is the right way forward, but is it?
For years we keep hearing about “smarter buildings” and promises that technology will provide all the answers. The sophistication of those technologies keeps increasing, as does the price, but has anyone thought if similar results can be obtained in a different manner? How cost-efficient are sensors in comparison to –well conducted- utilisation surveys? How different is the data?
There is a need to question and challenge the systematic technological overkill surrounding the workspace management industry. It wouldn’t hurt to be a bit smarter about how we use technologies. Maybe we don’t need smarter building as much as we need smarter users (#aouchy)?
One thing known for sure is that sensors do not capture human behaviours. Don’t we expect getting a human element when asking for occupancy intelligence? Any workspace designer would agree that in order to design suitable workspace better it is vital to know how it is being used. Knowing who sits where and how long for only provides a fraction of the information needed and doesn’t give any insight on what people do when they work. Do they interact with each other, do they focus, do they engage with technology, do they use the expensive smart-wall in their meeting room, do they use their video skype or just audio?
Those are the real questions that will be translated into a perfectly suited workspace design, not a “was that person there – yes/no”. Knowing this, if sensors are used it means a behavioural survey will need to be conducted in parallel probably by another consulting firm, which doubles up the costs.
Maybe good utilisation surveys should be setup by workspace designers to ensure the right data is being captured, it should be carried out by people to ensure human behaviours are observed accurately, and it should be scrutinised by data analysts because it is not as simple as it seems.