The value of data-driven environmental insights.

Tom Spurrier

Tom Spurrier, Senior Associate

Optimising our workplaces.

Sensing technologies and intelligent data analysis are both becoming more sophisticated and cost-effective The opportunities of this for the built environment are far reaching. To start to explore this, we’ve been working with one of our key clients to help them understand and optimise their workplace.

The study allowed us to trial various sensing systems associated with building performance.

Added to this, it meant we could test the aggregation of all data into a single portal. The idea being that access to the data would be unrestricted to enable data analytics and the development of optimisation strategies.

Sensor data.

We deployed low-cost commercially-available environmental sensors. Over three months, the sensors logged air temperature, humidity, CO2 levels, volatile organic compounds (potentially harmful chemicals given off by everyday products), and particulates (very small pieces of solids, mainly carbon). The accuracy of the sensors was spot-checked with specialist calibration equipment.

  Research indicates a correlation between good environmental conditions and productivity, innovation and profitability.  

The data allowed the workplace’s internal environment to be benchmarked against industry standards. The results showed that the general conditions were in-line with industry guidance, with some localised anomalous results. These anomalies highlighted the areas where further investigation into the performance of the building services needed to take place, which we are now supporting.

Notably, the benefits of trend identification far outweighed any concerns over absolute accuracy of the low-cost equipment.

Ultimately, the study allowed our client to identify improvement measures that would enhance user satisfaction and productivity. It also demonstrated that real data can allow for the identification of trends and performance issues that otherwise would be assessed subjectively, or not at all.