The use of data analytics to design working environments

Dr. Herman Kok is CEO and founder of Shign. Shign is a data science company that knows better than anyone what people experience in their physical environment and what it does to them. We help organizations to create sustainably stimulating environments, by indicating evidence-based where opportunities lie for improving the positive impact of the environment on people. We do this based on user experience data analytics in both office environments, healthcare, education and the leisure sector.

In its forty-year history of existence, facility management has evolved into a field where the focus is increasingly on creating added value for the user. Buildings, physical facilities and services must contribute to the objectives of the organization and have a positive effect on the performance of individual users. This is how facility management creates added value.

Creating this added value is a difficult task because we are constantly influenced by the environment in which we find ourselves. Some workplaces evoke positive feelings, in others you want to get away quickly: too much noise, unpleasant indoor climate, insufficient privacy. Although we perceive individual characteristics such as layout, acoustics, odor and light, the whole determines our responses to the working environment. There is a holistic effect.

80% of architects and designers (N = 420) agree that more evidence about the impact of design on users is needed. Only 5% undertake a formal evaluation after the occupation.

The challenge is to design a work environment that effectively contributes to users, processes and organization. After all, with millions of euros at stake, it is wise to avoid expensive design errors in a work environment. And design errors are made easily, for example by designing from an intuition, only aesthetic point of view, following trends and imitation without context, or ignoring the impact of the environment on the user. Design errors are usually characterized by a top-down approach where users are not involved in but are influenced by the final design. Customer satisfaction research and benchmarking - a legacy of FM from the nineties - are often a touchstone for facility design, but fall short because:

Customer satisfaction is a random snapshot of what someone thinks of the workplace based on past experiences. It says nothing about the impact that the workplace has on performance. And precisely because a person's perception fluctuates over time, the snapshot is not informative.

Benchmarking, where a (financial) reference average serves as a criterion, never leads to workplaces that best fit the job to be done within one's own organizational context.

“Housing costs amount to 1 / 100th of the personnel costs per m² of housing. That basically justifies every measure to make employees perform better thanks to the working environment.”

Despite these apparent shortcomings, customer satisfaction and benchmarking are often dominant in design and management, facility managers rely on the signal function. Also sensor data alone does not say enough. Knowing what the temperature, acoustics, humidity and occupancy is at any moment says nothing about how people in that environment are performing. You can only speculate about it.

Evidence-based designs as a solution for the user and facility management

It is impossible to say in advance which aspects of a working environment contribute to user performance. Due to the holistic effect, the hidden aspects can only be revealed through advanced analyses of user experiences over time. Examples of this are:

Users encounter different touchpoints during their journey in the work environment. Each of these contact moments is a unique experience and a moment of truth. At that moment you will need to capture someone's response – in real-time – to find out what the current environment is doing to that person. And that over a longer period.

This form of feedback collection, experience sampling, leads to more and also more mature data through repeated measurements with the same people.

With user experience data analytics we unravel the relationships between the workplace experience, the environmental conditions and user performance, respectively the effectiveness of facility design and performance of suppliers.

“Productivity of employees is 25-55% attributable to their working environment. Then it's better to ensure that the working environment is appropriate and stimulating.”

This makes evidence-based FM possible: design and management decisions based on the best information available from credible research. Systematic user feedback is therefore an integral part of FM's quality improvement. It makes evidence-based implementation of improvements in the working environment possible. This way of user empowerment offers unprecedented possibilities, such as dynamic routing for finding the best workplace and managing cleaning staff, (pro) active adjustment of environmental conditions such as indoor climate and lighting, testing facility concepts and above all - and all of that in the light of - optimizing user performance.