Industry View: Steven Charlton

Steven Charlton is the CEO and founder of I/O Atelier, a company that blends creativity and hard data to design buildings and places that bring out the very best in people.

The best way to tell a story is with a beautiful design built upon great data.

Can you share a valuable insight that has influenced your approach to occupancy studies?

Blending this SUS data with raw data from smart sensors has provided really strong data led decision making which has been especially important to our clients planning for a post Covid / WFH blended environment where predicting future trends has become increasingly difficult.

What do you predict will be the primary change in industry behavior over the next decade?

We believe there will be deep data analytics from multiple platforms coming together and helping us build predictive models of space usage before we start building - data led design is the way forward.

How do you balance technical data analysis with understanding human behavior in occupancy studies?

Data is used to augment our design flair and understanding of human nature - one should not be done without the other and we don’t see a future where humans won’t be integral to the process of providing quality space for other humans.

Can you provide an example of how occupancy data has been used to enhance workplace efficiency and employee satisfaction?

It’s very difficult to correlate efficiency or satisfaction to the design we have done and I don’t think any design firm can truly say they know it was their design or the data that was underpinning the design interventions that was the reason for the change and that’s why moving to circularity in data/review post occupation using smart building sensors is the future - we need to track the change in baseline data and then see how it changes over time.

I’ve always believed that creativity and data can happily co-exist. This belief gradually matured into a vision for a different design process: one where every architect, every designer, every data scientist, is free to challenge the way we’ve always done things.