This post is an update of an article originally published in Leesman Review #23 – July 2017

We read much about the availability of data, but the richest findings are where different data sets are allowed to overlap.

In our experience, business heads are increasingly seeing functionally effective and engaging workplaces as vital for competitiveness. But large organizations are also looking for a data-driven approach to underpin workplace design decisions. The subjective responses of the sort Leesman collects, of building occupants to the realities on the ground remains essential. But paired with new sensor-based data inputs and machine-learning algorithms, clients, and their advisors now have a breadth of evidence available for decision-making that was never before available.

Merging user perception and measurement data gives a powerful diagnosis for workplace optimization, both from a cost and comfort perspective. It can also yield detailed insights into the root causes of dissatisfaction. A low satisfaction score for meeting rooms doesn’t say whether there are too few of them, if they lack equipment or if they are too hot or too cold. And an activity such as ‘planned meetings’ might score low due to a lack of suitable spaces; but also because of a cumbersome reservation system or even because people feel these meetings are a waste of time.

Data capture and analysis

Micro-level data can be captured from different sources:

  • Human data collectors can measure occupancy, activities, interactions and noise levels. The ABOOT™ methodology patented by MCS, for example, measures these parameters frequently at all formal and informal working and meeting spaces.
  • Internet of Things devices can permanently monitor variables such as:
    • Space utilization (through PIR occupancy sensors, footfall cameras for people-counting or through indoor positioning)
    • Workplace comfort parameters: temperature, light, air quality, noise levels and so on
    • Energy consumption
  • Instant user feedback can be gathered through happy/not happy buttons or touchscreens.

Captured in a big data platform, where it can be processed, analysed and visualized to reveal correlations and patterns, the output is quickly put in context and provides insights into how the workplace fits user needs on a more granular level.

data-driven workplace decision-making


Workplace health check

Most of these studies presently happen around a capital project and are designed to inform new design projects. These fleeting, momentary examinations are valuable, but would be even more so if the relevant tools remained post completion. As sensor technology costs tumble, we should see take up increase beyond specific capital and design projects. Further boosting take-up will be employees slowly accepting that sensors are not there to measure them, but their environment and how well it supports them. Few, if any, organizations are static. They develop new services, streamline processes, enlarge divisions and dispose of others.

But how often is the space tested alongside, to see if it still meets the functional needs of each part of the business? Increasingly, we believe we will see this happening annually as part of a workplace health check. Thereafter, we believe leading businesses will progressively move to constant, real-time monitoring. These organizations will invest in tools that optimize the workplace’s functional effectiveness and help it contribute to the company’s wider performance.

About the author:

Steven Lambert | COO | MCS Solutions

Steven has 20 years of experience in leading operational transformation projects for Fortune 1000 companies in both Europe and the US. After graduating from the Wharton Business School in 2001, he worked with blue-chip tech companies in transforming their service operations. Since 2012, Steven has been COO and partner at software and technology firm MCS Solutions. He runs the global operations in FM consulting and IWMS software implementation.