What We Did
We integrated and analyzed project data collected from Gensler’s Activity Analysis, Workplace Performance Index (WPI), and 4-1-Where datasets. They represent millions of data points related to workplace effectiveness, experience, and performance; observations on workspace usage; as well as real estate vacancy, occupancy, and space allocation.
We tagged these datasets to identify project location and industry information, which allowed us to analyze our proprietary datasets alongside market economic data within ranges of project proximity. We then used statistical software to test for correlations among workplace performance, occupancy, space use, and the markets in which projects reside. By mining this data, our goal is to uncover hidden relationships that can deliver value and project decision support to our clients through a better understanding of those factors that impact design effectiveness.
“Big Data” commonly refers to huge volumes of data coming from multiple sources. The core undertaking is to link, match, cleanse, and transform data across databases and systems in order to connect and correlate relationships, hierarchies, and multiple linkages before the data spirals out of control. By governing data in this manner, it is possible to integrate structured and unstructured data assets to produce high-quality information and insights that are useful and timely. Theoretically, firms that can extract value out of the data at their disposal will have greater insight into the forces affecting their business and their clients’ businesses.
Initial results from our exploratory analysis showed that generational and geographic differences are significant. On average, today’s design solutions appear to be more appealing, as measured by Gensler’s WPI, in locations with larger populations of young, urban professionals. On the other hand, analysis of space utilization data gathered by Gensler’s Activity Analysis showed that older, wealthier, or more “seasoned” professionals tend to spend more time at their offices/ desks than average. Findings also suggest that interior elements can be used to enhance, or substitute, for attributes or deficiencies in the physical environment.
What This Means
Data delivers predictive insights. Combining data from Gensler’s Activity Analysis, WPI, and 4-1-Where with local demographic data delivers predictive outcomes, enhancing our ability to gauge workplace satisfaction across geographies and generations in relation to workplace strategy.
Location matters to workplace performance. Geographic analytics using Gensler’s internal data represent a robust diagnostic tool for assessing site selection. Our results show that location has a significant effect on employee experience and performance. This knowledge is an opportunity to inform real estate decisions as companies relocate to attract or retain top talent.
Design is an opportunity to mitigate variance by location, demographics. Big data analysis uncovers a richer, integrated understanding of design’s impact on employee satisfaction, productivity, and cultural and geographic fit, with an added benefit of delivering future-forward insights.
This is just the tip of the iceberg. Our initial efforts serve only as a starting point, upon which we are building capabilities for project- and location-specific analysis in support of client decision-making needs. We continue to evolve our ability to capture and manage project-related data to be more conducive to big data analysis. We see exploratory analysis of our data as an ongoing opportunity to uncover interesting insights or questions that will drive further research investigations.
Chris Jerde, Wesley LeBlanc, Andreas Andreou