In an era of unprecedented connectivity, we live and breathe information. Data can be—and very likely is—part of everything we do—from purchasing a coffee, to clocking in at work, to hopping in a Lyft, to binging a show on Netflix. The built environment is also no stranger to data, with IoT devices like energy management solutions and wearables making buildings and employees smarter about how environmental preferences can be minutely managed.
As an analyst for Perkins+Will’s Corporate Interiors practice, I help push the boundaries of how we use data to inform design choices. At its simplest, analytics in our industry is not all that different from analytics anywhere else. But often, the way we approach data and information science is tailored to a specific client, and therefore, more unique. For example, a client may want to evolve their workplace, but will require a reality check before diving into design innovations. And many of our clients crave insights on cutting-edge design strategies, like activity-based work, or emerging space typologies, like workplace makerspaces.
A Tool to Inform Design Decisions
In order to satisfy this desire for information, our team developed Indicator, a benchmarking analytics tool that captures comprehensive information on our workplace projects. Drawing on data inputted by our designers, this tool catalogs information from completed projects and compares it with a library of other projects. The ultimate goal is to have a robust set of projects—across many industries, of various sizes, and composed of diverse space typologies—to enable understanding of trends and industry standards. This data set opens up a lot of possibility for the firm for marketing, research, and creativity; it tells a story, and that is powerful.
The inception of Indicator was predicated on an increasingly savvy consumer market that is driven by knowledge, evidence, and peer review. By and large, online shoppers are moving away from brand and retailer loyalty and toward the credibility of independent consumer reviews because of the power of knowledge and data. The worlds of Yelp and Amazon have made all of us smarter shoppers who need proof that a product is high quality and a good value. As such, our tool allows us to answer questions with empirical and impartial data. Law firm client wants to know if their competitors are moving to universal offices? We can tell them. Client is considering shifting workstation square footage to shared spaces? We can give them both the average allocation of individual versus shared space for their industry as well as tell a more nuanced story with individual project context.
Many have by now heard data scientist Clive Humby’s famous words: “Data is the new oil.” As crude commodities, both oil and data must be changed in order to derive the most value for consumers. And while the possibilities for data are seemingly endless, most of it goes unnoticed or uncollected. Between 2013 thru 2015, more data was generated than the entire history of the world combined! In 2013 the world had about 4.4 zettabytes of data generated. In 2020 it’s estimated to reach 44 zettabytes, or 44 trillion gigabytes of information. Increasingly, the processes, software, websites, devices, and workflows we use leave behind bits of information, most of which can be analyzed but usually isn’t because it’s unstructured or the tools needed to make sense of the information are not well developed.
On the other end of the spectrum, computational design provides real-time feedback on design processes when plugged into tools like Power BI. Elements like programming, stacking, building performance, materials analytics, cost calculations, and environmental assessments can, with increasing ease, be patched into the design process to respond to actions in building information modeling (BIM). Perkins+Will has method and models for this type of work in development currently, but there is still a challenge in capturing metrics, storing data, analyzing, and then linking this to design. Overcoming the hurdles of managing definitions, maintaining a flexible data architecture (the actual construction of a database or data file), and interpreting that information requires extensive work upfront. The progress made thus far, however, sets the ground work for exceptionally smart design processes for the future that provide both clients and designers with the insights needed to make smarter decisions at a faster pace.
If it’s any indication of what the future will hold, consider how quickly the field of analytics has grown on the whole. For an “analytics” approach to the growth of analytics, consider this: The term data science has really only been in the mainstream for a decade and a half, and the field of analytics is so (relatively) new, that most people working in the field don’t hold degrees in analytics. As practices, methods, and education catch up with demand, the field will become more applicable to tangential vocations, such as design, and bring deeper understanding to our industry.