Harnessing Data for Conversations around Space Utilization

Mitchell Bobman, Analyst, Gensler | Wes LeBlanc, Director of Analytics, Gensler

Over the last two years or so, we’ve been asked to meet with a growing number of clients to discuss sensors, workplace utilization, and our experience in related applications. This has given us the opportunity to codify our thinking and recent work in this space. What follows is a summary of our current perspective. For many clients, utilization, and the potential use of sensors to gauge utilization is a focus, with secondary consideration given to other issues. This is common, as clients currently see a direct, provable relationship to standard utilization metrics, and a less provable relationship to other performance metrics such as talent attraction and retention, productivity, etc.

There are dozens of sensor providers competing for viability in an ambiguous market place. Due to a combination of different users, use cases, and technology, embedded sensors can take on many shapes, sizes, and functions. Despite the range and variety of these existing vendors, there is a surprisingly low number of proven case studies and implementations, and even fewer have proven commercial impact or viability at scale. When asked about the use of sensors in work environments, we generally find it most useful to step back and examine the questions being asked of the data being developed.

Choosing Spots and Asking Questions

An oft under-appreciated early step in a rigorous analytical process is to ask questions or define a hypothesis (or multiple). This step can often be just as, if not more, important than the data collected. Spending the time to develop valuable and important questions helps to give meaning and purpose to the data you collect. Some of the typical questions asked by clients include:

• How can we understand the utilization of our workplace over time?
• How can we identify the optimal size, quantity, and location of collaborative spaces and shared amenities?
• How can we prioritize (and re-prioritize) projects over time?
• How can we optimize the phasing of moves/ consolidations/ expansions across all of our facilities?
• How can we better optimize our work flows and processes across our organization?
• How can we improve health and wellness across our organization?
• How can we improve our net promoter score?

The identification and categorization of questions allows for analysts to have frameworks to test, as well as align, any research or exploratory exercise with an ultimately valuable answer.

Layering the Data Sources

Current work generally sources data from five alternatives: facilities occupancy and management, observational, sensory, exhaust, and secondary.

Facilities Occupancy and Management

• Generated by facilities management systems
• Typically used to manage occupancy and locate personnel
• Often misrepresents actual utilization and occupancy throughout a portfolio
• Inability to solve for nuanced questions related to utilization and occupancy (e.g. mobility)

Observational

• Typically generated by space surveys — either completed by surveyors or self-reported by employees
• Can be well aligned to the types of questions that organizations are trying to answer
• Surveying across a large portfolio and at a high frequency is typically only feasible on a periodic basis

Sensory

• Data generated by sensors implemented in a physical space
• Most commonly used technologies are infrared, computer vision, and Bluetooth Low Energy
• The granularity and accuracy varies by the type of sensor
• Can often collect detailed metadata about physical spaces such as noise, brightness, temperature, and humidity
• Rapidly-evolving market makes it difficult to understand the current capabilities and costs of different technologies

Exhaust

• A byproduct of existing operational systems already used by an organization, generally found in systems related to security, finance, operations, HR or IT
• These data streams, such as wifi utilization, room reservation systems, and IT log-ins, are typically the most scalable across large portfolios
• Data does not always directly align with the questions being asked by an organization
• Often requires a framework to export, structure, and analyze it for the appropriate purpose

Secondary

• Publicly available or subscription-based data used to supplement the analysis of a portfolio
• Often includes information on demographics, costing, benchmarks, and real estate market performance.
• Typically requires subscriptions and/or knowledge of how to extract and scale data for a specific project purpose.

These different sources of data can be used for a number of analyses with varying degrees of strength:

So There’s No Silver Bullet, Now What?

Since no ‘silver bullet’ solution currently exists, or will for some time, it has been our experience that the most efficient and successful way to help workplace clients answer key questions with quantitative analysis is through layering. When layered strategically, data can be leveraged by institutions and organizations to better inform decision making. One client was relieved to better understand the opportunities and limitations of the current landscape… commenting “so I’m not crazy”.

Observational, sensory, and exhaust data represent a large kit of parts to pull from, adding to current facilities information and secondary data. But a changing landscape of providers and few proof cases for scalable sensor applications suggests that answering workplace questions will, for now, remain an exercise in combining multiple sources and uses of information. The ability to identify and sharpen questions, and reach into the kit of parts to help formulate answers will remain critical.

Within the North Central Region, we are concluding a year-long process of testing and applying these and other methods with both our clients and in one of our own Gensler workplaces. We plan to elaborate on our thoughts, experiences, and findings once complete.