The Modern Enterprise’s Data Blind Spots
Since the advent of the modern era, the most successful enterprises have leveraged spatial analytics, or data on the space and workstreams of our physical world, to upend industries. In the early 20th century it was Ford using data points on physical movement in search of a better way to manufacture high quality and low-cost cars — this was further refined by Toyota’s TPS system later on in the 20th century. Google followed suite at the turn of the 21st century with its enormous investment in campus environments to promote knowledge work and engage employees that has transformed the company into a trillion-dollar global behemoth.
A few decades and a global pandemic later and our work environment has come to a reckoning. In addition to increasingly digital knowledge work comes a dispersed global workforce, with both dynamics bringing more questions than answers to enterprises seeking to optimize productivity. While a number of point solutions have allowed for insights to assess workforce productivity, blind spots remain in the data collected on teams. This largely hinders 2021’s response to Google, Toyota, and Ford on how to best optimize productivity. The argument here, though, is not what type of response this will be, but rather the data needed to inform the development of this response. Specifically, how can a 360-degree understanding of teams be used to enhance the productivity of workers and the enterprise in a global shift towards knowledge-based work? Building this rounded understanding of today’s workforce should be steeped in data across employee interactions, be that communication tools or physical movement around an office.
Perhaps best evidencing the value of employee productivity in the context of real estate is Jones Lang Lasalle’s ‘3–30–300 rule’. The rule states that on average, companies spend $3 in utilities, $30 in rent, and $300 in payroll per square foot per year — in other words, tools that enable enhanced productivity drive an infinitely higher ROI than cost saving from rent or utilities. However, a gap in data exists in understanding employee productivity as despite dozens of HR tools in-market, most if not all rely on data extracted solely from digital interfaces such as email exchanges or surveys. Almost all ignore analytics derived from the space around us or everyday office interactions to complete the overall picture to drive productivity recommendations. With billions of dollars in value at stake, all variables of the workplace should be considered in understanding efficiency and engagement, including everything from optimized lighting, to floor configuration, to desk types.
An unconventional but rather pointed example of the lift from spatial data comes in recent research on air quality in a building. In Healthy Buildings by John D. MaComber and Joseph G. Allen of Harvard Business School and Harvard’s T. H. Chan School of Public Health, research quantifies the improvements to a company’s top and bottom line with data and actionable analytics on our work environments. In this instance, the quality of air. Among many points made in the book, MaComber and Allen quantify the impact of a healthy building on the enterprise bottom line concluding that a 1% increase in clean air results in a 2% productivity boost and a 1% reduction in payroll expense associated with 2 fewer sick days per year (see below table). Putting this into perspective in a hypothetical P&L of a $6 million revenue business, that equates to a 9% lift in the bottom line via improved productivity simply by understanding and acting on data points from our work environments. You’ll notice in this example while the revenue figure is arbitrary, payroll, rent, and utilities all tie to JLL’s 3–30–300 rule. Case in point, a simple understanding of the physical environment around employees can have an outsized effect on the top and bottom line.
Another example of spatial data insights comes in Proxy’s identity management technology. Though leading with enterprise access control functionality, Proxy’s authentication software will eventually encompass everything from unlocking doors, to scheduling meetings, to loading personal Netflix profiles when nearing a TV on say, a business trip. The use cases for this type of intelligent tool with spatial data insights is boundless, particularly when contemplating workforce efficiencies.
Moreover, spatial analytics can and should also be used to boost employee engagement. This is notable in the context that a recent Gallup report cited a 20% or better boost to productivity to employees that are engaged. A full understanding of engagement, though, must go beyond digital interfaces which can often mask true meaning behind exclamation points and the smile emojis of email and Slack. To boost engagement, spatial data and analytics can provide an understanding of favorite break room coffees or popular snacks that can be used to customize notes of appreciation directly to employees or teams to boost morale or regularly communicate gratitude. Coupled with data aggregated from digital sources, spatial data provides the missing piece to the puzzle for a holistic workforce profile.
All that said, as convenient as the Toyota or Ford parallel is to today’s enterprise workplace, one can’t ignore evolving attitudes on data privacy. In Harvard Business Review’s How Companies Can Use Employee Data Responsibly, authors Ellyn Shook, Eva Sage-Gavin and Susan Cantrell discuss ways to benefit from the insights on employees without the use of their data feeling invasive. Specific examples of this include giving employees more control over exactly what their data is being used for and why. Companies can also create tangible examples of data insights being used to elevate, not penalize people. This feels all the more relevant when considering that Shook, Sage-Gavin, and Cantrell site 90% of employees feel comfortable allowing their company to collect data as long as they get something in return. All of these factors and many more should be evaluated when contemplating the spatial data derived from the employees and what it is ultimately used for.
Today a number of startups are emerging to capture, ingest, and build on the data points extracted from our working spaces, a few of which are provided below. Parsing through these, differentiators in the category will be those that create a more holistic understanding of an employee, both in the office, on business trips, and beyond, all while paying mind to consumer privacy. Additionally, as the industry continues to adopt these technologies, natural distribution channels have emerged in the form of REIT partnerships which are used to install and push these data technologies across their respective national footprints. The winners will take advantage of the virality component of this distribution channel to capture the market.
Leveraging data for insights on employees is not new. What is missing, though, is a 360-degree understanding of employees to develop transformational productivity solutions for enterprises today, as Ford and Toyota once did for a now a trillion dollar auto industry. While plenty of employee data resides in email exchanges, surveys, and objectives and goals, a digital only data source is two-dimensional and ineffective in understanding the full picture needed to create 21st century productivity solutions for 21st century knowledge workers. Thus, it is the attention to details of the employee experience that can create substantive ROIs across enterprises, but more importantly, create the insights needed for us to inform how to continue to improve and create efficiencies in our increasingly knowledge and services driven economy.
Disclaimer: Views are my own and may not reflect those of my employer.
Macomber, J.D., Allen, J.G. (2020) Healthy Buildings. Academic Trade.
Shook, Ellyn, Sage-Gavin, Eva, Cantrell, Susan. “How Companies Can Use Employee Data Responsibly.” Harvard Business Review. February 15, 2019.