The Future of Work: Empowering the Data-Driven Worker

The latest entry in a special project in which business and labor leaders, social scientists, technology visionaries, activists, and journalists weigh in on the most consequential changes in the workplace.
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The latest entry in a special project in which business and labor leaders, social scientists, technology visionaries, activists, and journalists weigh in on the most consequential changes in the workplace.
A simple tool for innovation. (Photo by Peter Macdiarmid/Getty Images)

A simple tool for innovation. (Photo by Peter Macdiarmid/Getty Images)

Growth in the technology sector has popularized a dangerously narrow conception of innovation. For a richer view of innovation look beyond the glistening headquarters of technology companies to dusty construction sites.

Gina Neff is an associate professor of communication and a senior data science fellow at the eScience Institute at the University of Washington. She is an advisor to Data & Society and the author of Venture Labor: Work and the Burden of Risk in Innovative Industries.


In a trailer that served as the office for one construction site, the foreman held up a powerful tool for saving his company money. I was there to study new computer visualization tools that were supposed to make construction more efficient. Screens and Smart Boards covered the trailer’s walls just as printed drawings would have a decade before. Hundreds of thousands of dollars had been invested in making the transition to the new system, Building Information Modeling, a set of databases and 3-D design tools to help designers in building architecture, engineering, and construction coordinate their work.

The foreman (I’ll call him Sam) had a simpler innovation—a dry erase foam board on which he had printed the plans for the project. No bigger than a child’s poster for a science fair, this tool was everything the detailed and complex three-dimensional computer visualizations and databases were not. It was lightweight, flexible, and mobile. The building plans on Sam’s whiteboard were just basic sketches, and they lacked the rich, up-to-date details of the databases used by the construction managers inside the trailer that served as the site’s field office.

But the whiteboard did something for innovation that the databases could not. Sam used it to talk with construction workers, who didn’t have access to computers at their worksites. Workers wrote on the whiteboard to brainstorm ideas and sketched out problems they saw, like a challenge the team faced in coordinating their construction tasks. They used the whiteboard to point to specific places in the building, drawing arrows and circles as they talked about construction problems. Then, when the next problem arose, they erased it and used it again.

Sam’s whiteboard was an everyday innovation, a creative idea from workers applied to the contexts in which they work. It was an innovation that invited good ideas from other workers in unexpected places. The problem is that a culture obsessed with data and technology is now ignoring the key source of everyday innovation—workers.

In the popular imagination, innovation means new computing technology. We now have an unprecedented ability to bring new types of data to the smallest of workplace decisions. Increases in computing power enable big data from the Internet of Things and powerful industry-specific software like the kind I study. Some warn that these technologies will lead to a new kind of Taylorism, where employers monitor every keystroke in hopes of ever greater productivity. It threatens to replace workers, dumb down their jobs, and link old forms of discrimination to new kinds of data. These are serious concerns that must be addressed for the future of good jobs. But they miss the point for growth.

Silicon Valley, often seen as the bastion of innovation, can actually stifle it by crowding out our understanding of where innovation comes from and creating tools that ignore the expertise of front-line workers and their potential for innovation. In our research, we found that the building models helped construction planners better see potential “clashes” among the electrical, plumbing, and mechanical systems, but when they tried to design solutions they too often faced a long internal process of approvals. It was as if every good idea generated only served as a reminder of how little power the team had to innovate. In another industry, the story was the same. The nurses we studied mentally adjusted the algorithmically parsed data about their patients with what they knew about their lives and situations. However, they had to track some of this data outside an electronic system too rigid for the nurses to record some of what they learned every day from their patients. Doctors complained about one of the most commonly used of these systems, saying that it was nearly impossible to extract and analyze data about their own clinic and their own patients to improve their clinical practices.

Too often, those in the technology sector who push companies to adopt data-driven decision-making equate data with solutions, ignoring the gap between the data and implementation, which depends on “real world” practices of workers. Identifying problems and generating solutions is less of a problem for most companies than figuring out how to put those solutions into action. For Sam’s company, a global construction firm, the expensive digital visualization system solved one problem for better design, but it was less useful for translating those decisions to the construction teams and integrating the team’s knowledge about the building process back into the design. The workers in the field had practical knowledge, but their managers in the trailer had the data. Workers were frustrated because leaders ignored many of their everyday innovations for using these types of technologies.

This is the case across many industries. Unfortunately, the design of these systems does not start with a vision of empowering workers to innovate with data, but of “disrupting” (the tech industry’s mantra) how they work. Silicon Valley firms now consider data a new form of capital, one that can be against, or instead of, workers. Silicon Valley firms argue that companies with data, all things being equal, should grow faster than others. Uber and AirBnB are the darlings of Silicon Valley and venture capitalists because they understand this.

Silicon Valley has highjacked the image of innovation as tech-driven (it sometimes is) and entrepreneur-driven (it sometimes is). But if we want economic growth, we should expand where we look for good ideas. Silicon Valley should learn how to design technologies that help workers make everyday innovations.


For the Future of Work, a special project from the Center for Advanced Study in the Behavioral Sciences at Stanford University, business and labor leaders, social scientists, technology visionaries, activists, and journalists weigh in on the most consequential changes in the workplace, and what anxieties and possibilities they might produce.