Data collection is a pervasive and mundane part of many workplaces. While far from a new phenom- enon, digital technologies have enabled unprecedented expansion in the volume and kinds of data collected about workers. Today, employers amass data from technologies such as wearables, mobile apps, computer software, and other surveillance tools but also through simple, low-tech methods such as self-reporting information on a paper form.1 The ability to analyze large datasets enables employers to make broad and at times speculative inferences about workers’ productivity, their future behavior, their interactions with other people and machines, and more.

Worker data spans many types of information, methods of collection, and institutions. An HR depart- ment may collect data about workers’ characteristics and employment history (often called people an- alytics) to make decisions about hiring, promotion, and firing. Employers quantify workers’ produc- tivity through tools like motion sensors, facial recognition, GPS-tracking, or keystroke monitoring to enforce efficiency benchmarks and automate aspects of management.2 Health insurers, financial insti- tutions, data brokers, and other entities collect myriad data about workers, blurring the lines between worker surveillance and more general consumer tracking and data profiling. Government agencies collect and use data to enforce regulations, shape policy, and analyze workforce trends, while unions and worker organizations may survey members or collect data to support and inform advocacy.