5.9.4 (HL)—Data and employees

Employee data can support scheduling, training, safety and performance decisions, but monitoring affects autonomy, fairness and trust.

Metrics may be incomplete or biased; workers need clear purpose, access and routes to challenge errors.

Check whether the measure reflects the job and whether the use is proportionate and explained.

A warehouse uses workload data to plan breaks, not to rank workers without context about equipment faults.

A precise metric can still be an unfair measure.

Digital Taylorism uses digital systems to divide, standardize, measure and closely monitor work, extending scientific-management ideas through real-time data, algorithms or automated targets. It may identify bottlenecks, support scheduling, improve consistency and raise productivity, but narrow metrics can ignore quality and context, intensify work, reduce autonomy, create stress and weaken trust. Employee data should have a clear purpose, proportionate collection, transparent criteria, human review and a way to correct errors or challenge decisions; productivity gains must be weighed against motivation, retention, ethics and long-term performance.