Data collection in the workplace has become ubiquitous. Employers use a growing number of information systems to plan, organize and manage workflows and work performed by their employees, most prominently systems for enterprise resource planning (ERP) and customer relationship management (CRM), which are now used by mid- to large-size organizations in most industries. Many systems constantly store digital records about work activities and behaviors of employees. This data is increasingly stored in centralized databases and in the cloud. Employers exploit the data to support managerial decisions, organize work, automate workflows and monitor workers. The technical systems in place are often complex and opaque. Most workers will not be aware of the data flows and decisions that occur in the background while they routinely interact with networked software and devices at work.
Cracked Labs, together with UNI Europa and other partners, published a case study that explores, examines and documents software systems and technologies used by employers that utilize extensive personal data about the work activities and behaviors of employees to streamline, reorganize and manage work, expand control over workers, subject them to digital monitoring and make automated decisions about them – with a focus on Europe. To illustrate wider practices, it investigates cloud-based software for enterprise data analytics, workflow automation and algorithmic management provided by the German vendor Celonis, based on a detailed analysis of software documentation and other corporate sources.
Celonis is considered the global market leader in software for process mining, which utilizes activity log data recorded by enterprise systems from vendors like SAP, Oracle, Salesforce and Microsoft to create a digital representation of how work is actually being performed in an organization, down to granular steps and tasks. Process mining aims to analyze, standardize and optimize workflows in order to make them more productive and efficient while lowering costs. Still considered a “startup”, Celonis has a significant customer base in Europe and the US. It received more than a billion in venture capital and was listed among the five largest private investments in “AI” technology globally in 2022. Celonis also provides software for workflow automation and task management.
The case study documents a wide range of data practices, which can affect workers in many fields, from insurance claim handling to manufacturing, from creative work to warehouse picking, from low-wage to knowledge work. Based on large amounts of log data about work activities, Celonis evaluates, assesses and monitors workflows in many industries in order to optimize them in line with the employers’ business goals. Metrics about productivity, time, quality, automation and cost are ubiquitous. Several mechanisms help to automate the reorganization and management of work. In addition, Celonis’ technology can be used to monitor, rate and rank workers at the individual level The company’s workflow automation technology automatically prioritizes, distributes and assigns tasks to workers.
The last section of the case study summarizes the identified data practices and discusses potential implications for workers. While granular performance monitoring at the individual level is clearly problematic, extracting aggregate knowledge from personal data increases the power imbalance at work and can also have significant effects. Utilizing the data to standardize and unilaterally reorganize workflows can accelerate and intensify work, reduce discretion, make workers easier to replace, facilitate outsourcing, undermine bargaining power and affect wages. Automated task assignment and algorithmic management practices can also have a variety of side effects. The rapid expansion of data flows and functionality potentially undermines purpose limitation, a cornerstone of European data protection law.