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PIA blog first wrote about the normalization of workplace surveillance six years ago. A new report from Cracked Labs called Monitoring, Streamlining and Reorganizing Work with Digital Technology shows how privacy at work is even more at risk today.

Basic workplace surveillance has now morphed into something called process mining. Wikipedia defines process mining as a family of techniques relating the fields of data science and process management to support the analysis of operational processes based on event logs. The goal of process mining is to turn event data into insights and actions. Typically, event data refers to what employees do within a business; the insights and actions are designed to allow management to see who is doing what and when theyre doing it, and how to optimize activities and processes to increase efficiency and profits.

An earlier article in Analytics Insight lists what it sees as the top five processing mining companies. Number one is Celonis, an important software company that most people have probably never heard of. Headquartered in Germany, its active worldwide, has a valuation of $13 billion, and employs over 3,000 people. It has more than 5,000 enterprise customer deployments, including many well-known companies in key industries. The software is particularly popular among management consultants: according to Celonis, over 2,000 consulting firms use it for their client projects.

The new Cracked Labs report concentrates on Celonis as its the market leader, its representative of the whole class of emerging process mining companies. The reports summary gives a good idea of how Celonis software carries out process mining using the following data practices:

Weve previously discussed some workplace surveillance practices things like monitoring everything an employee does on their computer, even down to what they click on with the mouse (Celonis calls this task mining). Process mining has moved beyond piecemeal surveillance of employees to bring all this data together for higher-level analysis. A key element is workflow analysis and automation. By aggregating data about individuals, the software is able to evaluate and assess workflows, and their duration and outcomes. It can calculate performance metrics for teams, departments, and other groups. The new report notes a significant recent development in this area:

In 2020, Celonis started to refer to its software as an execution management system (EMS), putting the focus on managing and changing operational processes rather than merely analyzing them. Employers and third parties can create Celonis-based execution applications, which combine process analysis and optimization with functionality for workflow automation and task management. Similar to other cloud-based enterprise software systems, Celonis has turned into a platform. Third-party vendors can offer Celonis-based applications via the companys app, marketplace. This includes, for example, applications to manage or supervise work in manufacturing, warehouses, helpdesk services and call centers.

That is, beyond merely carrying out surveillance on staff, the data collected is used to reconfigure work patterns and optimize them according to various metrics. The personal data collected is also made available to a new class of platform-based applications that can carry out even deeper analysis and produce further managerial options.

The routine and expanding use of personal data in this way is clearly problematic from a privacy viewpoint. But this shift to automating workflows with software brings with it another problem, described by the new report as follows:

It potentially divorces work from the reasons for doing a task, which remain opaque and shifts authority from humans to the information system. Especially when combined with a limited set of possible actions, this restricts autonomy and discretion at work. It may result in a variety of side effects, such as employees experiencing the algorithmic system as arbitrary rule, employees prioritizing their efforts to match the systems expectations, the invisibility of work activities that lack an accurate digital representation in the system or even complete dysfunctionality. Not least, algorithmic management, especially when based on statistical inferences, entails the risk of discrimination against already disadvantaged employees.

Summarizing its findings, the report concludes:

While employers can certainly use these technologies in ways that are beneficial for everyone, the findings in this case study suggest that organizations may utilize them to unilaterally streamline and reorganize work according to their business goals while making workers subject to disproportional digital monitoring and control. The unscrupulous exploitation of worker data at scale increases the power imbalance between employers and workers and normalizes extensive surveillance in the workplace. While these technologies could, in theory, also be used to optimize work towards goals like better working conditions and increased employee wellbeing, Celonis almost exclusively emphasizes optimization towards the most ordinary and aggressive business objectives like increased efficiency and lowered costs.

This indicates how the loss of privacy in this case in the workplace is not some abstract issue. Thanks to the increasing use of process mining in companies, surveillance in the workplace can lead to an erosion of fundamental rights. Most troubling, perhaps, is that it can dehumanize work itself, effectively turning employees into little more than cogs in a machine run not by human managers, but by sophisticated and inscrutable software.

Featured image by Celonis.

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Process Mining: The Next Stage in Workplace Surveillance, and a ... - Privacy News Online