| Phenomena | People analytics: between increasing efficiency and monitoring in the workplace

People analytics: between increasing efficiency and monitoring in the workplace

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The term people analytics summarises various data analysis methods in HR management. This involves collecting and systematically analysing digital data from and about employees. The aim of people analytics is to support managers in making personnel-related decisions by analysing large amounts of data[1]. These can relate, for example, to the question of which employees have the right skills to lead an upcoming project, which reasons lead to high job satisfaction or employee dismissals or which departments could be affected by staff shortages in the future.

Researchers typically differentiate between three maturity levels of people analytics[2]. Descriptive people analytics describes the present or past and is closely related to classic personnel controlling or reporting. In personnel planning, for example, this can involve the creation of employee skills profiles. Predictive people analytics, on the other hand, aims to identify correlations and create forecasts using statistical models. For example, data such as length of service, age, distance between home and workplace or employee surveys can be used to forecast potential staff turnover or determine reasons for a high probability of dismissal. In prescriptive people analytics, the most advanced form of such analyses, scenarios are modelled and actions are recommended or – depending on the legal situation in the country of use – already implemented. One example of this is the partial automation of shift planning based on data such as order situation, customer volume or employee skills. The transition from traditional HR practices to data-driven people analytics approaches is often fluid in practice.

People analytics is by no means a specific technology. Rather, different technical processes are used to answer the specific questions posed by the company. People analytics has become increasingly possible in recent years due to the growing availability of digital employee data and advances in the field of artificial intelligence, which allow for increasingly complex analyses and forecasts in the HR sector[3]. In research and practice, the phenomenon of people analytics is therefore closely linked to developments in artificial intelligence. The results of analyses are often made available to managers in the form of dashboards, which should enable them to derive targeted measures from the analyses. The aim of such measures can be, for example, to reduce staff turnover or staffing errors in order to improve the company’s performance.

While people analytics can simplify processes and improve decision-making, for example by analysing data on equal opportunities and diversity in the company in a structured way or developing customised training opportunities, ethical and data protection issues must be at the heart of its use. In contrast to the use of data analytics in areas such as finance or purchasing, the sensitivity of the data poses a particular challenge in the HR sector. Depending on which data is analysed (e.g. whether it is anonymised or aggregated) and who has access to this data, people analytics can invade employees’ privacy and lead to surveillance[4]. Although processes such as artificial intelligence in particular enable increasingly comprehensive insights and forecasts, they also pose particularly significant ethical challenges in terms of their future potential. Their complexity makes it difficult to understand forecasts, can lead to discrimination or inhibit change (e.g. if women are underrepresented in data sets that are analysed with regard to suitable leadership skills). Self-fulfilling prophecies can also arise if an analysis shows that an employee has a high probability of resigning, thus receiving less support from the manager and ultimately actually quitting. Prescriptive analyses that recommend actions raise questions about the extent to which recommended decisions are questioned by managers and employees. How the use of people analytics affects employees and organisations depends on how it is designed and put into practice[5]. Company agreements can ensure that the use of such software also benefits employees.

Comparability with analogue phenomena

Before employee data was recorded digitally, it was managed and organised in analogue form, for example by manually stamping cards for time recording or paper-based personnel files. The increasing digital collection of employee data, which can include not only master data such as age, place of birth or salary, but also behavioural data such as the use of internal systems (e.g. internal social media or training platforms) or content data (e.g. employee surveys), opens up new opportunities to answer specific HR-related questions based on this data.

In the debates surrounding people analytics, this systematic data analysis is often contrasted with the gut feeling of managers[6]. While a manager can gain an impression of the current mood in the workforce through personal discussions with employees, for example, people analytics software offers the possibility of analysing this using regular, automated pulse surveys and processing it in dashboards. A shift planner can manually create a suitable shift schedule based on existing data on required staff and discussions with employees about their preferences regarding working hours. People analytics software uses large amounts of data about past shift schedules, customer traffic or even the weather to suggest possible shift schedules. People analytics software and the underlying technology make it possible to process large amounts of data at high speed and also to integrate many different data sources. Advanced people analytics in particular, i.e. predictive or prescriptive use cases, can significantly exceed the capabilities of human managers in terms of processing relevant information.

In comparison to these analogue types of decision-making based on human experience, however, people analytics means that only quantifiable and measurable criteria are taken into account[7]. This means that important information that has not been recorded digitally can be neglected. For example, a system can record that an employee has not completed a training course on a training platform, but the reasons for this are not recorded. However, the manager may be able to assess the various professional or personal reasons for this from day-to-day collaboration. In practice, the analyses and human experience are therefore intertwined and should complement each other. This ensures that employees are not reduced to measurable metrics.

Social relevance

The use of people analytics changes processes in the day-to-day work of managers and employees. As a rule, it is managers who determine the questions and objectives of the analyses and incorporate the findings into their respective decisions. Employees, on the other hand, are the objects of such analyses, whose data is analysed with the aim of optimising management decisions. The analysis of behavioural data in particular, such as the use of internal communication systems like email or video conferencing software, can enable performance and behaviour to be monitored and is therefore subject to strict legal regulations in Germany. In addition, information asymmetries arise between management and employees if data and analyses are only available to management. In this way, existing power imbalances in companies can become entrenched[8]. The co-determination of employees and their representatives is therefore of great importance when companies use people analytics.

Due to the increasing use of people analytics, the protection of employee data also plays a role in political debates: in Germany, an independent employee data law is intended to provide legal clarity in the future[9]. However, the far-reaching social implications of people analytics go beyond direct changes for companies. It leads to a change in social norms and expectations with regard to the transparency of personnel decisions, the handling of privacy in the workplace and criteria for evaluating performance and leadership.

Sources

  1. Marler, J. H./Boudreau, J. W. (2017). An evidence-based review of HR Analytics. In: The International Journal of Human Resource Management 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699
  2. Giermindl, L. M. et al. (2021). The dark sides of people analytics: Reviewing the perils for organisations and employees. In: European Journal of Information Systems 31(3), 410–435. https://doi.org/10.1080/0960085X.2021.1927213
  3. Leonardi, P. M. (2021). COVID‐19 and the new technologies of organizing: Digital exhaust, digital footprints, and artificial intelligence in the wake of remote work. In: Journal of Management Studies 58(1), 249–253. https://doi.org/10.1111/joms.12648
  4. Tursunbayeva, A. et al. (2021). The ethics of people analytics: Risks, opportunities and recommendations. In: Personnel Review 51(3), 900–921. https://doi.org/10.1108/PR-12-2019-0680
  5. Jörden, N. M./Sage, D./Trusson, C. (2021). ‘It’s so fake’: Identity performances and cynicism within a people analytics team. In: Human Resource Management Journal 32(3), 524–539. https://doi.org/10.1111/1748-8583.12412
  6. Loscher, G. J./Bader, V. (2023). Creating accountability through HR analytics – An audit society perspective. In: Human Resource Management Review 33(4), 1–11. https://doi.org/10.1016/j.hrmr.2023.100974
  7. Greasley, K./Thomas, P. (2020). HR analytics: The onto‐epistemology and politics of metricised HRM. In: Human Resource Management Journal 30(4), 494–507. https://doi.org/10.1111/1748-8583.12283
  8. Jarrahi, M. H. et al. (2021). Algorithmic management in a work context. In: Big Data & Society 8(2), 1–14. https://doi.org/10.1177/20539517211020332
  9. Denkfabrik digitale Arbeitsgesellschaft (2024). Auf dem Weg zu einem Beschäftigtendatengesetz: Rechtsklarheit und Vertrauen für eine erfolgreiche digitale Transformation. https://www.denkfabrik-bmas.de/schwerpunkte/beschaeftigtendatenschutz/auf-dem-weg-zu-einem-beschaeftigtendatengesetz-rechtsklarheit-und-vertrauen-fuer-eine-erfolgreiche-digitale-transformation [09.08.2024].