HR analytics involves utilising information technologies for both simple statistical reporting (such as calculating annual employee turnover rates) and complex predictive modelling (such as identifying which employees may exit the organisation within a year).
This evidence-based approach to HR enables a more in-depth understanding of the organisation, improves objectivity in decision-making, and enhances competitive advantage and business success.
The benefits of HR analytics may explain why it has gained such popularity among both HR scholars and practitioners, and why companies’ HR data is increasingly being used to inform strategic decision-making. Unfortunately, the fanfare surrounding HR analytics distracts from its problematic aspects.
To perform HR analytics, companies need to collect, store and analyse extensive and detailed employee data, some of which can be very sensitive information.
One HR function that deals with sensitive and highly personal information is diversity management. In 2017, more than 50% of British companies reported using HR analytics for diversity reporting (CIPD, 2017). This involves collecting large volumes of highly sensitive data such as employees’ gender, sexual orientation, ethnicity, age etc.
Improper handling and disclosure of this sensitive data may end up harming employees’ physical and psychological wellbeing, for example by indirectly facilitating the persecution of, and discrimination against, minorities in countries with deep-rooted socio-political tensions.
Even in more egalitarian societies, leaked data can cause workplace conflict, trigger subconscious biases and impair the career and wellbeing of minority employees in particular.
These under-considered ethical issues explain why some employees hesitate to disclose sensitive data or provide inaccurate information, given fears around data security; not understanding the strategic objective underlying data analysis, or suffering negative repercussions.
Inaccurate, or so-called ‘spotty’ data is problematic because it does not provide a representative picture of the business and subsequent decisions, based on low-quality data, may hinder business success.
Therefore, organisational benefits accruing from HR analytics can only be realised through a transparent, democratic and ethical approach to data collection. Yet these lofty ideals are not so easily achieved.
Transparency involves making employees aware of what data is collected about them, how it is going to be used and who will have access to it. An ethical approach to data also implies respecting privacy, anonymity, and consent so no employee feels forced to disclose personal data.
Providing employees with detailed information surrounding HR analytics is time-intensive and often not feasible, given rapid changes in data processes and technological and legislative developments. Also, voluntary data disclosure may further limit the representativeness of HR analytics.
To ensure confidentiality, employee data should only be accessed by a few organisational stakeholders, principally HR/line managers.
However, this can also be problematic since managers’ biases may impact their use of the data which can harm employees’ wellbeing and careers. Yet not giving managers data access undercuts the utility and application of the contextually rich HR reports generated through data analytics.
Data also needs to be granular enough to allow managers to home in on specific business issues and make data-driven decisions. However, this magnification capacity may compromise anonymity and inadvertently identify individual employees (especially those with unique diversity markers).
Whatever the approach taken by businesses, every single aspect of the data analysis process involves ethical dilemmas and trade-offs that are rarely referenced in the highly positive narrative surrounding HR analytics. In reality, even the most ethical of companies are also driven by money and market pressures. Against this backdrop HR managers may become the vital gatekeepers who prevent employees from becoming faceless data.
Tabea Kohrs, Sara Chaudhry & Maryam Aldossari are researchers at the University of Edinburgh Business School.
