How HR Analytics Important to the Organisation?

As scholars and practitioners seek to comprehend how data may be converted into useful knowledge resulting in enhanced organisational performance, there has been a growing focus on the idea and use of data and analytics in management.

What is HR Analytics

HR Analytics is "an HR practice enabled by information technology that uses descriptive, visual, and statistical analyses of data related to HR processes, human capital, organizational performance, and external economic benchmarks to establish business impact and enable data-driven decision-making” (Marler and Boudreau 2017, p.15)

Comparing more recent legacy IT systems to the human resource information systems (HRISs), cloud platforms, and apps that are now widely available, HR departments can now gather, handle, and evaluate vast amounts of employee data (Kim et al., 2021). HR departments are now using HR analytics more often as a result of this change.

The HR analytics team at Google, for instance, has created an evidence-based strategy to enhance its hiring and selection procedure by recognising multiple components of high performance which might foresee an applicant's probability of success through the application of sophisticated HR technology to collect and analyse candidate and staff information (Shrivastava et al., 2018). Levenson (2018) and Simón and Ferreiro (2018) highlight that HR analytics provides organisations with additional tools to tackle HR issues such as employee engagement, inclusion and diversity of personnel, and turnover, in along with recruiting and selection.

 

How HR Analytics Improve Organisational Performance

Managers and executives may utilise the useful information that HR analytics produces in the form of organisational facts as proof for making decisions. Similar to this, firms that use and utilise the knowledge gained from HR analytics in conjunction with other sources of information to inform their choices are likely to increase the efficacy of those choices, which in turn boosts organisational performance. Numerous case studies that have examined how HR analytics supports evidence-based decision-making to enhance HR and company performance provide as proof of this. For example;

Case Study 1 

Simón and Ferreiro (2018) recently completed a case study that details the creation and execution of an HR analytics programme at Inditex, a major global fashion retail firm based in Spain. Inditex created workforce analytics-focused key performance indicators in partnership with the authors. By doing this, Inditex HR managers were able to get and use crucial information to make better choices about their workers, which improved store performance as a whole (Simón and Ferreiro, 2018).

Case Study 2

HR analytics were used by Bank of America to enhance HR and business results in partnership with Humanyze, a vendor of HR analytics software (Kane, 2015). To facilitate the collection of worker data, Humanyze created ID badges for Bank of America employees that included microphones, Bluetooth, and infrared technologies (Kane, 2015). As to Kane's (2015) research, the most important element that predicted productivity was the way employees engaged with one another. Bank of America adopted workplace changes according to this data, which resulted in a 19% decrease in stress, a 23% rise in productivity, and an 18% rise in team collaboration (Kane, 2015).

HR Technology Enabling HR Analytics

The rapid progress of information technology has instigated a digital revolution, wherein enterprises are using big data to tackle unexplored prospects. HR is no exempt to this, as departments are moving towards having an HR division that is more technologically advanced. Therefore, a major factor in the adoption and expansion of HR analytics is the availability of HR technology, including HRISs and other digital HRM (e-HRM) technologies.

For instance, HR information may be collected, stored, manipulated, retrieved, and distributed using HRIS which also has the ability to create reports on key performance indicators (KPIs). In addition, by combining statistical applications, big data, and business intelligence, these systems may include more sophisticated analytics and reporting modules to forecast both short- and long-term labour trends.

Artificial intelligence (AI) technologies, such chatbots, have been integrated for the purpose of optimising HR operations more recently, thanks to developments in HR IT platforms. HR technology progresses along an evolutionary path from simple systems with AI and analytic skills to more sophisticated ones with basic data collection and storage (HRIS).

The widespread use of HR analytics is fueled by access to HR technology, which also serves as a prerequisite for and enabler of HR analytics since;

• HR professionals can quickly access workforce data using HR technology, which paves the way for HR analytics and helps them make more educated, data-driven choices. HR technology makes it possible to gather, purify, and work with different kinds of data from several sources, which may help with decision-making within the company.
• The process of turning workforce data into information is facilitated by HR technology, and thanks to its capacity for statistical and predictive analysis, executives, HR specialists, and line managers may use this information to make strategic personnel choices.
• The extensive flexibility of today's HR technology systems also enables HR practitioners to convert data into insightful understandings by creating scorecards, dashboards, and data visualisations.

Conclusion

Developing and fostering a decision-making culture provides several benefits for enhancing organisational performance. Similar arguments are made in favour of HR analytics' significant contribution to evidence-based management. HR analytics, for example, provides data via tools like scorecards, dashboards, and predictive analytics. These organisational knowledge sources provide a connection with evidence-based management and HR analytics, enabling human resources professionals and business associates to come up with better workforce decisions.

Reference

Kane, G. (2015), People analytics through super- charged ID, MIT Sloan Management Review, 56(4), available at: https://sloanreview.mit.edu/article/people-analytics-through-super-charged-id-badges/.

Kim, S., Wang, Y. and Boon, C. (2021), Sixty years of research on technology and human resource management: looking back and looking forward, Human Resource Management, 60(1), pp. 229-247, doi: 10.1002/hrm.22049.

Levenson, A. and Fink, A. (2017), Human capital analytics: too much data and analysis, not enough models and business insights, Journal of Organizational Effectiveness, 4(2), pp. 145-156, doi: 10.1108/JOEPP-03-2017-0029.

Marler, J.H., Boudreau, J.W. (2017), An evidence-based review of HR analytics, International Journal of Human Resource Management,Routledge, 28(1), pp. 3-26, doi: 10.1080/09585192.2016.1244699.

Shrivastava, S., Nagdev, K. and Rajesh, A.(2018), Redefining HR using people analytics : the case of Google, Human Resource Management International Digest, 26(2), pp. 2-6, doi: 10.1108/HRMID-06-2017-0112.

Simón, C. and Ferreiro, E. (2018), Workforce analytics: a case study of scholar–practitioner collaboration, Human Resource Management, 57(3), pp. 781-793, doi: 10.1002/hrm.21853.