Technological advances and people analytics trends

 

Artificial intelligence, machine learning, natural language processing, and others are people analytics buzzwords used to automate processes. Recent advancements include real-time data collection using mobile apps and wearables, as well as agile methods to make human resource teams more responsive and flexible.

Source; https://www.youtube.com/watch?v=Z0PHY4pvsus

Artificial intelligence

Artificial intelligence automates and improves data collection, analysis, and decision-making in organizations. The study of massive amounts of data by artificial intelligence can reveal hidden patterns and correlations that human researchers may miss. Zippia reports that only 6% of companies employ AI in recruitment. By 2023, 24% of organizations anticipate using AI heavily in internal recruitment (Messeri and Crockett, 2024). A chatbot driven by artificial intelligence can answer HR questions and deliver performance reviews to employees individually. 

“Artificial intelligence is not a substitute for human intelligence; it is a tool to amplify human creativity and ingenuity.”

Machine Learning (ML)

Machine learning algorithms and models allow machines to learn and improve from data. Machine learning models are used in people analytics to predict which employees will leave, which candidates will succeed in a specific role, and which training programs will improve employee performance. Machine learning in human resources can enhance retention by identifying the most needed areas (Bell, 2022). To be more specific, attrition models can help you determine which employees are most likely to leave, why, and how to prevent it. With this knowledge, you can focus on retaining employees who need it most. You can meet their needs by increasing their involvement and commitment to the organization.

Analytical Forecasting

Predictive analytics helps firms identify patterns and trends in employee experiences and utilize this knowledge to make better hiring, training, and retention decisions. Predictive analytics may become more essential in people analytics. As businesses collect more data on their employees and operations, they will need more advanced tools to make sense of it and use it to achieve their goals (Bunn and Wright, 1991). Organizations can predict future high achievers by assessing employee performance, tenure, education and experience. Predictive people analytics helps identify top performers. 

Cloud storage

Cloud computing in People Analytics stores and processes massive and complicated data and enables access to analytics tools and apps from anywhere with an internet connection. Recruitment, onboarding, performance management, and benefits administration employ cloud-based HR software (Abu-Libdeh, Princehouse and Weatherspoon, 2010). This simplifies HR and boosts employee happiness. However, organizations adopting cloud computing services must comply with legislation and secure and protect data.

NLP

To better understand employee engagement and mood, NLP is analyzing unstructured data, including employee comments, social media posts, and survey replies. Understand how employees feel about their jobs. NLP algorithms can find themes and patterns in huge text data to improve HR policies and practices. NLP could discover recurring patterns in employee feedback, such as workload or communication difficulties, and devise interventions to address them (Cambria and White, 2014). NLP can determine which talents and experiences are most wanted by analyzing job descriptions and resumes. NLP can amplify data biases and be erroneous.

Human Resources Data Analysis Trends

Human resources (HR) has moved from administrative to strategic. Data analytics has completely transformed human resources (HR), so HR professionals and business executives who want to stay competitive and grow their organizations must stay current on  HR data analytics trends (III and Boudreau, 2015).  

 Focus on Employee Experience

The word "employee experience" refers to everything an employee learns, contributes, sees, and evaluates, from applying to leaving the company. Assessment and improvement of employee experience are becoming more important in human resource data analysis. Using several data sources and analysis tools, the employee lifecycle is understood. Measure employee experience by conducting surveys and assessing the findings. These surveys can address workplace satisfaction, work-life balance, career advancement, and manager effectiveness. One can analyze the results to identify strengths and weaknesses and create targeted initiatives to remedy them. Companies are also using employee journey mapping to better understand the employee experience from their perspective (Malik et al., 2022). This involves mapping out all of an employee's touchpoints with the company, from onboarding to offboarding and then analyzing each touchpoint to identify strengths and weaknesses. 

Human Resource Information Systems and People Analytics Integration

HRISs and TMSs can give demographic data, employment history, and training and development records. People Analytics tools can help businesses integrate this data and identify patterns and trends that may not be apparent from a single data source. It includes predictive analytics models and machine learning techniques (Tursunbayeva, Di Lauro and Pagliari, 2018). 

Greater demand for data literacy in HR

The firm values data because it can improve operations, processes, and customer experiences. People with data capabilities are needed. Professionals must analyze data to apply people analytics. Data trends, patterns, and outliers must be identified to target HR strategies and interventions. This knowledge should aid strategy (Benzing, Adams and Boehme, 2022).

Data literacy comprises thorough source and accuracy checks. Learning data literacy entails knowing constraints (Benzing, Adams and Boehme, 2022). HR workers must consider data source biases while assessing and making choices.

Privacy and ethics are highlighted in people analytics. Companies employing data to improve HR management create ethical and privacy concerns. Ethics in people analytics is data fairness. Data must be legally obtained and used without discrimination against workers and applicants (Benzing, Adams and Boehme, 2022). Companies must also ensure data is accurate, secure, and handled properly.

Privacy risks originate from HR data. The personal data of employees and applicants is safe. Organizations must collect, use, and protect data with consent. Companies must allow workers to view and edit their data (Tursunbayeva, Di Lauro and Pagliari, 2018)

AI is a mirror, reflecting not only our intellect but our values and fears 

References

•      III, E.E.L. and Boudreau, J.W. (2015). Global Trends in Human Resource Management: A Twenty-Year Analysis. [online] Google Books. Stanford University Press. Available at: https://books.google.lk/books?hl=en&lr=&id=k7S7CAAAQBAJ&oi=fnd&pg=PR5&dq=Human+Resources+Data+Analysis+Trends+&ots=hPRHkIOhOB&sig=u_fjISIFl0QgFGZTXhVkCZhSNGE&redir_esc=y#v=onepage&q=Human%20Resources%20Data%20Analysis%20Trends&f=false [Accessed 28 Mar. 2024].

•      Abu-Libdeh, H., Princehouse, L. and Weatherspoon, H. (2010). RACS. Proceedings of the 1st ACM symposium on Cloud computing - SoCC ’10. doi:https://doi.org/10.1145/1807128.1807165.

•      Bell, J. (2022). What Is Machine Learning? Machine Learning and the City, pp.207–216. doi:https://doi.org/10.1002/9781119815075.ch18.

•      Benzing, M., Adams, K. and Boehme, G. (2022). Data Literacy on Demand: Creating a Set of Data Literacy Modules for Remote Instruction. [online] sc.lib.miamioh.edu. Available at: https://sc.lib.miamioh.edu/handle/2374.MIA/6797 [Accessed 28 Mar. 2024].

•      Bunn, D. and Wright, G. (1991). Interaction of Judgemental and Statistical Forecasting Methods: Issues & Analysis. Management Science, 37(5), pp.501–518. doi:https://doi.org/10.1287/mnsc.37.5.501.

•      Cambria, E. and White, B. (2014). Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]. IEEE Computational Intelligence Magazine, [online] 9(2), pp.48–57. doi:https://doi.org/10.1109/mci.2014.2307227.

•      Malik, A., Budhwar, P., Mohan, H. and N. R., S. (2022). Employee experience –the missing link for engaging employees: Insights from an MNE’s AI‐based HR ecosystem. Human Resource Management, 62(1), pp.97–115. doi:https://doi.org/10.1002/hrm.22133.

•      Messeri, L. and Crockett, M.J. (2024). Artificial intelligence and illusions of understanding in scientific research. Nature, [online] 627(8002), pp.49–58. doi:https://doi.org/10.1038/s41586-024-07146-0.

•      Tursunbayeva, A., Di Lauro, S. and Pagliari, C. (2018). People analytics—A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, [online] 43, pp.224–247. doi:https://doi.org/10.1016/j.ijinfomgt.2018.08.002.