ResearchPDF Available

Leveraging HR Analytics for Data-Driven Decision Making: A Comprehensive Review

July 2023

DOI:10.13140/RG.2.2.16977.30562

Authors:

Dr Malla Jogarao

Indian Maritime University

Dr S T Naidu

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

Human Resource (HR) Analytics has emerged as a critical tool for organizations to effectively manage their workforce and make informed decisions. This research paper aims to provide a comprehensive review of HR analytics, highlighting its importance, methodologies, benefits, and challenges. The paper explores the various applications of HR analytics in areas such as recruitment and selection, performance management, employee engagement, talent development, and retention. Additionally, it examines the key data sources, metrics, and analytical techniques used in HR analytics, including descriptive, predictive, and prescriptive analytics. Furthermore, the paper discusses the potential impact of HR analytics on organizational performance, employee satisfaction, and overall business outcomes. Lastly, it addresses the ethical considerations and privacy concerns associated with HR analytics implementation. The findings of this research paper emphasize the significance of HR analytics in enabling evidence-based decision making and fostering a data-driven HR function within organizations.

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Leveraging HR Analytics for Data-Driven Decision Making: A Comprehensive Review

Dr.Malla Jogarao, Assistant Professor, Indian Maritime University, Visakhapatnam

Dr.T. Hemalatha, Assistant Professor, AIMS, Visakhapatnam

Dr.S.T.Naidu, Associate Professor of Law, Nobel Institute of Management Science, Visakhapatnam

Abstract:

Human Resource (HR) Analytics has emerged as a critical tool for organizations to effectively

manage their workforce and make informed decisions. This research paper aims to provide a

comprehensive review of HR analytics, highlighting its importance, methodologies, benefits, and

challenges. The paper explores the various applications of HR analytics in areas such as

recruitment and selection, performance management, employee engagement, talent development,

and retention. Additionally, it examines the key data sources, metrics, and analytical techniques

used in HR analytics, including descriptive, predictive, and prescriptive analytics. Furthermore,

the paper discusses the potential impact of HR analytics on organizational performance,

employee satisfaction, and overall business outcomes. Lastly, it addresses the ethical

considerations and privacy concerns associated with HR analytics implementation. The findings

of this research paper emphasize the significance of HR analytics in enabling evidence-based

decision making and fostering a data-driven HR function within organizations.

Key Words: HR Analytics, Employee recruitment and selection, Performance, retention,

Introduction

Human Resource (HR) analytics has gained significant attention in recent years as organizations

recognize the potential of data-driven decision making in managing their workforce effectively

(Bondarouk & Ruel, 2019). HR analytics refers to the systematic collection, analysis, and

interpretation of HR data to gain insights and make informed decisions that drive organizational

performance and employee engagement (Cascio, 2018).

The significance of HR analytics lies in its ability to transform HR from a primarily

administrative function to a strategic partner within organizations (Rasmussen & Ulrich, 2015).

By leveraging advanced analytics techniques, HR professionals can extract valuable insights

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from vast amounts of HR-related data, enabling evidence-based decision making across various

HR domains (Marr, 2018).

The scope of HR analytics is broad and encompasses multiple areas within the HR function. It

includes analyzing data related to recruitment and selection processes, performance management,

employee engagement and satisfaction, talent development and succession planning, as well as

retention and attrition patterns (Rasmussen, Ulrich, & Younger, 2017). HR analytics enables

organizations to identify trends, predict outcomes, and prescribe actionable strategies to optimize

HR practices and improve overall business outcomes (Sharma & Dhar, 2020).

Objectives of the Study:

1. To provide a comprehensive review of HR analytics, covering its methodologies,

2. To explore the various applications of HR analytics across different HR domains.

3. To explore the various benefits, and challenges

Methodology:

The research paper adopts a systematic literature review approach to provide a comprehensive

review of HR analytics. The systematic review method involves a rigorous and structured

process of identifying, selecting, and analyzing relevant literature in a systematic manner (Ravi

& Bali, 2019).This research paper's review of pertinent literature and studies is an important

component. It entails a thorough examination and synthesis of previous scholarly works on HR

analytics, including journal articles, books, conference papers, and industry reports.

The literature study covers a number of topics, including HR analytics approaches, applications,

advantages, difficulties, and future developments. It offers a framework for comprehending the

status of the field's knowledge at the moment and points up opportunities for future research as

well as research gaps.

Applications of HR Analytics

HR analytics has various applications across different domains within the field of human

resources. Here are the applications of HR analytics:

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Recruitment and selection analytics:

Recruitment and selection analytics involve using data and analytics to improve the effectiveness

and efficiency of the hiring process. It helps in identifying the most effective recruitment

channels, assessing candidate fit, predicting job performance, and reducing biases in the

selection process (Bondarouk & Ruel, 2019).

Performance management analytics:

Performance management analytics involves analyzing data to measure and improve employee

performance. It helps in setting meaningful performance goals, providing feedback and coaching,

identifying training and development needs, and identifying high-performing individuals or

teams (Aguinis & Lawal, 2018).

Employee engagement and satisfaction analytics:

Employee engagement and satisfaction analytics focus on measuring and analyzing factors that

impact employee engagement and satisfaction. It helps in identifying drivers of engagement,

assessing the effectiveness of engagement initiatives, understanding employee sentiment, and

predicting turnover risks (Rasmussen, Ulrich, & Younger, 2017).

Talent development and succession planning analytics:

Talent development and succession planning analytics involve using data to identify and develop

high-potential employees for future leadership roles. It helps in identifying critical skills gaps,

assessing leadership potential, creating personalized development plans, and ensuring a robust

pipeline of talent (Laumer, Eckhardt, & Weitzel, 2017).

Retention and attrition analytics:

Retention and attrition analytics focus on understanding and predicting employee turnover. It

helps in identifying flight risks, analyzing reasons for attrition, developing retention strategies,

and improving employee retention rates (Ravi & Bali, 2019).

These applications of HR analytics demonstrate its potential to optimize HR processes, enhance

decision making, and improve overall organizational performance. By leveraging data and

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analytics, organizations can gain valuable insights to drive strategic HR initiatives and better

manage their human capital.

Data Sources and Metrics

Data sources and metrics play a crucial role in HR analytics, enabling organizations to gather and

analyze relevant information to make informed decisions. Here are the commonly used data

sources and key HR metrics for analytics:

Internal Data Sources:

a) Human Resource Information System (HRIS): HRIS serves as a comprehensive database

that contains employee-related data, including personal information, employment history,

training records, performance evaluations, and compensation details

b) Performance Evaluations: Performance evaluations provide valuable data on individual and

team performance, including ratings, feedback, and goal achievements. This data helps assess

performance trends and identify areas for improvement

c) Surveys: Employee surveys, such as engagement surveys, satisfaction surveys, or pulse

surveys, gather feedback and opinions from employees regarding their experiences, perceptions,

and engagement levels within the organization. Survey data provides insights into employee

attitudes and can help identify areas of improvement (Bondarouk & Ruel, 2019).

External Data Sources:

a) Social Media: Social media platforms can be a valuable source of data for HR analytics. By

monitoring social media platforms, organizations can gain insights into employer branding,

employee sentiment, and emerging trends related to talent management (Sharma & Dhar, 2020).

b) Market Trends: Analyzing market trends, industry reports, and economic indicators can

provide valuable external insights. This data helps organizations understand external factors that

may impact talent acquisition, employee retention, and overall HR strategies (Laumer, Eckhardt,

& Weitzel, 2017).

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Key HR Metrics for Analytics:

a) Turnover Rate: The turnover rate measures the percentage of employees who leave the

organization within a specific period. It helps assess the effectiveness of retention strategies and

identify areas of concern that may require intervention (Rasmussen, Ulrich, & Younger, 2017).

b) Time to Hire: Time to hire represents the duration between initiating the recruitment process

and successfully filling a vacant position. It helps evaluate the efficiency of the recruitment

process and identifies bottlenecks that may influence talent acquisition (Rasmussen, Ulrich, &

Younger, 2017).

c) Engagement Scores: Engagement scores measure the level of employee engagement within

the organization. This metric provides insights into employee satisfaction, commitment, and

motivation, enabling organizations to take targeted actions to enhance engagement (Rasmussen,

Ulrich, & Younger, 2017).

These data sources and metrics contribute to the analytical capabilities of HR professionals,

enabling evidence-based decision-making and strategic HR management. By leveraging internal

and external data and focusing on key HR metrics, organizations can gain valuable insights to

optimize HR processes and improve overall organizational performance.

Benefits and Impact of HR Analytics

HR analytics has the potential to bring significant benefits and make a substantial impact on

organizations. By leveraging data-driven insights, HR professionals can make informed

decisions, enhance HR practices, and drive organizational success. Here are some key benefits

and impacts of HR analytics:

Improved Decision-Making: HR analytics enables evidence-based decision-making by

providing insights into various HR processes and practices. IoT( Internet of things) significantly

impacts the Decision Making (Thirupurasundari et al., 2021).Data-driven decisions help HR

professionals address workforce challenges, optimize resource allocation, and align HR

strategies with organizational goals (Aguinis & Lawal, 2018).

Enhanced Talent Acquisition: HR analytics can improve the effectiveness of talent acquisition

processes. By analyzing recruitment data, organizations can identify the most successful

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sourcing channels, evaluate candidate quality, reduce time to hire, and make targeted

improvements to attract and select the best-fit candidates (Boudreau & Cascio, 2017).

Increased Employee Engagement and Satisfaction: HR analytics can help measure and

understand employee engagement and satisfaction levels. By analyzing employee survey data

and other relevant metrics, organizations can identify factors that impact engagement, design

targeted interventions, and create a positive work environment (Sharma & Dhar, 2020).

Talent Development and Succession Planning: HR analytics assists in identifying high-

potential employees and creating effective talent development and succession plans. By

analyzing performance and competency data, organizations can identify future leaders, provide

targeted development opportunities, and ensure a pipeline of skilled employees for critical roles

(Cascio, 2018).

Reduced Turnover and Retention: HR analytics helps in identifying factors contributing to

turnover and developing effective retention strategies. By analyzing turnover rates, exit

interviews, and engagement scores, organizations can identify underlying causes of attrition,

address employee concerns, and implement targeted retention initiatives (Bondarouk & Ruël,

2019).

Cost Savings and Efficiency: HR analytics can lead to cost savings by optimizing HR processes

and resource allocation. For example, by analyzing workforce data, organizations can identify

areas of inefficiency, optimize staffing levels, and reduce unnecessary expenses (Boudreau &

Cascio, 2017).

Strategic Alignment: HR analytics enables HR professionals to align HR strategies with

organizational goals. By analyzing data on key HR metrics, organizations can assess the impact

of HR practices on business outcomes, align HR initiatives with strategic objectives, and

demonstrate the value of HR to organizational success (Aguinis & Lawal, 2018).

These benefits and impacts highlight the transformative potential of HR analytics in enabling

data-driven decision-making, enhancing HR practices, and driving organizational effectiveness.

By leveraging HR analytics effectively, organizations can gain a competitive advantage in

managing their most valuable asset—their people.

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Challenges and Limitations of HR Analytics

Data Quality and Accessibility: HR analytics heavily relies on the availability of accurate and

reliable data. However, organizations may face challenges related to data quality, completeness,

and accessibility. Data discrepancies, inconsistencies, and inadequate integration between

systems can hinder the accuracy and reliability of HR analytics outcomes (Bersin, 2018).

Privacy and Data Security: HR analytics often deals with sensitive employee data.

Organizations need to ensure that appropriate measures are in place to protect employee privacy

and maintain data security. Compliance with data protection regulations and maintaining ethical

data handling practices is crucial to avoid potential legal and ethical issues (Lawler & Boudreau,

2018).

Skill and Expertise Gap: HR analytics requires a combination of HR knowledge, statistical

analysis skills, and data interpretation capabilities. Many HR professionals may lack the

necessary technical skills and expertise to effectively analyze and interpret data. Bridging the

skill gap through training and upskilling programs is essential for successful implementation of

HR analytics .

Data Integration and Systems Compatibility: HR data is often scattered across various

systems and databases, making it challenging to integrate and analyze the data holistically.

Incompatibility between different HR systems and difficulties in data integration can hinder the

ability to obtain a comprehensive view of the workforce (Bondarouk & Ruël, 2019).

Change Management and Organizational Culture: Implementing HR analytics requires a

cultural shift within the organization. Resistance to change, lack of support from senior

leadership, and a culture that is not data-driven can impede the adoption and acceptance of HR

analytics initiatives. Change management efforts and building a data-driven culture are necessary

for successful implementation (Bersin, 2018).

Interpretation and Actionability of Insights: HR analytics provides valuable insights, but their

effectiveness relies on the ability to interpret and translate those insights into actionable

strategies. Lack of clarity on how to use the analytics findings and limited understanding of how

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to apply the insights to HR practices may limit the impact of HR analytics initiatives (Lawler &

Boudreau, 2018).

Ethical Considerations: The use of HR analytics raises ethical considerations, particularly

regarding employee privacy, fairness, and potential biases in decision-making. Ensuring that HR

analytics initiatives are conducted ethically and that decisions based on analytics insights are fair

and unbiased is critical (Aguinis & Lawal, 2018).

It is important to address these challenges and limitations proactively to maximize the benefits

and mitigate potential risks associated with HR analytics. By addressing data quality, skills gaps,

systems integration, cultural barriers, and ethical considerations, organizations can overcome

these challenges and leverage HR analytics effectively.

Conclusion

HR analytics has emerged as a powerful tool for organizations to leverage data-driven insights

and make informed decisions in various HR domains. By utilizing internal and external data

sources, organizations can gain valuable insights into their workforce, enhance HR processes,

and drive organizational success. The applications of HR analytics, such as recruitment and

selection analytics, performance management analytics, employee engagement and satisfaction

analytics, talent development and succession planning analytics, and retention and attrition

analytics, provide organizations with the ability to optimize their HR strategies and practices.

However, implementing HR analytics comes with its own set of challenges and limitations.

Organizations need to address issues related to data quality, privacy, skill gaps, systems

compatibility, change management, and ethical considerations to maximize the benefits of HR

analytics. By overcoming these challenges and limitations, organizations can effectively harness

the power of HR analytics to drive evidence-based decision-making, enhance talent

management, improve employee engagement and satisfaction, and align HR strategies with

organizational goals.

As the field of HR analytics continues to evolve, organizations must adapt to new technologies,

develop analytical capabilities, and foster a data-driven culture. By doing so, organizations can

position themselves at the forefront of HR practices, gain a competitive advantage, and drive

sustainable organizational success.

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In conclusion, HR analytics has the potential to transform HR practices and enable organizations

to make data-driven decisions. By embracing the opportunities offered by HR analytics and

addressing the associated challenges, organizations can unlock the full potential of their

workforce and drive continuous improvement and innovation in their HR strategies and

practices.

References:

1. Bondarouk, T., & Ruel, H. (2019). Electronic HRM: Four decades of research on

adoption and consequences. The International Journal of Human Resource Management,

30(3), 343-379.

2. Cascio, W. F. (2018). Leveraging human resources for competitive advantage through

analytics. Human Resource Management, 57(1), 21-27.

3. Laumer, S., Eckhardt, A., & Weitzel, T. (2017). Understanding the link between big data

analytics and innovation. Journal of Business Economics, 87(8), 977-1009.

4. Marr, B. (2018). Data-driven HR: How to use analytics and metrics to drive performance.

Kogan Page Publishers.

5. Rasmussen, P., & Ulrich, D. (2015). Learning to be strategic: The challenges for HR.

Human Resource Management, 54(4), 667-675.

6. Rasmussen, P., Ulrich, D., & Younger, J. (2017). HR analytics and its impact on HRM

strategies. Employee Relations, 39(5), 686-701.

7. Ravi, V., & Bali, R. K. (2019). Big data analytics in HRM: A review and future research

agenda. International Journal of Human Resource Management, 30(5), 799-829.

8. Sharma, N., & Dhar, R. L. (2020). HR analytics: Exploring the impact of human resource

analytics on organizational performance. Journal of Organizational Change Management,

33(7), 1256-1274.

9. Shih, Y. Y., Huang, J. H., & Huang, T. H. (2021). Predictive HR analytics: A systematic

review and future research agenda. Human Resource Management Review, 100874.

10. Van den Heuvel, S., Bondarouk, T., & Kuipers, B. (2018). HR analytics adoption: Are

privacy concerns and organizational resources driving adoption barriers or adoption

intentions? International Journal of Human Resource Management, 29(13), 2119-2144.

11. Ravi, V., & Bali, R. K. (2019). Big data analytics in HRM: A review and future research

agenda. International Journal of Human Resource Management, 30(5), 799-829.

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12. Bondarouk, T., & Ruel, H. (2019). Electronic HRM: Four decades of research on

adoption and consequences. The International Journal of Human Resource Management,

30(3), 343-379.

13. Sharma, N., & Dhar, R. L. (2020). HR analytics: Exploring the impact of human resource

analytics on organizational performance. Journal of Organizational Change Management,

33(7), 1256-1274.

14. Laumer, S., Eckhardt, A., & Weitzel, T. (2017). Understanding the link between big data

analytics and innovation. Journal of Business Economics, 87(8), 977-1009.

15. Rasmussen, P., Ulrich, D., & Younger, J. (2017). HR analytics and its impact on HRM

strategies. Employee Relations, 39(5), 686-701.

16. Kanungo, D., Sahu, K., Malla Jogarao, D. K. S., Kumar, T. K., & Nagra, A. (2023). Evolution

Towards Greater Digitalization in HR Procedures. Journal of Pharmaceutical Negative Results,

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17. Aguinis, H., & Lawal, S. O. (2018). Big data analytics in human resources: A scoping

review. Journal of Management, 44(6), 2590-2620.

18. Boudreau, J. W., & Cascio, W. F. (2017). Human capital analytics: Why and how to start.

Organizational Dynamics, 46(3), 109-118.

19. Bondarouk, T., & Ruël, H. (2019). Electronic HRM: Four decades of research on

adoption and consequences. The International Journal of Human Resource Management,

30(3), 343-379.

20. Cascio, W. F. (2018). Leveraging human resources for competitive advantage through

analytics. Human Resource Management, 57(1), 21-27.

21. Thirupurasundari, D. R., Jogarao, M., Prakash, O., & Raj, K. B. (2021). Investigating the

Impact of IoT on Business Performance. Turkish Online Journal of Qualitative Inquiry,

12(6).

22. Sharma, N., & Dhar, R. L. (2020). HR analytics: Exploring the impact of human resource

analytics on organizational performance. Journal of Organizational Change Management,

33(7), 1256-1274.

23. Aguinis, H., & Lawal, S. O. (2018). Big data analytics in human resources: A scoping

review. Journal of Management, 44(6), 2590-2620.

24. Bersin, J. (2018). People analytics: Here with a vengeance. Deloitte Review, 23, 74-87.

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25. Bondarouk, T., & Ruël, H. (2019). Electronic HRM: Four decades of research on

adoption and consequences. The International Journal of Human Resource Management,

30(3), 343-379.

26. Lawler, E. E., & Boudreau, J. W. (2018). People analytics and HRM: The path forward.

People and Strategy, 41(2), 22-29.

Predicting Job Change Intentions for a Food Manufacturing Company: A Deep Learning Case Study

Article

Full-text available

Jul 2024

Rachid Ejjami

This study aims to address employee job change intention in the competitive food manufacturing industry by developing deep learning models to predict job change intentions. Using a comprehensive dataset of 32,000 employee records, including demographics, education, and work experience, the study employed a quantitative methodology with neural network analysis. Various deep learning models were implemented and evaluated using TensorFlow and Keras, with techniques like GridSearchCV, Random Search CV, SMOTE, and Keras Tuner used for hyperparameter tuning and addressing class imbalance. The findings revealed significant differences in model effectiveness, with Model 7: Complex Neural Network Architecture, featuring a complex architecture and appropriate regularization, achieving a reasonable balance across metrics and demonstrating improved recall for job changers. This suggests its suitability for predicting job change intention in a food manufacturing company. The study concludes that well-tuned deep learning models can significantly enhance predictive accuracy, offering valuable insights for HR professionals to develop targeted retention strategies. Future research should explore additional features influencing staff job change intention, validate these models across diverse organizational contexts, and integrate real-time data analytics and explainable AI techniques to improve transparency and effectiveness in HR practices.

Human capital analytics: why are we not there?

Article

Jun 2017

Purpose While human capital analytics (HCA) recently has developed enormous interest, most organizations still find themselves struggling to move from operational reporting to analytics. The purpose of this paper is to explore why that is the case and can be done to change that. Design/methodology/approach Referring to the “LAMP” model, the authors stress four elements as potential reasons why HCA are not sufficiently being “pushed” toward their audience, namely, logic, analytics, measures, and process. Similarly, they name five conditions why the wider use of HCA is not “pulled” in by the analytics user. Findings The authors investigations show that these “push” and “pull” factors behind the lack of greater use of HCA represent fertile ground for future research and implications for practitioners on both ends. Practical implications These “push” and “pull” factors behind the lack of greater use of HCA represent fertile ground for future research and implications for practitioners on both ends. Originality/value These “push” and “pull” factors behind the lack of greater use of HCA represent fertile ground for future research and implications for practitioners on both ends.

Data-driven HR: How to use analytics and metrics to drive performance

Jan 2018

B Marr

Marr, B. (2018). Data-driven HR: How to use analytics and metrics to drive performance. Kogan Page Publishers.

Learning to be strategic: The challenges for HR

Jan 2015
667-675

P Rasmussen
D Ulrich

Rasmussen, P., & Ulrich, D. (2015). Learning to be strategic: The challenges for HR. Human Resource Management, 54(4), 667-675.

Predictive HR analytics: A systematic review and future research agenda

Jan 2021
Hum Resource Manag Rev
100874

Y Y Shih
J H Huang
T H Huang

Shih, Y. Y., Huang, J. H., & Huang, T. H. (2021). Predictive HR analytics: A systematic review and future research agenda. Human Resource Management Review, 100874.

HR analytics adoption: Are privacy concerns and organizational resources driving adoption barriers or adoption intentions?

Jan 2018
INT J HUM RESOUR MAN
2119-2144

S Van Den Heuvel
T Bondarouk
B Kuipers

Van den Heuvel, S., Bondarouk, T., & Kuipers, B. (2018). HR analytics adoption: Are privacy concerns and organizational resources driving adoption barriers or adoption intentions? International Journal of Human Resource Management, 29(13), 2119-2144.

Electronic HRM: Four decades of research on adoption and consequences

Jan 2019
INT J HUM RESOUR MAN
343-379

T Bondarouk
H Ruël

Bondarouk, T., & Ruël, H. (2019). Electronic HRM: Four decades of research on adoption and consequences. The International Journal of Human Resource Management, 30(3), 343-379.

Investigating the Impact of IoT on Business Performance

Jan 2021
12

D R Thirupurasundari
M Jogarao
O Prakash
K B Raj

Thirupurasundari, D. R., Jogarao, M., Prakash, O., & Raj, K. B. (2021). Investigating the Impact of IoT on Business Performance. Turkish Online Journal of Qualitative Inquiry, 12(6).

People analytics: Here with a vengeance

Jan 2018
74-87

J Bersin

Bersin, J. (2018). People analytics: Here with a vengeance. Deloitte Review, 23, 74-87.

Leveraging HR Analytics for Data-Driven Decision Making: A Comprehensive Review

Abstract

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