Human Capital Analytics E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The book equips readers with essential insights and strategies for leveraging cutting-edge technology and human capital analytics, ensuring organizations thrive in the era of human-robot collaboration and sustainable workforce development.
Human Capital Analytics: Exploring the HR Spectrum in Industry 5.0 provides a comprehensive investigation into the ever-changing junction of human capital and cutting-edge technology in the context of the Fifth Industrial Revolution. This volume emphasizes the revolutionary role that human capital analytics plays in changing workforce management, talent development, and HR strategies. This position is particularly relevant as organizations transition into Industry 5.0, where human-robot collaboration is the norm. The purpose of this book is to provide a forward-looking perspective on how data-driven human resource strategies will become vital for boosting worker potential and driving organizational success. This is accomplished by integrating developing technologies such as artificial intelligence, machine learning, and robots.
Readers will find that this book:
- Explores the transformative role of human-robot collaboration, emerging technologies, and strategic HR planning in the context of the Fifth Industrial Revolution;
- Provides a comprehensive overview of how predictive analytics and human capital analytics can enhance workforce management, employee engagement, and performance measurement;
- Focuses on how HR 5.0 contributes to advancing the United Nations Sustainable Development Goals, driving both social and business impact;
- Includes empirical studies, case studies, and real-world examples of implementing Industry 5.0 in organizations;
- Provides actionable strategies for HR professionals to navigate the digital transformation of human resource management, incorporating AI, robotics, and data-driven approaches.
Audience
Human resource developers, analysts, professionals, business executives, data scientists, consultants, professors, academics, and students exploring ways to leverage technology for Industry 5.0.
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Seitenzahl: 596
Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Part 1: FOUNDATION AND STRATEGIC FRAMEWORK OF HUMAN CAPITAL ANALYTICS IN INDUSTRY 5.0
1 Human Capital Analytics in Industry 5.0: Opportunities and Challenges
Abbreviations
1.1 Introduction
1.2 Evolution of Industry 5.0
1.3 Concept and Definition of Human Capital Analytics
1.4 Features of Human Capital Analytics
1.5 Advantages and Disadvantages of Human Capital Analytics
1.6 Uses of Human Capital Analytics
1.7 How Human Capital Analytics Help in Industry 5.0?
1.8 Difference Between Industry 4.0 and Industry 5.0
1.9 Human Capital Analytics Enabled in Different Sectors
1.10 Challenges and Opportunities of Human Capital Analytics
1.11 HR Analytics and Artificial Intelligence
1.12 HR Analytics and Blockchain
1.13 HR Analytics and the Internet of Things
1.14 Level of Analytics Used in Human Capital Analytics
1.15 The Shift of People Analytics
1.16 Conclusion and Future Direction
References
2 Human Capital Analytics and Emerging Technologies in Industry 5.0
2.1 Introduction
2.2 Industry 5.0 and the Role of Human Capital
2.3 Emerging Technologies Shaping Human Capital
2.4 Human Capital Analytics: Enhancing Workforce Management
2.5 Challenges and Ethical Considerations
2.6 Recommendations for Organizations
2.7 Conclusion
References
3 Fifth Industrial Revolution and an Overview of its Impact on Human Resources
3.1 Introduction
3.2 Trajectory of Industrial Revolutions
3.3 An Overview of the Fifth Industrial Revolution
3.4 The Fifth Industrial Revolution and Human Resource Management of the Organizations
3.5 Fifth Industrial Revolution: Implications of the Technological Advancements on the Workforce
3.6 The Fifth Industrial Revolution: Opportunities and Challenges
3.7 Conclusion
References
4 Human Capital Transformation - Revolutionizing HR Practices with Advanced Technologies and Ethical Insights
4.1 Introduction
4.2 Systematic Review
4.3 Its Impact on HR
4.4 Conclusion
References
5 HR 5.0 Strategic Planning and Evolution of HR Functions in the Business Environment
5.1 Introduction
5.2 Evolution Cycle of HR Functions
5.3 Factors Influencing the Business Environment and its Connection to Strategic Planning
5.4 Three Steps to Strategic Planning in HR 5.0
5.5 Summary and Conclusion
References
Part 2: ANALYTICAL TECHNIQUES AND APPLICATIONS IN WORKFORCE MANAGEMENT
6 Investigating the Transformative Effects of AI, Machine Learning, and Robotics on Human Capital Analytics - An Empirical Study
6.1 Introduction
6.2 Literature Review
6.3 Research Design and Methodology
6.4 Data Analysis
6.5 Practical/Managerial Implications
6.6 Limitations of the Study
6.7 Future Research
6.8 Conclusion
References
Appendices (if applicable)
7 Unleashing Human Capital Analytics for Data-Driven Workforce Management
7.1 Introduction
7.2 Theoretical Background
7.3 Illustrative Case Studies: Transforming HR with Data-Driven Strategies
7.4 Strategic Integration: Deploying Data Analytics and Technology Solutions in HR Operations
7.5 Optimizing Talent Acquisition Through Data-Driven Human Capital Analytics
7.6 Data-Driven Strategies for Optimal Workforce Performance in Industry 5.0
7.7 Engage and Retain: Data-Driven Success
7.8 Navigating Complexities: Challenges and Strategic Solutions in Deploying Human Capital Analytics
7.9 Emerging Technological Solutions: AI and Machine Learning in HR Optimization
7.10 Conclusion
References
8 Mastering the Art of HR Data Collection and Management Techniques
Introduction
Importance of HR Data in Modern Organizations
Overview of HR Data Collection and Management
Data Collection Techniques
Data Management Systems
Leveraging HR Data for Decision-Making
Future Trends
Objectives and Benefits of Mastering HR Data Techniques
Fundamentals of HR Data
Data Privacy and Compliance Considerations
Data Collection Techniques
Computerized Data Collection Strategies
Best Practices for Accurate and Reliable Data Collection
Data Quality and Accuracy
Best Practices for Data Storing
Analyzing HR Data
Techniques for Data Examination
Using HR Data for Free Course
Using Data to Enlighten HR Game Plans and Strategies
Case Studies of Data-Driven HR Decision Making
Future Trends in HR Data Management
The Occupation of HR in the Time of Cutting-Edge Change
Assumptions for the Inevitable Destiny of HR Data Collection and Management
References
9 Communicating Human Capital Analytics Insights to Decision-Makers Effectively
Introduction of Analytics for Human Capital Analysis
Conclusion
References
10 Predictive Analytics - Next Chapter in Talent Management
10.1 Introduction
10.2 Predictive Analysis - A Game Changer
10.3 Challenges in Predictive Analytics
Conclusion
References
11 Employee Engagement and Performance Management - Critical Role of Employee Potentials
11.1 Introduction
11.2 Literature Review and Theoretical Concepts of the Study
11.3 Conceptual Framework
11.4 Research Objectives
11.5 Research Methodology
11.6 Findings and Discussions
11.7 Practical Implications
11.8 Limitations and Future Directions
11.9 Conclusions
References
12 Performance Evaluation and Measurement of Human Capital Analytics in the Information Technology (IT) Sector in India
12.1 Introduction
12.2 Literature Review
12.3 Objective of the Study
12.4 Methodology Adopted
12.5 Data Analysis
12.6 Discussion
12.7 Conclusion
12.8 Implications
References
13 Data-Driven Strategies to Improve Workplace Diversity, Equality and Inclusion (DEI)
13.1 Introduction
13.2 The Coalesce
13.3 The Contraption
13.4 Challenges and Deliberations
13.5 Conclusion
References
Part 3: FUTURE TRENDS, CHALLENGES, AND SUSTAINABLE DEVELOPMENT IN HR PRACTICES
14 Role of Human Capital Analytics and Industry 5.0 in Advancing the United Nations’ Sustainable Development Goals
14.1 Introduction
14.2 Review of Extant Literature
14.3 Conceptual Clarification
14.4 The UN SDGs
14.5 Inherent and Emerging Challenges
14.6 Concluding Remarks
References
15 Transforming Workplaces with Human-Robot Collaboration
15.1 Introduction
15.2 Sustainability and Industry 5.0
15.3 Industry 5.0, Human Resources, and Robots
15.4 Conclusion
References
16 Human Capital Analytics in Industry 5.0 for Sustainable Growth
16.1 Introduction of SDGs and the Role of Humans in Organization
16.2 Human Capital Analytics: Concepts and Applications
16.3 Literature Review
16.4 Integrating SDGs with Human Capital Strategies
16.5 Utilizing Data Analysis for Managing Talent and Development
16.6 Industry 5.0 Principles for Sustainable Value Creation
16.7 Challenges and Opportunities for Promoting Sustainable Development via Insights and Industry 5.0
16.8 Future Directions
References
17 HR 5.0 – Demystifying the Way Forward
17.1 Introduction
17.2 Literature Review
17.3 Recommendations for Solving the Challenges Faced in HR 5.0
17.4 Conclusion
References
18 The Digital Transformation of HRM: Comparing Traditional and Technology-Based Practices
18.1 Introduction
18.2 Background of HRM and Related Work
18.3 Research Methodology
18.4 Data Analysis and Interpretation
Conclusion
References
19 Technological Implementation of Industry 5.0 at Kinara Capital – A People’s Welfare Organization
19.1 Introduction
19.2 Kinara Capital
19.3 HR Team Responsibility
19.4 Highlights of HR in Employee Welfare
19.5 Kinara and Industry 5.0
19.6 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 4
Table 4.1
A comparison between Industry 4.0 and Industry 5.0.
Chapter 6
Table 6.1
Sample profile.
Table 6.2
Sample work profile.
Table 6.3
Overall goodness of fit of the model.
Table 6.4
Results of the reflective measurement model.
Table 6.5
Results of path coefficients and hypothesis testing.
Table 6.6
Results of mediation analysis.
A. Research instrument (survey questions).
Chapter 7
Table 7.1
Different types of HR analytics.
Table 7.2
Real-world case studies and success stories of human capital analyti...
Table 7.3
Essential elements for succeeding in the hr environment of industry ...
Table 7.4
Challenges and strategic solutions in deploying human capital analyt...
Chapter 12
Table 12.1
Financial performance of TCS (FY 2014 to 2023).
Table 12.2
Various returns of TCS.
Table 12.3
Employee cost, revenue, and value added.
Table 12.4
PAT and HEVA.
Chapter 18
Table 18.1
Key drivers of digital transformation.
Table 18.2
Employee expectations to digital transformation.
Table 18.3
Advantages of traditional HRM.
Table 18.4
Disadvantages of technology based HRM.
Table 18.5
Impact of use of technology on employee experience.
Table 18.6
How technology changes the way HR manages employee data.
Table 18.7
Key strategies for navigating digital transformation.
Table 18.8
Use of technology for better employee development.
Table 18.9
Key considerations for organizations while implementing technology ...
List of Illustrations
Chapter 2
Figure 2.1 Pillars of Industry 5.0.
Figure 2.2 Evolution of Industry 4.0 to Industry 5.0.
Figure 2.3 Human capital in the modern industrial landscape.
Figure 2.4 Synergy between technology and human expertise in Industry 5.0.
Figure 2.5 Impact of emerging technologies on workforce management.
Figure 2.6 Recommendations for organizations.
Chapter 4
Figure 4.1 A journey from Industry 1.0 to Industry 5.0. (Source: Sente venture...
Figure 4.2 Key Technologies. (Source: Research gate)
Figure 4.3 Number of citations (right) and journal articles (left) that Scopus...
Chapter 6
Figure 6.1 Conceptual framework. Source: Author’s own compilation.
Figure 6.2 Hypothesis testing through structural equation modeling. Source: Au...
Chapter 10
Figure 10.1 HR analytics and decision making. source: Self-Developed.
Figure 10.2 Challenges in HR analytics. Source: www.onemodel.co.
Chapter 11
Figure 11.1 Factors of employee potential. Source: Developed by the researcher...
Figure 11.2 Factors of employee engagement. Source: Developed by the researche...
Figure 11.3 Factors of organizational performance. Source: Developed by the re...
Figure 11.4 Conceptual model of the study. Source: Developed by the researcher...
Chapter 12
Figure 12.1 Total operating cost of TCS. (Source: TCS Annual Reports)
Figure 12.2 Four pillars of TCS’ HR transformation services. (Source: [60])
Figure 12.3 Various returns of TCS.
Figure 12.4 Human capital value added.
Figure12.5 Human economic value added (HEVA).
Chapter 13
Figure 13.1 The Coalesce: Data-driven strategies and DEI.
Figure 13.2 A glimpse of CultureMonkey’s employee survey platform.
Figure 13.3 A glimpse of CultureMonkey’s employee engagement drivers and AI-ba...
Figure 13.4 A glimpse of CultureMonkey’s anonymous feedback tool.
Figure 13.5 A glimpse of CultureMonkey’s employee feedback-action module.
Figure 13.6 A glance at the VIBE Index showcasing people’s sense of belonging ...
Chapter 14
Figure 14.1 The 17 UN SDGs [17].
Figure 14.2 Nexuses between HCA, Industry 5.0, and SDGs.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Human Capital Analytics
Exploring the HR Spectrum in Industry 5.0
Edited by
Deepa Gupta
GL Bajaj Institute of Management, Greater Noida, India
Mukul Gupta
GL Bajaj Institute of Management, Greater Noida, India
Pawan Budhwar
International Human Resource Management, Aston University, UK
Jim Westerman
Department of Management, Appalachian State University, Boone, NC, USA
Rajesh Kumar Dhanaraj
Symbiosis International Deemed University, Pune, India
and
Balamurugan Balusamy
Shiv Nadar University, Delhi-NCR Campus, Noida, India
This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-23832-3
Front cover images courtesy of Adobe FireflyCover design by Russell Richardson
Preface
The role of human capital is evolving due to advancements in technology in a world rapidly transitioning to the Fifth Industrial Revolution. In the era of Industry 5.0, the collaboration between humans and technology is more integrated than ever. Understanding the implications of these changes for human resources has never been more crucial. Human Capital Analytics: Exploring the HR Spectrum in Industry 5.0 delves deeply into how human capital analytics is revolutionizing HR practices in this new age. The book provides insights into the future of work, workforce management, and talent enhancement.
The structure of this book provides a comprehensive view of how HR is transforming in Industry 5.0. Initially, it addresses the primary opportunities and challenges presented by human capital analytics today. It also examines how emerging technologies like artificial intelligence (AI), machine learning, and robotics are significantly impacting HR functions. As organizations embrace these technologies, the strategic role of HR is shifting, necessitating new approaches to data-driven decision-making, workforce optimization, and people management.
A key focus of the book is the alignment of human capital analytics with global goals for sustainable development. Industry 5.0 is not solely about technological advancement but also about creating a future that benefits all, where human capabilities and technology converge to achieve global objectives. The book discusses how HR 5.0 strategies are crucial for fostering workplace diversity, equity, and inclusion, highlighting the need for comprehensive, ethical, and progressive approaches.
The chapters further explore the practical application of human capital analytics. They detail methods for collecting and managing HR data, employing predictive analytics in talent management, and assessing performance in India’s IT sector. These examples illustrate the growing importance of data and analytics in enhancing employee engagement, maximizing workforce potential, and ensuring organizational success.
As you read through this book, you will gain a deeper understanding of how Industry 5.0 is reshaping the HR landscape. It equips HR professionals, leaders, decision-makers, and students with the knowledge and tools necessary to thrive in a rapidly changing business environment, covering everything from strategic planning to the impact of AI and robotics. This book demystifies HR 5.0 concepts and techniques, enabling you to fully leverage human capital in this new era.
Human Capital Analytics: Exploring the HR Spectrum in Industry 5.0 offers a wealth of valuable insights and intelligent perspectives worth exploring. It represents a journey into the future of human resources, where technology and people collaboratively strive to create a better, more sustainable world.
We hope that this book will inspire further research and innovation in this exciting field and contribute to the development of practical solutions that can be implemented on a global scale. Finally, our gratitude goes to Martin Scrivener and the team at Scrivener Publishing for their support in bringing this volume to light.
February 2025
Part 1FOUNDATION AND STRATEGIC FRAMEWORK OF HUMAN CAPITAL ANALYTICS IN INDUSTRY 5.0
1Human Capital Analytics in Industry 5.0: Opportunities and Challenges
Veena Grover1*, Manju Nandal2, Divya Sahu1 and Mahima Dogra1
1Department of School of Management, Noida Institute of Engineering & Technology, Greater Noida, Uttar Pradesh, India
2Department of Management, Chandigarh University, Chandigarh, India
Abstract
For the past 10 years, the industry has been provided with Industry 4.0 to address the challenges and shortcomings; ultimately, the time has come for Industry 5.0. It is concerned with augmenting the digital shift with more effective and efficient collaboration between humans, machines, and systems within their digital ecosystem. Human capital analytics (HCA) incorporates this technology to make the most efficient use of a limited but valuable part of resources. The HCA is a traditional method for analyzing and effectively utilizing people’s data by shifting from viewing HR as an organization’s supporting arm to viewing HR as a source of revenue. The objective of this chapter is to describe the most significant potential for HC analytics in Industry 5.0 using the fundamental concepts of key technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and big data analytics. This article discusses a few essential aspects and difficulties that every firm may encounter when it comes to Industry 5.0. There is also a discussion of the benefits and drawbacks of Industry 5.0, as well as future research prospects and applications made obtainable by Industry 5.0. Finally, this chapter discusses the difficulty in recognizing the connected concerns, as well as how HCA has run its course and people analytics is emerging as a preferable strategy for using human capital.
Keywords: Human capital analytics (HCA), human-centered, technologies, robotics, digitalization, 6G systems
Abbreviations
•
HCA
Human capital analytics
•
IOT
Internet of Things
•
AI
Artificial intelligence
•
KPI
Key performance indicators
•
ROI
Return on investment
•
IT
Information technology
•
HR
Human resource
•
CV
Curriculum vitae
•
GDP
Gross domestic product
•
R&D
Research and development
•
CSTI
Council for Science, Technology, and Innovation
1.1 Introduction
Human capital has become more important than ever in this era of Industry 5.0, which revolves around the integration of today’s technologies like cyber-physical systems, artificial intelligence (AI), and the Internet of Things (IoT). HCA is included as a critical technique in any organization that is willing to optimize its workforce and use the whole efficiency of its human resources (HRs). HCA utilizes analytics and data-driven insights to improve different HR practices such as employee engagement, workforce management, talent acquisition, and performance management in decision-making processes. As we make a shift toward Industry 5.0, using HCA becomes important for businesses wanting to prosper where humans and intelligent machines are more linked. Integrating HCA as organizations traverse the intricacies of Industry 5.0 gives both possibilities for growth and obstacles to overcome. Success in this endeavor necessitates a strategic and ethical approach, with an emphasis on optimizing human-technology collaboration for a more productive and sustainable future.
Many HR managers today deal with greater complexity and difficult questions than their predecessors. These new inquiries, rather than focusing on traditional “personnel” issues, address essential business issues: Where should we locate our plant? Which M&A target will provide the necessary skills? Where should we put a new R&D facility? Why is our turnover rate so high in China [1]? Answering questions like this necessitates new information and new ideas. Today’s HR executive must think like an economist, someone who studies and oversees the allocation of limited resources. One of the rare resources in the global economy is talent. Embracing that perspective is half the battle. The other half is gathering relevant information. Companies are accustomed to monitoring internal “leading indicators,” but the economist-minded HR chief must also look outward [1]. Macroeconomic metrics such as GDP (gross domestic product), employment movements, and public infrastructure spending are crucial in guiding corporate decisions. By calibrating the limited talent expenditures that organizations can make, these leaders want to make their workforce more responsive to their organization’s current and future demands. Finally, HR decisions are like many other business decisions. Humanity is going through the Fourth Industrial Revolution, commonly known as “Industry 4.0,” which is based on the broad adoption of information technology (IT) in various domains of industry, process automation, and the spread of AI technologies [2].
Each decade, the magnitude of humanity’s economic, environmental, and social challenges grows. Industry 5.0 is a novel approach, and there is significant doubt regarding what it will offer and how it will impact business and manufacturing. People must prepare for changes that will be more complicated than those that humanity has previously encountered. Before moving on to Industry 5.0, it is important to understand what is wrong with Industry 4.0 and why it needs to be replaced. The need to boost industrial efficiency while simultaneously lowering production costs has resulted in the active implementation of automation in all its forms. The integration of physical, network, and computational processes inside a shared cyber-physical system is, by definition, complex [2]. We can conclude that several modifications are required, which will ultimately free us of most, if not all, of the difficulties. Primarily, it will enhance the significance of mankind, diminishing a substantial quantity of risks and dissatisfaction. Ultimately, he is the focal point of the cosmos; without his existence, there would be a void, therefore we cannot just exclude him from any future advancement. There should be a strong correlation between humans and machines, along with a widespread growth in trust in technology. These objectives can be achieved during the upcoming Industrial Revolution. Industry 5.0 refers to the fifth generation of development of industry, characterized by advanced technologies and automation [2]. At the April 2021 “international scientific conference on economic and social development problems”, the concept of Industry 5.0 was introduced. This word suggests a combination of human intelligence and the advanced capabilities of machines, resulting in a collective intelligence that will drive technological advancements, enhance human capabilities, and prevent technological singularity. During the Aveva World digital event on June 17, 2021, a comparable perspective was shared. It emphasized the ability to merge Industry 4.0 technologies with the human-centric concepts of Industry 5.0. These changes over time aim to facilitate the seamless connection between human intelligence and cognitive computing [2]. Industry 5.0 and HC analytics should be merged to succeed in a dynamic, world-class, and cutthroat competition. Industry 5.0 reflects a transition from a focus on value to the economy to a focus on social worth, as well as a transition from a focus on welfare to a focus on well-being. The concept of Industry 5.0 is not limited to only industry but is also being used in the organization and different sectors of the market. Industry 5.0 presents itself as a plan that fixes the worker’s well-being at the place of production, along with more resilient and environment-friendly production systems. Industry 5.0 is necessary for obtaining economic growth for the factory and competitive advantages in today’s business environment characterized by paid advancements in technology. It discusses analyzing the prospective applications and benefits of Industry 5.0 for organizations to increase their return on investment (ROI). The seismic shift of Industry 5.0 indicates the human and machine collaboration to increase the production efficacy of industrial goods. Leveraging HCA as organizations traverse the intricacies of Industry 5.0 gives both possibilities for growth and obstacles to overcome. Success in this endeavor necessitates a strategic and ethical approach, with an emphasis on optimizing human-technology collaboration for a more productive and sustainable future.
1.2 Evolution of Industry 5.0
The very first revolution in the industry was Industry 1.0, which took place in the 18th century. It comprised of utilization of machines to invent and create products or items with the help of tools and techniques. In 1760, the Industrial Revolution started in England and eventually reached America by the late 18th century. The arrival of Industry 1.0 noted a transition from a manual labor–based economy to one filled with the usage of equipment. This modification influenced various sectors like textile, mining, glass, agriculture, etc. [3]. The shift from 1871 to 1914 to the manufacturing industries is usually known as Industry 2.0 because it ensures faster transportation of persons and the introduction of new ideas. The present revolution is marked by economic growth, corporate efficiency, and a rush in unemployment rates due to the replacement of factory workers by machines.
By the 1970s, the commencement of Industry 3.0 took place, also generally regarded as the period of the digital revolution with the introduction of memory-programmable controls and computers for automation. The era is characterized by the large-scale production and utilization of integrated circuit chips and digital age technology. It encompasses technologies like digital cellular phones, computers, and Internet [4]. The progress of technology is changing both conventional objects and business procedures. Technology is undergoing a transformation into a digital format as a component of the digital revolution. Industry 4.0 refers to the integration of assets with current technologies like AI, cloud computing, robots, IoT, 3D printing, and other similar innovations [5]. Organizations that have implemented Industry 4.0 are very flexible and prepared to make decisions based on data. Industry 5.0 is the upcoming iteration of previous technological advancements, focused on enhancing the efficiency and intelligence of machines. The Industry 5.0 revolution signifies the collaboration between machines and humans to enhance the efficiency of industrial production. The productivity of production is growing due to the collaboration between human workers and universal robots [6]. Every executive team in the manufacturing company has the responsibility of establishing the production line, overseeing important performance measures, and assuring the smooth functioning of the activities. The production of robots and industrial is the future trajectory of Industry 5.0. The implementation of AI and computing technologies is expediting progress in the manufacturing business and augmenting operational efficiency within corporations [7]. Emphasizing the advantages of the manufacturing sector, Industry 5.0 also fosters sustainability by aiming to develop a renewable energy-powered system that operates in a sustainable manner. To implement Industry 5.0 in organizations, it is crucial for the staff to establish effective machine-to-machine and operator-to-machine interactions. Proficiency in fields such as robotics and AI is required [8]. The primary objective of a business organization is to generate profit. Decisions are based on sophisticated variables. To reduce costs, virtual schooling is essential for enterprises, as production is not required. Employee training should be discontinued.
1.3 Concept and Definition of Human Capital Analytics
HCA is a data-driven technique for managing as well as optimizing an organization’s workforce. It involves the use of data analytics, data mining, and other quantitative methods to extract meaningful insights from HR and other relevant data sources [9]. The aim of HCA is to make strategic decision-making in areas such as employee engagement, talent acquisition, workforce planning, and complete organizational effectiveness. HCA enhances statistical analysis and data to understand many aspects of human capital management within an organization. It comprises activities like retention, recruitment, training and development, and overall workforce productivity. Some of the components of HCA are as follows:
Data Collection:
Combining the important data from various sources within the organization, like employee surveys, records, performance reviews, and other workforce-related processes.
Data Analysis:
With the help of statistical methods and analytical tools, the collected data are analyzed. It comprises finding out trends, and patterns that can show valuable insights into workplace performance.
Visualization and Reporting:
With the help of visualizations like charts and graphs, it allows the outcomes to come in a clear and better format to help in decision-making by leaders and HR professionals.
Benchmarking:
For assessing performance and identifying areas for improvement, the organization’s human capital metrics are compared with internal historical data or industry benchmarks.
Strategic Decision-Making:
Applying the insights gained from analytics to making effective decisions about organizational development, and other HR strategies. This assists in aligning the workforce with the overall organizational goal.
Predictive Modeling:
Making use of predictive analytics to predict trends and results of human capital like forecasting employee turnover or forecasting skill gaps in the organization.
The objective is to enhance the effectiveness of HR management by making it more data-driven. By knowing the drivers and patterns of workforce performance, organizations can utilize their talent management strategies, allocate resources, and generate a more productive workforce [10]. It is necessary to note that successful implementation of HCA needs a mixture of technology infrastructure, analytical skills, and a powerful understanding of both HR practices and the organization’s business aims. Furthermore, ethical considerations, transparency, and data privacy are important features when working with HCA data.
1.4 Features of Human Capital Analytics
HCA involves using analytics and data to understand, manage, and utilize the organization’s human capital. Some of the features are as follows:
Talent Acquisition and Recruitment Analytics:
Analyzing the data related to talent acquisition and recruitment helps organizations find out the effective sources of talent, streamline the hiring process, and make data-driven decisions to attract and retain the right persons.
Employee Performance Analytics:
It analyzes data related to the performance of employees including key performance indicators (KPIs), performance reviews, and goals. It helps to identify areas of improvement, high-performing employees, and factors influencing performance.
Benchmarking Analytics:
Organizations get help by comparing human capital metrics against industry benchmarks by understanding how they stand against their peers. This is valuable for setting realistic goals for continuous improvement.
Workforce Planning:
It helps in forecasting and planning for future needs of the workforce in an organization. It assists the organizations in understanding their current workforce, finding the skill gaps, and planning for the development of the talent to meet the objectives of the organization.
Predictive Analytics:
This involves predictive modeling to forecast turnover, identify high-performing employees, and anticipate skill needs, future trends, and outcomes related to human capital.
Technology Integration:
HCA often integrates with HR systems and other technology platforms to collect and analyze relevant data efficiently. This streamlines the process and improves overall HR effectiveness.
Retention Analytics:
It helps organizations to understand the causes for employees leaving and identify strategies to improve retention rate by analyzing data related to turnover.
1.5 Advantages and Disadvantages of Human Capital Analytics
HCA has several advantages for organizations wanting to optimize their HR and workforce management. Some of them are as follows:
Efficient Recruitment Process:
Recruitment analytics helps in improving the efficiency of the hiring process by finding out the effective source channels, evaluating candidates, and streamlining the overall cycle of recruitment.
Effective Learning and Development Programs:
HCA assists organizations in assessing the training and development programs by analyzing data on skill development and impact on training, which, in turn, optimizes their initiatives to meet the needs of the employees and the organizations.
Cost Saving:
With the help of predictive analytics, organizations can estimate workforce trends and address potential issues proactively which helps in cost savings.
Strategic Workforce Planning:
It allows organizations to predict and plan for the future of workforce needs based on analytics and data. This ensures aligning the right talent at the right place to facilitate business goals and objectives.
Employee Engagement:
HCA measures and analyzes employee engagement, helping to find the areas for improvement and deploying strategies to enhance employee satisfaction.
Adaptive HR Strategies:
The HR department can adapt quickly to the changing dynamics of the workforce with the use of real-time data and analytics. This allows organizations to react effectively to shifts in markets, advancing technologies, and other external factors.
Better Productivity:
It contributes to overall productivity by identifying the factors that affect employee performance and engagement.
Now, while HCA has many advantages, there are also potential challenges related to its implementation. Some of them are as follows:
Lack of Standardization:
Most of the time, there is a lack of standardization in the tools used for HCA across organizations. It becomes challenging to measure performance and analyze data effectively.
Data Biases:
Another issue arises in the arena of biases in data which can affect the outcomes of HCA. For example, performance evaluations or promotion decisions that are biased can lead to biased data, contributing to existing inequalities within organizations.
Limited Predictive Accuracy:
When involving human behaviors, predictive analytics are not usually accurate even though they might provide valuable insights.
Data Quality and Integrity:
Outdated, incomplete, or inaccurate data can lead to flawed analyzes and inappropriate insights despite HCA being highly dependent on the accuracy and quality of the data being used.
Employee Resistance:
Resistance among employees due to constant monitoring of their performance and analyzing of their behavior can cause mistrust and negatively impact the atmosphere of the organization.
Cost of Implementation:
There is always a cost attached to technology investments and resources required for training and upgrading the staff to be able to use the newly implemented systems.
1.6 Uses of Human Capital Analytics
HCA is used in multiple facets of HR management to enhance decision-making, improve workforce performance, and align organizational strategy with human capital goals. Here are some major applications of HCA:
Talent Acquisition and Recruitment:
HCA helps in making neutral selections for hire by accessing data on candidate abilities, competencies, and efficiency during the recruitment process. Organizations can improve their employee attraction strategies by analyzing data to determine the most effective talent acquisition areas.
Workforce Planning:
HCA enables organizations to recognize existing skill gaps and prepare for future workforce demands through the examination of data concerning staff capabilities, qualifications, and training demands. Predictive analytics may lead to planning for succession by identifying workers with high potential and preparing for significant leadership changes.
Employee Performance Management:
For the purpose of giving information on team and individual performances, HCA focuses on employee performance statistics, including key performance markers. To create effective feedback and recognition programs, data analysis can be used for tracking and analyzing employee feedback.
Employee Well-being:
By analyzing data pertaining to employee welfare, healthcare usage, and absence from work, HCA may determine how successful health and wellness initiatives perform. Developing a welcoming workplace for workers involves an examination of factors impacting work-life balance.
Success Standards for HR Initiatives:
HCA performs the evaluation of many HR strategies such as engagement programs, compensation packages, and workmen training. Organizations can adjust their processes and guidelines to streamline the goals of the organization by evaluating the effects of HR policy.
Compliance and Risk Management:
HCA helps monitor and ensure compliance with labor rules, limiting the potential for legal complications in human capital management. Reviewing data may assist in identifying feasible personnel management risks, allowing organizations to act on preventive actions.
Learning and Development:
HCA inspects training programs by checking the data associated with improving skills, knowledge acquisition, and its impact on workforce performance.
Employee Engagement Satisfaction:
HCA undertakes an analysis of employee survey data to evaluate engagement levels, highlight issues impacting job satisfaction, and design plans for improvements. Predictive models can assist in identifying employees who will probably leave the organization, providing proactive approaches to retention.
1.7 How Human Capital Analytics Help in Industry 5.0?
HCA plays a vital role in Industry 5.0 by employing data-driven insights and analytics to optimize HR utilization in an increasingly integrated and technologically advanced industrial context. While the concept of Industry 5.0 may not be widely established as of my last knowledge update in January 2022, it can be speculated that it builds upon the foundation of Industry 4.0, emphasizing a more collaborative and holistic integration of human intelligence with advanced technologies [11]. HCA is poised to play a crucial role in Industry 5.0 by addressing the evolving dynamics between human workers and intelligent technologies. In Industry 5.0, where machine and human collaboration is likely to be more intricate, HCA becomes instrumental in optimizing this collaboration. Organizations can gain a vision of how effectively humans can work with each other and able to interact with new technologies. This information helps in improving training initiatives, ensuring that the workforce is equipped to work in a highly sophisticated technological atmosphere [13]. With the evolving technological landscapes, the skill requirements of the workforce also follow. HCA allows organizations to utilize the present skill sets of their employees, identify gaps in skills, and create customized training programs to hone the skills wanted for successful collaboration with new technologies. This adaptability is important in Industry 5.0, especially where some instant growth of current technologies is required for a workforce with a versatile skill set. Workforce planning is a central point in Industry 5.0 where the integration of emerging technologies is an ongoing process. HCA helps organizations in predicting workforce needs by analyzing the skill demand and identifying areas for upskilling or reskilling for the workforce. This approach helps to ensure that the workforce stays aligned with technological advancements. Employee well-being is considered most important in high-tech environments. HCA gives measures to monitor and assess factors like job satisfaction, stress, etc. Knowing the impact of technology on employee well-being allows organizations to implement procedures to maintain a stable and healthy workforce in Industry 5.0 [12].
Additionally, HCA contributes to helping organizations guide the cultural shifts linked with the integration of new technologies by changing management strategies in Industry 5.0. With the help of knowing potential resistance to change and knowing the implications for employees, organizations can create better change management initiatives that help to smooth the transitions, fostering an effective organizational culture [14]. HCA acts as a guiding force in Industry 5.0 that ensures the seamless merging of human intelligence and new technologies. It empowers organizations to guide the complex evolving industrial sphere while helping the human element remain at the peak of technological advancements.
1.8 Difference Between Industry 4.0 and Industry 5.0
The Fourth Industrial Revolution or Industry 4.0 remains a novel concept for many companies. Importantly, it entails using cutting-edge technology to improve productivity and efficiency. This comprises the integration of technologies like IoT, robotics, AI, sensor utilization, and system automation. There exists a new player known as Industry 5.0 beyond the realm of Industry 4.0 which shows a subsequent stage in industrial evolution. This recent development has been characterized by the incorporation of the human element into every phase of industrial operations [14].
With the adoption of Industry 4.0, organizations harness technology, increasing market competitiveness, efficiency, and productivity. Yet, it is inevitable to consider additional areas beyond the pursuit of technologically driven factories by AI and other technologies. This philosophy of Industry 5.0 is the foundation of today’s world. This method accepts the importance of technological advancements as well as the creative potential of human beings. The companies, that have made this shift like Tesla Motors, Apple, and Boeing, are recognized for the collaboration of personalization, technology, and innovation brought by human presence. The focus on big companies like Apple, Tesla Motors, and Boeing is not whimsical. Unlike smaller corporations struggling with the utilization of technology of Industry 4.0, these big corporations have transcended these limitations. They studied them and invested in the processes and values of the human factor which is important for business growth.
1.8.1 The Pillars of Industry 4.0
Data Analysis entails collecting and analyzing data from numerous sources to make better decisions and analyzes.
Autonomous Robots: Intelligent robots that can perform monotonous jobs that people previously accomplished. They can comprehend information and make decisions while completing their tasks.
Simulation: Simulating the performance of an industrial product or process using real-world data.
System integration: The organized and centralized merging of data from several departments within a corporation.
IoT: Physical devices linked together by sensors, software, and technologies that enable interaction.
Cybersecurity: Guaranteeing data security is critical, and this is accomplished through secure systems.
Cloud computing: Data and analysis are saved in the cloud for real-time access from anywhere in the world.
Additive manufacturing: By using 3D printing, it is feasible to make systems and components that are both stronger and lighter.
Augmented reality: When a product or physical item is combined with virtual reality to improve industrial operations.
1.8.2 The Pillars of Industry 5.0
This industry possesses immense possibilities to revolutionize the current operational practices of enterprises. The Japanese Council for Science, Technology, and Innovation (CSTI) has characterized the pillars of Industry 5.0 as follows:
Encourage initiatives that create new value for the future development of industry and social reform.
Address economic and social concerns.
Strengthen the pillars of scientific and technological innovation.
Create a virtuous systemic cycle of human capital, knowledge, and capital for innovation.
The distinction between Industry 4.0 and 5.0 is therefore evident. The first is concerned with technology and automation. The second step entails examining technology foundations and encouraging social transformation and HR actions. Industry 4.0 places a significant emphasis on machine connectivity, fostering increased automation in processes. In contrast, Industry 5.0 takes a dedicated approach to customer experience, often necessitating individualized and personalized considerations. A notable distinction between Industry 4.0 and 5.0 lies in the former’s focus on intelligent products with seamless traceability throughout the production process, while the latter prioritizes products designed to enhance the consumer experience through the integration of technology.
1.9 Human Capital Analytics Enabled in Different Sectors
HCA is utilized in different sectors as organizations recognize the benefit of implementing data-driven insights to enhance their employee resources. Here are some different key industries where HCA is typically utilized:
Corporate Enterprises:
Large organizations in different fields use HCA to improve their workforce, promote employee engagement, and make well-informed decisions concerning personnel management, training, and advancement.
IT and Technology Services:
HCA helps in talent acquiring, skill mapping, and discovering areas in continuing learning and development in the growing technological industry.
Healthcare:
Healthcare organizations implement HCA to supervise and improve the performance of healthcare employees, like doctors, nurses, and executives. It assists in employee planning, training, and enhancing overall treatment.
Finance and Banking:
HCA helps in recruiting employees, risk control, and compliance inside the financial sector. It serves as a requirement for identifying top-performing individuals and making sure the workforce may meet evolving regulatory needs.
Manufacturing and Industry:
HCA is used in manufacturing to maximize employee productivity, promote safety, and improve total operational efficiency. It helps by assessing skills, conducting training, and developing the workforce for the challenges of the Industrial Revolution.
Retail:
Retail businesses implement HCA for personnel management, boosting customer service, and predicting hiring needs during excessive seasons. It assists in comprehending consumer behavior and combining employee abilities with consumer demands.
Education:
HCA is used in the education sector for managing staff and educators, analyzing student success, and improving institutional performance. It helps in identifying productive methods of instruction and maximizing educational results.
Telecommunications:
Telecommunication organizations utilize HCA to effectively monitor their workers, integrate abilities with new technologies, and improve customer service. It supports hiring individuals for positions in network leadership, cybersecurity, and interaction with customers.
Energy and Utilities:
HCA has been utilized in the energy industry to simplify the labor engaged in producing energy, transportation, and service. It assists in recognizing inadequacies in abilities overseeing talent, and ensuring a proficient workforce for a shifting energy environment.
Government and Public Sector:
Government agencies implement HCA for workforce planning, boosting public service delivery, and ensuring employees possess the necessary abilities for the digital evolution of government services.
Transportation and Logistics:
HCA assists in optimizing labor scheduling, strengthening safety measures, and ensuring that the workforce is well-equipped to manage the intricate details of contemporary transportation and logistics systems in this sector.
Hospitality and Tourism:
HCA has been used in the hospitality sector to regulate varied workforce, optimize guest experiences through involving employees, and optimize employment based on seasonal needs.
HCA is a strong and powerful technology that can be used in many fields. The fact that it can offer statistical information for decision-making makes it crucial for organizations aiming to enhance their HR while managing the complexity of the modern business environment.
1.10 Challenges and Opportunities of Human Capital Analytics
1.10.1 Challenges of Human Capital Analytics
Some of the Challenges of HCA are as follows:
Privacy and Ethical Concerns:
It is always impossible to find the right proportion between the need for data and the rights of individuals. To manage these issues and boost trust in the workplace, effective privacy policies, gaining informed consent, and following ethical rules can be established in place.
Data Quality and Integration:
HCA can be less accurate and helpful if the data are compromised in their quality or if it is difficult to blend data from various sources.
Skills Gap in Analytics:
There are not sufficient skilled workers in various industries that can fully utilize and comprehend HCA. To make sure that data efforts are successful, the organization must invest money into training programs and hiring strategies to fill the skills gap.
Change Management:
When organizations use HCA, they often need to change their culture. Employees and supervisors who do not want to change can make it harder to use data-driven decision-making. Using effective interaction and change management plans, in addition to showing the real-life advantages of HCA, may help to overcome reluctance and encourage an environment of analytics.
Complexity of Models and Algorithms:
Complex sophisticated statistical models and formulas can make individuals hard to comprehend and apply, especially for people who are not technical. HCA can be made better by making the interfaces more user-friendly, teaching non-technical users, and motivating the HR and data science teams to work together.
1.10.2 Opportunities of Human Capital Analytics
Here are some of the Opportunities that come with HCA:
Strategic Workforce Planning:
HCA helps businesses estimate which abilities their employees will need in the future, search skill gaps, and make sure their employees are contributing toward identical strategic business goals. This proactive technique makes the organization more adaptable and competitive.
Talent Acquisition and Retention:
Organizations can make better decisions about attracting and retaining employees when utilizing predictive analytics. HCA helps firms find potential employees and put systems in place to keep key workers.
Employee Engagement and Well-being:
HCA offers details about how involved, happy, and healthy its employees are. The organizations can use this information to make efficient programs that may boost the workforce culture, working experience, and overall well-being.
Productivity and Performance Optimization:
Performance analytics are found to be located in the field of improvement, workflow enhancement, and upgrade overall productivity. Finally, it finishes by making an organization’s workings more efficient.
Flexible Learning and Development:
HCA provides altered learning and development programs that will allow employees to learn new things and create the requisite skills to perform their past and future work.
Diversity and Inclusion:
HCA assists in diversity and inclusion by providing relevant information on how to hiring procedures, looking for reasonable biases, and providing an improved working atmosphere.
Improving Human-Machine Duo:
With Industry 5.0, HCA makes it extra easy for humans and machines to work together without any issues. It utilizes both strengths and challenges to make the best collaboration possible, leading to total efficiency in the organization.
1.11 HR Analytics and Artificial Intelligence
HR analytics and AI integrate to construct a powerful synergy in the workforce. HR analytics advantages the data analysis to provide HR decision-making, while AI enriches the HR processes by using a machine learning approach and proper automation. These composites are transforming the way organizations hire, educate, and sustain, boosting the strategic and adaptive nature of HR to meet changing company needs. HR analytics and AI have a significant role in hiring and talent acquisition. Algorithms based on AI can rapidly analyze huge amounts of CVs, assessing candidates according to their set criteria. This will improve the recruitment process and enable a more reliable and unbiased initial selection. HR analytics provides insights on previous hiring data, permitting predictive analytics to fix the best applicants and anticipate their success for roles. AI may include processes like hiring, recruiting, and instruction for employees more personalized, which is advantageous for both the employer and the employee. Through the extensive use of AI, customized training, and onboarding personalization based on each worker’s personality, companies can help important steps efficiently. On the other hand, HR analytics plays an important role in identifying skill gaps in the workforce and importantly making sure that training programs are very precious and in accordance with strategic goals. When it applies to performance management, AI’s anticipating skills notify the company’s assessment of how well the workers will do. Machine learning algorithms assess past performance data, the outcomes of training, and other factors that are important in finding out how well a concept will be suited in the future. This capacity is to predict future goals that help HR analytics make options about succession planning, individualized job development, and advertisements that promote a culture of continually getting better.
1.12 HR Analytics and Blockchain
HR analytics and blockchain-based technologies are two distinct technologies that may combine collectively to change the numerous elements of HR management by including more open, secure, and efficient technology. HR analytics uses data and analytics to help employers make smart choices regarding the workforce. On the other hand, blockchain is a decentralized and dispersed technology that keeps records secure, open, safe, and not changeable. When it comes to HR analytics, using blockchain may make data precise and confidential. Security and the integrity of data are uses often issues with traditional HR tools. Data that have been kept are incapable of being changed or messed with the time it has been saved. Being not able to be changed can be particularly useful for making an accurate record of employee information, details, credentials, and performance metrics. HR analytics may use this safe and reliable data to find useful information that can assist them in generating smart decisions.
1.13 HR Analytics and the Internet of Things
The powerful duo of HCA and IoT, when worked together, can do wonders in improving the workforce by giving the benefits of real-time data from many interconnected devices. HR analytics uses data analysis to appraise HR decision-making, while IoT uses interconnected networks to share data. When they are mixed, they emerge into creating a dynamic environment where HR professionals gain deeper data about employees. HRA processes these data to gain insight into employee engagement, collaborations, and overall efficiency of the workplace. Another important application of this lies in employee health monitoring and well-being. With the help of wearable IoT devices, we can track employee’s physical activities, sleeping patterns, and stress levels. Then, HR analytics can analyze this data to find out the correlations with the parameters like work hours, job roles, etc. This gained information allows HR professionals to execute targeted programs, address potential burnouts, and create a better and healthier work atmosphere. IoT’s influence expands its wings to talent acquisition and management. IoT data can be used by smart recruitment tools to assess candidate’s digital footprints, determine their online presence, etc. In addition to that, IoT-enabled devices can give real-time performance metrics. For example, the sensors found in manufacturing environments can track production efficiency, allowing HR analytics to assess employee performance, know the areas for betterment, and create data-driven decisions for training and development.
Collaboration and employee engagement are other areas that are pretty much influenced by the combination of HR analytics and IoT. IoT sensors found in smart meeting rooms can track data on meeting durations, employee interactions, and participation levels. HR analytics analyzes these data to use collaboration patterns, utilize team dynamics, and identify communication strategies. These obtained data contribute to building a collaborative and engaged workplace culture. However, the amalgamation of HR analytics and IoT sometimes raises concerns about data related to privacy and security. Since sensitive data are gathered on IoT devices, organizations should impose tight security measures to protect the employees’ information. Employees must be clearly communicated regarding the aim and advantages of IoT data collection which is primarily crucial for building trust and ensuring compliance with privacy measures. There lies the vast potential for transforming workforce management with the dynamic combination of HR analytics and IoT. By utilizing real-time data from IoT devices, HR professionals can obtain valuable insights into employee performance, well-being, and behavior, allowing them to make better and informed decisions that enhance workforce efficiency, employee satisfaction, and overall organizational success. As technology continues to advance, the integration of HR analytics and IoT is likely to become increasingly sophisticated, offering new opportunities for innovation in HR management.
1.14 Level of Analytics Used in Human Capital Analytics
HCA encompasses understanding and development of the workforce of company management through the application of data and analytics. The degree of analytics utilized for HCA may differ, with organizations choosing varying degrees of complexity in accordance with their objectives, current assets, and data capabilities. Various levels of analytics are implemented within the domain of HCA which are as follows:
Descriptive Analytics:
At this stage, key documentation and descriptions of historical data are contained. It serves as an answer to the inquiry “What happened?” and gives perspectives on previous changes and patterns in the workforce. Data on employee turnover, trends in employees, time, and attendance are a few examples.
Diagnostics Analytics:
The objective of diagnostic analytics is to ascertain the roots of past incidences. It includes an analysis of past data to figure out the elements that have influenced specific results in the workforce. Analyzing the factors that contribute to high employee attrition and identifying factors that enhance employee engagement are two examples.
Predictive Analytics:
By applying machine learning models and statistical algorithms, predictive analytics can predict future workforce patterns or outcomes. It facilitates organizations in making proactive decisions and predicting potential problems, such as identifying high-potential employees, calculating workforce demand, and estimating employee turnover.
Prescriptive Analytics:
In addition to predicting the outcome, prescriptive analytics gives guidance on the specific actions that need to be executed to attain the desired outcome. It requires the application of training and optimization techniques. Examples: Requesting training initiatives to rectify deficiencies in skills, therefore optimizing the allocation of employees for optimal efficiency.
Advanced Analytics and Machine Learning:
By applying advanced machine learning algorithms, people can discover patterns, correlations, and valuable insights within tremendous and elaborate datasets. This may involve sentiment analysis, natural language processing, or anything more. Implementing machine learning to employee sentiment analysis derived from survey responses with the goal of identifying performance-influencing factors.
Many organizations initiate their analytics implementation with fundamental descriptive and diagnostic analytics, then advance to more sophisticated forecasting factors, directive, and strategic analytics. The level of complexity in analytics tends to be dependent upon the data maturity, technological framework, and analytical capabilities of the organization.
1.15 The Shift of People Analytics
1.15.1 People Analytics
People analytics, commonly referred to as HR analytics or workforce analytics, is a data-driven approach to managing people at work. It indulges the usage of statistical analysis, data mining, or other analytical techniques to extract meaningful insights from data related to HR. The primary goal of people analytics is to inform and improve decision-making processes in areas such as workforce planning, employee engagement, talent acquisition, and performance management. The human-centered approach prioritizes fundamental human interests and needs. Rather than modifying individuals’ talents, we leverage technology to adapt procedures to their requirements. Workers’ rights, autonomy, and human dignity are also protected. Technological advancements do not allow for the full provision of a sufficient degree of customers’ expected customization. Production workers continue to play an important role in this process, allowing technology to be used and improved [14]. After all, only people who exist can come up with unique concepts that can lead to product development. With the
