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Employee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s workplace. Embedded in this is the growing expectation of the human resource (HR) function to understand how workforce analytics informs the business and fuels success. This paper aims to explore how the HR function can achieve this.

The evolution of the “Future of HR” and how it is moving from “descriptive and diagnostic” to “prescriptive and predictive.”

According to KPMG’s 2019 Future of HR survey: 37 per cent of respondents feel “very confident” about HR’s actual ability to transform and move them forward via key capabilities such as analytics and artificial intelligence (AI). Over the next year or two, 60 per cent say they plan to invest in predictive analytics. Among those who have invested in AI to date, 88 per cent call the investment worthwhile, with analytics listed as a main priority (33 per cent). Despite data’s remarkable ability to deliver news insights and enhance decision-making, 20 per cent of HR believe analytics will be a primary HR initiative for them over the next one to two years, and only 12 per cent cite analytics as a top management concern.

Taking a page from meeting customer needs, innovative technologies such as AI and the cloud, data analytics can give an organization the potential to gather infinitely greater amounts of information about customers.

Today’s workforce analytics focuses mostly on what happened and why. For instance, you might have tools for identifying areas of high turnover and diagnosing the reasons. But thanks to advancements in technology and data analytics capabilities, HR is better-positioned to be the predictive engine required for the organization’s success.

There has never been a better time for HR to create greater strategic value, as the potential for meaningful workforce insights and analytics comes within reach. Even advancements in cloud-based systems for human capital management are coming packaged with analytics and visualization capabilities, enabling HR leaders to integrate people data with other data sources, such as customer relationship management, for a full view of the business.

This paper will be of value to HR leaders and practitioners who wish to use predictive analytics and emerging technology to drive performance improvement and gain the insights about their workforces.

The purpose of this paper is to explore the question: human capital analytics (HR analytics) – are we there yet? It will seek to clarify what is meant by “being there yet” and it will argue that the most positive proponents for this field are way too optimistic about the current state and what impact it will have on HR in the short-to-medium term but that the long-term outlook remain positive for the field.

This is a viewpoint paper and the conclusions draw upon the author’s experience in the field.

It has been widely acknowledged that HR analytics is still a fairly immature field and has not yet reached its full potential. In this viewpoint, the author argues that the most positive proponents for this field are way too optimistic about the current state and what impact it will have on HR in the short-to-medium term but that the long-term outlook remains positive for the field. The author names four main reasons why HR analytics is still in its infancy: maturity, mindset, organization and competencies.

If these four aspects are addressed, the HR analytics function will be able to contribute much more to HR’s role as a value generator.

Focusing on these aspects will set HR analytics up for success and will lead to potentially large shareholder value creation.

Sharing a practitioner perspective on the current value, challenges and future direction of HR analytics, from experience in a Fortune 500 company, to contribute to the development of the field in practice and academia. The paper aims to discuss this issue.

Perspective/position paper with practical findings.

HR analytics – i.e., applied management/OE science – clearly adds value when a number of pre-conditions are met. The value goes beyond talent outcomes, and applies to profits, cyber security, safety, and other outcomes.

HR/OE practitioners and academia should continue to work together, and consider both clear monetary value and change management when working toward evidence-based HR and evidence-based management.

The approach increases the impact of for- and non-profit organizations, giving higher impact at lower cost, via more efficient and effective use of human capital and also removes biases present in approaches that are not evidence-based.

Few very large companies have shared their experiences building up HR analytics, and this paper does exactly that from a large company that has invested heavily in HR analytics and is considered a front-runner globally (Shell). This showcases to practitioners and researchers what HR analytics can be, provided proper investments are made, and practitioners and researchers work together – i.e., what the impact of HR analytics is and what the challenges and pitfalls are.

The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.

A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.

Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.

Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.

Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.

In this paper, we explore the effects of emerging digital technologies on professionalization within organizations. Specifically, we examine how the emergence of data analytics as a new cross-functional profession rooted in new digital technologies is challenging human resources (HR) as an established organizational profession. Our qualitative study reveals how rhetorical work and material work have established a symbiosis between data science and HR. Rather than leading to de-professionalization, new technologies are enabling HR practices to be augmented and new actors to be integrated into the professionalization project, thereby elevating the status of HR. These findings contribute to the literature on the role of technology in institutional theory and its influences on the professionalization.

Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.

In this chapter, we apply diffusion of innovation theory and the theory of management fashion to examine the diffusion trajectory of human resource (HR) analytics in a U.S. context. We focus on the role mass media plays in influencing the diffusion process and address two research questions. First, does the mass media on HR analytics make observable the positive outcomes of HR analytics and is this related to increasing HR analytics adoption over time? Second, does the mass media on HR analytics show evidence of management trendsetting rhetoric?

We analyze published popular trade, business press, and peer-reviewed academic articles over a decade using a big data discourse analytical technique, natural language processing.

We find preliminary evidence that suggests that although the media has broadcasted positive outcomes of HR analytics, adoption has tailed off. In concert with the tailing off of HR analytic adoptions, the media appears to be recasting HR analytics as solving newer problems such as managing talent. Whether this shift makes a difference has yet to be determined.

Business press appears to influence the adoption process, both by broadcasting positive outcomes and through creating management fashion trendsetting rhetoric.

To promote the use of HR analytics, academic institutions and the HR profession need to train HR professionals in the use and benefits of HR analytics.

We lay the groundwork to improve our understanding of the role media plays in influencing how new HRM practices spread across organizations. We introduce the application of an emerging big data analytic technique, natural language processing, to analyze published media on HR analytics.

Introduction: The rapid growth of high technology has urged many organisations to dynamically look for innovative ways, ideas, testing, and ingenious solution in improving their current product, process, system, and technology. For contemporary business, artificial intelligence (AI)-based people analytics is an instrument currently employed to develop a better prosperous future.

Purpose: The study aims to investigate the usage of AI in human resource management (HRM) practices. It also examines the benefits and challenges of using AI in implementing people analytics in organisations.

Methodology: This chapter contains a systemic review of articles and papers on analytics. The presented qualitative study did a literature review based on the articles published in the last five years and extracted from the Scopus database.

Findings: This chapter indicated that AI-based people analytics is on the verge of changing various aspects of HRM practices better to furnish it for a vibrant, ever-changing workplace. It concludes different usage of AI in people analytics for better managing human resources (HR) at the workplace. Also explored the benefits and challenges of implementing AI in the people analytics domain.

Implications: This chapter will help understand ongoing practices of AI-enabled process benefits and challenges. This insight will help develop a better AI-enabled function for a better decision-making system. The future scope of the study is how to overcome the challenges.

The research paper aims to analyse the scholarly literature on advancing HR analytics as an intervention for attrition, a problem that lingers on organisational performance. This study aspires to provide an in-depth literature review and critically assess the knowledge gaps in HR analytics and attritions within organisational performance.

The review analyses the corpus of 196 research articles published in ostensible journals between 2011 and 2023. To identify research gaps and provide valuable insights, this study synthesises relevant studies using School of thought (S), Context (C), Methodology (M), Triggers (T), Barriers (B), Facilitators (F) and Outcomes (O) (SCM-TBFO framework). This study employs the R programming language to conduct a systematic literature review in accordance with the “preferred reporting items for systematic reviews and meta-analysis” (PRISMA) guidelines.

The emerging discipline of HR analytics encompasses the potential to manage attrition and drive organisational performance enhancements effectively. The study of SCM-TBFO encompasses a multidimensional approach, incorporating diverse perspectives and analysing its complex aspects compared to various approaches. The School of thought includes the human capital theory, expectancy theory and resource-based view. The varied research contexts entail the USA, United Kingdom, China, France, Italy and India. Further, the methodologies adopted in the studies are artificial neural networking (ANN), regression, structure equation modelling (SEM) case studies and other theoretical studies. HR analytics and attrition triggers are data mining decision systems, forecasting for firm performance and employee satisfaction. The barriers include leadership styles, cultural adaptability and lack of analytic skills, data security and organisational orientation. The facilitators were categorised into data and technology-related facilitators, human resource policies and organisational growth and performance-related facilitators. The study's primary outcomes are technology adoption, effective HR policies, HR strategies, employee satisfaction, career and organisational expansion and growth.

The primary goal of the literature review is to provide a comprehensive overview of the current state of HR analytics and its impact on organisational performance, particularly in relation to attrition. Further, the study suggests that attrition, a critical organisational concern, can be effectively managed by strategically utilising HR analytics and empowering data-driven interventions that optimise performance and enhance overall organisational outcomes.

The growth of the global labor force and business analytics has significantly impacted human resource management (HRM). Human resource (HR) analytics is an emerging field that creates value for employees and organizations. By examining the existing studies on HR analytics, the paper systematically reviews the literature to identify active research areas and establish a roadmap for future studies in HR analytics.

A portfolio of 503 articles collected from the Scopus database was reviewed. The study has adopted a Latent Dirichlet allocation (LDA) topic modeling approach to identify significant themes in the literature.

The HR analytics research domain is classified into four categories: HR functions, statistical techniques, organizational outcomes and employee characteristics. The study has also developed a framework for organizations adopting HR analytics. Linking HR with blockchain technology, explainable artificial intelligence and Metaverse are the areas identified for future researchers.

The framework will assist practitioners in identifying statistical techniques for optimizing various HR functions. The paper discovers that by implementing HR analytics, HR managers and business partners can run reports, make dashboards and visualizations and make evidence-based decision-making.

The previous studies have not applied any machine learning techniques to identify the topics in the extant literature. The paper has applied machine learning tools, making the review more robust and providing an exhaustive understanding of the domain.