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The fast-changing technological environment compels organisations to make swift and adaptive strategic choices, necessitating the use of tools and systems that can effectively manage complexities, foresee challenges, and capitalise on opportunities; as a result, there is a growing trend towards the adoption of human resource analytics to improve decision-making processes in the ever-evolving field of human resources. Despite the increasing use of HR metrics and analytics in human resource management, HR professionals have been relatively slow to embrace a data-driven approach, and existing research on data-driven decision-making in HRM often lacks the comprehensive frameworks necessary to provide practical guidance for the effective integration of human resource analytics. Hence, this study aims to explore the utilisation of human resource analytics to improve decisionmaking processes within organisations, highlighting both the potential benefits and the challenges that may arise. A systematic literature review revealed that the adoption of Human Resource Analytics offers organisations significant benefits, such as enhanced decision-making, better alignment with strategic objectives, improved employee experiences, a stronger competitive advantage, and reduced time expenditures. However, challenges persist, including concerns related to data privacy and security, the quality, integrity, and accuracy of data, a lack of data literacy and skills among employees, and inadequate technological infrastructure. To address data security concerns, HR technology providers must adopt robust security measures, including encryption, firewalls, and intrusion detection systems. Additionally, they should develop user-friendly data management systems and analytical tools that enable HR professionals to derive valuable insights from the data effectively.
Keywords:
Human Resource Management, HR Analytics, Decision Making, Challenges, Opportunities, Human Capital Theory
JEL Classification: O15, M12
Introduction
The rapid evolution of artificial intelligence (AI) is significantly reshaping the business landscape, especially within human resource management (HRM) (Dubey, 2023). This rapidly evolving technological landscape demands that organisations make strategic decisions quickly and adaptively. This requires tools or systems that help navigate complexities, anticipate challenges, and seize opportunities. Consequently, Okatta, Ajayi, and Olawale (2024) highlight that organisations are increasingly utilising human resource analytics (HRA) to enhance decision-making within the dynamic realm of human resources (HR). For Khaliq and Saritha (2023), HRA, often referred to as people analytics or talent analytics, entails the use of analytical methods on HR data to identify significant patterns, trends, and relationships. It refers to the methodical examination of HR data aimed at guiding decision-making processes, improving organisational effectiveness, and refining workforce management approaches (Okatta et al., 2024). HRA incorporates AI into HR processes, allowing organisations to enhance their ability to analyse, predict, and diagnose various workplace challenges, ultimately leading to more informed decisions regarding employee management (Dubey, 2023). The growing interest in HRA has led to its adoption across various management disciplines, particularly in HR, where departments are increasingly using data to enhance decision-making (McCartney & Fu, 2022). This approach allows organisations to transcend conventional HR methods, enabling them to make data-driven decisions that enhance both workforce effectiveness and overall business outcomes (Khaliq & Saritha, 2023).
It is widely recognised that industries driven by innovation are increasingly becoming the primary catalysts for global economic recovery and growth (Li, Guo & Cao, 2021). Khaliq and Saritha (2023) mention that the management of the workforce has grown more intricate due to several factors, including globalisation, advancements in technology, and shifts in workforce demographics. Therefore, the process of decision-making within organisations is inherently complex and dynamic, influenced by a multitude of internal and external factors (Li et al., 2021). Krishna, Sharma, Jadhav and Manoj (2022) add that decision-making has evolved into a multifaceted and often unpredictable endeavour that hinges on accurate information. In this current dynamic business landscape, organisations are increasingly acknowledging the vital importance of HR in achieving organisational success. Katfi, Mrhari, Katfi and El Mnouer (2023) state that HRM is a crucial aspect of organisational operations, focused on maximising the potential of human capital. It includes various functions, policies, and practices aimed at recruiting, training, motivating, and retaining employees. Hence, every day, organisations face the necessity of making choices among various potential solutions, which can range from straightforward to intricate decisions (Neziraj & Shaqiri, 2018). The outcomes of these selections may yield immediate effects, but they can also have significant long-term implications, contingent upon the timeliness and effectiveness of the decision-making process (Neziraj & Shaqiri, 2018). Thus, organisations are increasingly leveraging digital technologies to enhance the efficiency of their decision-making processes (Ilieva & Logeais, 2021). Neziraj and Shaqiri (2018) underscore that rapid advancements in technology and networking contribute to frequent changes in this landscape, introducing new models, methods, and tools for decisionmaking. Central to these advancements is the concept of data-driven decision-making, which emphasises the systematic use of analytics and data to inform and validate managerial decisions (Tuli, Varghese & Ande, 2018). Hence, organisations can harness data-driven insights to better understand workforce dynamics, identify trends, and make informed choices that enhance employee performance and satisfaction. Diván (2017) asserts that data-driven decision making refers to the approach of making choices based on data analysis rather than relying solely on intuition. This analytical approach enables HR professionals to align talent management strategies with organisational goals, ultimately fostering a more productive and engaged workforce. Tuli et al. (2018) compliment that HR professionals can enhance organisational performance by anticipating emerging trends, proactively tackling challenges, and leveraging predictive modelling and advanced analytics to gain a comprehensive understanding of workforce dynamics. Organisations that successfully leverage analytics as valuable tools can enhance their decision-making processes as they are better equipped to make informed and precise decisions (Krishna et al., 2022). While it offers numerous advantages, the existing literature indicates that it also poses certain challenges. Thus, the objective of this study is to explore the utilisation of HRA to improve decisionmaking processes within human resource departments (HRD), while also addressing the associated challenges and opportunities.
In the current fast-paced business landscape, organisations encounter significant challenges in effectively managing their HR as conventional HR methods are increasingly inadequate to tackle the complexities introduced by globalisation, rapid technological progress, and evolving workforce demographics (Khaliq & Saritha, 2023). Therefore, the challenges faced by decision-makers are often complex and varied, influenced by an environment rife with uncertainty and risk; this complexity complicates the decisionmaking process, making it more demanding (Neziraj & Shaqiri, 2018). Ilieva and Logeais (2021) point out that managers often encounter biases that can skew their rational decision-making processes, necessitating the pursuit of strategies to eliminate or reduce these biases. The authors comment that cognitive biases such as confirmation bias, anchoring, and loss aversion consistently pose challenges for managers during decision-making (Ilieva & Logeais, 2021). These biases can distort judgment and lead to suboptimal choices, making it essential for leaders to recognise and mitigate their effects. Sutherland and Cook (2017) note that the challenge with relying on intuition in decision-making lies in its inherently subjective nature, which often lacks the support of documented evidence or prior experiences to validate the chosen actions. Hence, as the complexity of business decision-making grows, the demand for accurate information becomes increasingly critical to facilitate effective decision-making (Hobfeld, 2017).
Moreover, Sandeep and Bhambu (2023) state that recent years have seen a surge in interest surrounding new analytical methods, particularly big data and AI, leading to a plethora of reports on HRA. However, management researchers have paid relatively little attention to this area (Marler & Boudreau, 2017). Furthermore, while organisations have been employing HR metrics and various levels of analytics within HRM, HR professionals have often been slow to adopt a data-driven approach. In addition, research on data-driven decision-making within HRM often lacks comprehensive frameworks that offer practical guidance for the integration of HRA. This gap highlights the need for structured approaches that can effectively facilitate the incorporation of data analytics into HR practices, ensuring that organisations can leverage data to enhance their decision-making processes (Tuli et al., 2018). Therefore, the aim of this study is to examine how HRA can enhance decision-making processes, while also considering the challenges and opportunities that arise from its implementation.
The significance of this research lies in its capacity to equip organisations with actionable insights as they navigate the complexities of digital transformation within the realm of HRM. Traditionally, HRM decisions were predominantly shaped by personal experience, intuition, and qualitative assessments (Tuli et al., 2018). Thus, the findings aim to empower organisations to enhance their HR functions, thereby facilitating a smoother transition into a digitally driven operational landscape. Through this approach, the research seeks to bridge the gap between theoretical knowledge and practical application, ultimately contributing to the overall efficiency and effectiveness of HRM in the context of ongoing technological advancements.
Furthermore, the findings of this research align with some of the Sustainable Development Goals (SDGs), particularly Goal 8 and Goal 9. Goal 8 emphasises the importance of promoting sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. Organisations that implement advanced HR strategies can enhance decision-making processes that lead to improved employee engagement, productivity, and job satisfaction, ultimately contributing to a more balanced workforce and economic stability. Goal 9 focuses on building resilient infrastructure, promoting inclusive and sustainable industrialisation, and fostering innovation. The adoption of innovative HR practices not only supports the development of a skilled workforce capable of driving technological advancements but also encourages a culture of innovation within organisations.
Literature Review
Theoretical framework
This study is grounded on the Human Capital Theory (HCT) that highlights the importance of investing in HR, viewing employees as essential assets that drive organisational success. HRA supports this perspective by offering valuable insights into the development, effective use, and optimisation of human capital, ultimately aiming to improve overall organisational performance (Khaliq & Saritha, 2023). The HCT underscores the significance of investing in the development of employees and assessing its effects on overall performance. HRA plays a crucial role in this framework by offering valuable metrics and insights that inform decisions regarding training and development initiatives (Dubey, 2023). The utilisation of HRA represents a transformative approach to decision-making within organisations, offering a wealth of opportunities for enhancing operational efficiency and strategic planning. At its core, the theoretical foundation of this practice is rooted in data-driven decision-making, which emphasises the importance of empirical evidence in guiding HR strategies. Thus, organisations can uncover patterns and insights from employee data, enabling them to make informed choices regarding talent acquisition, performance management, and workforce development. As organisations increasingly recognise the value of data in shaping their human capital strategies, the integration of HRA becomes essential for driving competitive advantage and achieving sustainable growth.
Levels and types of decision making
While the term 'decision' is frequently equated with 'choice', Lieva and Logeais (2021) argue that it is more advantageous to perceive decisions as an institution. The authors contend that individuals within an organisation participate in a 'decision-making process' that is governed by specific rules and involves multiple participants, which justifies viewing these processes as institutions. For Tomić (2023), decision-making entails selecting from various alternatives by gathering and assessing information, weighing the potential outcomes of each option, and making informed choices based on this evaluation. It plays a vital role in organisational contexts, encompassing a spectrum of complexities from everyday decisions to extensive strategic planning. Neziraj and Shaqiri (2018) assert that the decision-making process consists of several interconnected steps, with the selection of an option being a crucial element that precedes the forecasting of potential solutions, and the evaluation of the outcomes associated with the final choice. This selection significantly influences the overall success or failure of the endeavour, highlighting the importance of careful consideration in the decision-making framework.
According to Neziraj and Shaqiri (2018), the classification of HR decisions can be organised based on the degree of structuring (structured, semi-structured, non-structured) and the decision level (strategic, functional, operational). At the strategic level, examples include structured decisions like workforce forecasting, semi-structured decisions such as HR policy planning, and non-structured decisions related to the effects of mergers and acquisitions. Moving to the functional level, structured decisions encompass recruitment, while succession planning management is semi-structured, and the implementation of an HR information system falls into the non-structured category. Finally, at the operational level, structured decisions involve employee benefits participation, semistructured decisions pertain to candidate selection, and non-structured decisions address absenteeism management.
Leveraging data analytics in the HR department
Analytics involves the examination of historical data to identify trends, while HRA specifically utilises human capital data to offer senior management a comprehensive view of the organisation's dynamics (Krishna et al., 2022). The concept of HRA was initially introduced by Fitzenz (1978), who suggested the development of metrics to assess the impact of HR activities on an organisation's performance (Alam, 2024). Dubey (2023) argues that the use of data in HR decision-making has evolved from basic metrics focused on administrative tasks to advanced analytics driven by technology and data science. This data encompasses various aspects, including workforce demographics and profiling, as well as system-generated information such as payroll, benefits, and leave entitlements (Krishna et al., 2022). HRA has become an essential resource for organisations seeking to utilise their workforce data effectively, enabling them to make strategic decisions that enhance overall performance and drive success (Khaliq & Saritha, 2023). The incorporation of data-driven analytics into HRM represents a significant advancement in contemporary organisational strategies. Srivastava (2025) notes that it significantly enhances modern decision-making by transitioning organisations from intuition-based methods to data-driven approaches. As organisations navigate an increasingly intricate and competitive landscape, the capacity to make well-informed, strategic choices regarding their workforce becomes essential (Okon, Odionu & Bristol-Alagbariya, 2024). Thus, HRA utilises data and statistical methods to enhance personnel management decisions by analysing historical data to inform future choices (Katfi et al., 2023). Ben-Gal (2019) identifies three key functions of HRA: (1) enhancing decision-making processes in areas such as recruitment, employee retention, and personnel development; (2) providing valuable insights for effective personnel management; and (3) aiding in the execution of the organisation's strategic objectives.
Effective decision-making in HR relies on utilising relevant data to diagnose, select, evaluate, and optimise actions that maximise desired outcomes with available resources (Katfi et al., 2023). Tomić (2023) asserts that this is achieved through better data collection, analysis, and dissemination, which leads to more informed and strategic choices. Today, HRA leverages big data and machine learning to predict trends, uncover patterns, and deliver actionable insights for workforce management (Dubey, 2023). A significant 84% of professionals regard the implementation of HRA as important or very important (Álvarez-Gutiérreza, Stone, Castaño, and García-Izquierdo, 2022). Whether for making strategic decisions or tactically adjusting HR policies, the information available to decision-makers is crucial. Decision-making requires a cross-cutting analysis, beyond HR aspects, as HR metrics alone are often insufficient to legitimise HR decisions (Katfi et al., 2023). Together, these data sets provide valuable insights that can address management's inquiries regarding the organisation's operational effectiveness (Krishna et al., 2022) as displayed in Figure 1.
Types of human resource analytics
Data-driven decision making within the organisation involves making various choices informed by the available data. According to Boatman (2025) and Khaliq and Saritha (2023), HRA utilises various data analysis techniques to reveal patterns and insights within organisational data encompassing descriptive, diagnostic, predictive and prescriptive analyses.
Descriptive analytics focuses on summarising and visualising data (Khaliq & Saritha, 2023). Also called decision analytics, Boatman (2025) explains that it employs statistical analysis techniques to interpret and summarise historical raw data, focusing on past events without making future predictions. Descriptive analytics serves as a crucial resource for HR professionals, enabling them to interpret extensive historical data and pinpoint opportunities for improvement and process optimisation, thereby laying the groundwork for more sophisticated analytical approaches (CHRMP, 2025). Alam (2024) explains that it employs tools for data mining and data aggregation to explore questions regarding the occurrence of specific events and the reasons behind outcomes. It is valuable for assessing behaviours, comparing characteristics over time, identifying anomalies, and recognising strengths and weaknesses within the data (Boatman, 2025). For example, it offers HR teams critical insights into employee performance, engagement, and retention by analysing data from employee surveys (Justworks, 2024). This analysis helps identify trends in satisfaction, enabling HR to implement workplace improvements and address issues that may affect employee morale (Justworks, 2024).
To comprehend the reasons behind the events that transpired, organisations utilise diagnostic Analytics to provide clarity on the root cause of the problem (Sharma, Jha & Dash, 2022). Diagnostic analytics aims at identifying causes and correlations. Khaliq and Saritha (2023) observe that it builds on descriptive analytics by summarising historical data and exploring the underlying reasons for observed trends and anomalies. In this context, the historical context of the data serves as a crucial resource that enhances the decisionmaking process (Diván, 2017). Boatman (2025) argues that it can help organisations investigate rising employee absenteeism by analysing patterns in absence data, such as specific days of the week or periods following unapproved time-off requests. HR can understand the underlying reasons for these absences through employee feedback and exit interviews and implement targeted strategies to address the issues (Boatman, 2025). Moreover, diagnostic analytics enables organisations facing high turnover rates to pinpoint the root causes, such as departmental issues, management shortcomings, or insufficient career development and compensation, allowing HR professionals to implement targeted strategies to enhance employee retention and reduce absenteeism (CHRMP, 2025).
Predictive analytics forecasts future outcomes. It serves as crucial assets in various HR functions, particularly in recruitment, where it can assess data from resumes and job descriptions to identify essential skill sets and attributes that indicate a candidate's potential for long-term success (Sandeep & Bhambu, 2023). Additionally, it enables organisations to anticipate talent management outcomes, such as employee turnover, by identifying individuals at risk of leaving and the factors influencing their decisions (Boatman, 2025). HR can analyse data related to employees' potential risks by assessing areas like growth opportunities and compensation packages, allowing them to implement strategies that enhance retention and reduce turnover rates (Boatman, 2025). Furthermore, predictive analytics enables HR professionals to foresee workforce trends, such as employee turnover and skills shortages, allowing them to implement strategies to mitigate these issues proactively (CHRMP, 2025). This type of analytics can help HR identify employees at risk of leaving and those likely to be promoted and address retention factors and focus on targeted training and development initiatives (CHRMP, 2025). Sharma et al. (2022) note that the successful implementation of predictive analytics enables organisations to effectively leverage big data, provided they possess knowledge of statistics and programming languages such as R and Python.
Prescriptive analytics, the most complex level of data analysis aimed at determining optimal actions, relies on the successful completion of three foundational analytics stages to ensure effective outcomes (Sharma et al., 2022). It offers actionable recommendations. Prescriptive analytics represents the most advanced phase of the analytics process, transforming predictive insights into actionable strategies using big data and various technical tools such as machine learning, algorithms, artificial intelligence, and pattern recognition (Boatman, 2025). Prescriptive analytics employs statistical models to analyse data and recommend targeted actions, akin to a doctor's prescription for preventive care (Sandeep & Bhambu, 2023). In the context of staffing, Boatman (2025) states that it enables HR to anticipate future needs by analysing employee interactions with digital benefits, which can indicate potential vacancies, particularly in response to increased activity related to retirement planning or family leave policies. For instance, if an organisation faces rising employee turnover, prescriptive analytics can propose strategies such as enhancing employee engagement, improving training programs, or offering better compensation to mitigate the issue.
Each of these four types plays a crucial role in enhancing decision-making processes within HR as encapsulated in Figure 2.
HR Analytics in Practice
HRA involves the systematic analysis of data, whether large or small, in Excel sheets or other formats, to enhance employee performance and retention (Alam, 2024). HRA make use of data and analytical techniques to derive insights and facilitate informed decision-making regarding various HR functions, including employee performance, engagement, recruitment, retention, and training and the steps are depicted in Figure 3.
To effectively leverage HRA, it is essential to define clear objectives, gather and prepare relevant data, and apply analytical techniques to derive insights that inform decision-making. Following this, stakeholders should communicate findings, develop action plans based on the insights, and continuously monitor the impact of implemented changes to ensure ongoing improvement in HR practices.
Sources of data analysed through HRA
HRA involves the systematic gathering and analysis of HR data to enhance decision-making and boost organisational performance across various functions such as talent acquisition and employee retention. Sandeep and Bhambu (2023) state that HRA utilises various data types to assess and comprehend employee behaviour, performance, and engagement. It utilises data from diverse sources to enable organisations to better understand workforce dynamics and align HR strategies with overall business goals (Dubey, 2023). Fu, Nicholson and Easton (2024) argue that data consists of a collection of facts that can be stored and has the potential to provide valuable insights to its users. HRD rely on HR information to manage their daily operations. This encompasses the entire employee lifecycle, from attracting candidates to handling their exit or retirement from the organisation (Wijesingha & Wickremeratne, 2020). All personal data related to employees is stored in a centralised database, ensuring easy access and efficient management of information (Wijesingha & Wickremeratne, 2020). Sandeep and Bhambu (2023) argue that key categories include demographic data, which reveals trends and disparities among diverse employee groups; performance data, which evaluates job effectiveness and identifies high performers; and training and development data, which assesses the impact of training programs on employee growth and performance. Moreover, employee satisfaction and social media data can be used to assess and enhance employee behaviour, performance, and engagement, enabling organisations to identify improvement areas and develop effective strategies for fostering employee well-being (Sandeep & Bhambu, 2023) as depicted in Figure 4.
Materials and Methods
In this study, a comprehensive literature review was conducted to identify relevant publications in the field of human resource management, specifically focusing on decision-making processes. The initial search yielded a total of 532 records from various academic databases, including Scopus, Google Scholar, Emerald, and Elsevier. After removing 18 duplicate entries, 514 studies were considered for further evaluation. An initial screening of the abstracts led to the exclusion of 186 studies that did not meet the inclusion criteria. This left 328 studies for full-text review, during which an additional 248 studies were discarded based on their content. Ultimately, 80 studies were included in the final analysis. The exclusion criteria were strictly defined to ensure relevance, encompassing publications that were not directly related to human resource management, those that did not pertain to decisionmaking, and any studies published more than ten years ago. This thorough selection process ensured that the included studies were both current and relevant to the research objectives as depicted in Figure 5.
Workforce analytics plays a crucial role in data-driven decision-making within HRM.
Findings and Discussion
Workforce analytics plays a crucial role in data-driven decision-making within HRM.
Opportunities in HRA usage
According to Srivastava (2025), the implementation of HRA presents organisations with substantial advantages, including improved decision-making, better alignment with strategic goals, improved employee experience, a stronger competitive edge, and time reduction.
Improved decision-making
HRA offers valuable insights through the analysis of workforce data and facilitation of evidence-based decision-making (Srivastava, 2025). Tuli et al. (2018) comments that HRA employs statistical methods and predictive modelling to analyses HR data and identify trends, patterns, and correlations that inform strategic workforce planning and decision-making. Srivastava (2025) notes that this approach minimises reliance on assumptions, enhances accuracy, and aligns HR strategies with organisational objectives, ultimately fostering efficiency, productivity, and improved outcomes in talent management and operational processes. HRA provides valuable organisational data that equips managers and executives with actionable insights for informed decision-making. When organisations integrate these insights from HRA with other evidence sources, they enhance the effectiveness of their decisions, ultimately leading to improved organisational performance (McCartney & Fu, 2022). Dubey (2023) points out that HRA is essential across various HR functions, as it utilises predictive analytics to enhance talent acquisition by identifying top candidates and forecasting their future performance. Sandeep and Bhambu (2023) mention that many HR managers support the use of analytics to pinpoint and measure the HR factors that influence business outcomes within their departments. Hence, the term 'people analytics', often viewed as a more neutral designation and consistently utilised by Google, which has adopted the term 'people operations' in place of 'HR department' (Heuvel & Bondarouk, 2017). Leading software providers, such as Workday and SAP's SuccessFactors, refer to their offerings as 'workforce analytics' (Heuvel & Bondarouk, 2017). For example, the recruitment process can be expedited and made more efficient when the organisation analyses sufficient data to identify trends. This enables hiring managers to pinpoint the specific skills, backgrounds, and expertise needed for various roles, allowing for a more targeted approach to talent acquisition (Sharma et al., 2022). Additionally, it improves performance management and employee engagement by providing insights into productivity, job satisfaction, and factors influencing employee retention (Dubey, 2023). The increasing reliance on data analytics in HR is transforming organisational operations by providing a factual basis for decision-making. This approach not only aids in promoting equitable hiring practices and identifying representation gaps but also plays a crucial role in evaluating the success of diversity initiatives (Alam, 2024). Organisations that successfully leverage these advanced techniques, can better understand patterns and trends that influence workforce dynamics, ultimately enhancing decision-making processes and improving overall productivity (Sandeep & Bhambu, 2023).
Better alignment with strategic goals
Srivastava (2025) posits that HRA plays a crucial role in aligning HR strategies with the goals of the organisation by providing datadriven insights into workforce trends, skill gaps, and performance metrics. This alignment allows HR to evolve from a traditional support role to a strategic partner, thereby directly contributing to the overall success of the organisation (Srivastava, 2025). HRA focuses on both the examination and enhancement of human capital, as well as the application of analytical methods alongside employee data to guide organisational strategy and boost performance (McCartney & Fu, 2022). Using HRA, organisations can effectively synchronise their human capital strategies with their business objectives, streamline talent management processes, and improve both employee engagement and productivity (Khaliq & Saritha, 2023). Therefore, HRIS is used to facilitate the collection, storage, manipulation, retrieval, and dissemination of HR data, and is designed to generate reports on key performance indicators (KPIs) (Kim et al., 2021; Zhou, Liu, Chang & Wang, 2021; McIver, Lengnick-Hall & Lengnick-Hall, 2018; Schiemann, Seibert & Blankenship, 2018). Krishna et al. (2022) note that HRA enhances not only reporting capabilities but also supports the overarching management of the business. Simon and Ferreiro (2018) detail the implementation of an HRA program at Inditex, which enabled HR managers to utilise key performance indicators for informed workforce decisions, ultimately enhancing organisational performance.
In addition, HRA provides a distinct perspective that enables HR to demonstrate its value and role as a strategic partner in driving an organisation's success. By leveraging reliable data, HR professionals can enhance the talent acquisition, retention, and engagement strategies of business leaders, thereby contributing to overall organisational effectiveness (Krishna et al., 2022). The rapid advancement in HR technology, including HR information systems (HRISs), cloud platforms, and applications, has empowered HRD to gather, manage, and analyse extensive amounts of employee data, a significant improvement over previous legacy IT systems (Kim, Wang & Boon, 2021). For instance, senior executives can effectively monitor current salary trends and general and administrative expenses (SGAE) through advanced HR tools (Krishna et al. (2022). Furthermore, Bank of America partnered with Humanise to leverage HRA through innovative ID badges equipped with technology for data collection, leading to improved HR and business outcomes (Kane, 2015). Additionally, these systems can integrate advanced analytics and reporting modules that leverage big data, business intelligence, and statistical methods to forecast both shortand long-term workforce trends (Garcia-Arroyo & Osca, 2019; Mikalef, Boura, Lekakos & Krogstie, 2019; McIver et al., 2018; van den Heuvel & Bondarouk, 2017; Stone et al., 2015).
Improved employee experience
HRA improves the quality of recruitment and talent management as well as enhances employee productivity and help decrease turnover rates (Dubey, 2023). HRA extends beyond recruitment and selection, providing organisations with tools to tackle a range of HR issues such as employee engagement, diversity and inclusion, and turnover. Tuli et al. (2018) underscore that the adoption of data-driven approaches has emerged as a transformative element, particularly in HRM, enabling organisations to boost employee engagement, optimise workforce performance, and foster sustainable growth. HRA examines feedback and behavioural data to uncover the drivers of employee engagement and satisfaction (Srivastava, 2025). It assesses organisational performance by regularly evaluating employee performance, identifying both high and low achievers (Khan, Malik & Khan, 2024). Krishna et al. (2022) explain that data analytics plays a crucial role in comprehending employee needs, pinpointing high-performing individuals, and identifying those who may benefit from further training. Alam (2024) adds that HR professionals can analyse employee behaviours to identify factors that influence satisfaction and engagement. HRA enhance an organisation's understanding of employee performance by collecting and analysing data on their interactions and time management (Sharma et al., 2022). This understanding enables organisations to develop targeted strategies, such as customised training programs and career development opportunities (Alam, 2024). Machine learning algorithms are effective tools for analysing extensive datasets, providing valuable insights into employee behaviour and performance (Sandeep & Bhambu, 2023). This capability allows organisations to make data-driven decisions that enhance their workforce management strategies and improve overall organisational effectiveness (Andersen, 2017; Buttner & Tullar, 2018; Levenson, 2018; Simon & Ferreiro, 2018). This analysis not only highlights areas for improvement but also facilitates ongoing development within the organisation (Khan et al., 2024). This information aids hiring managers in identifying top talent and helps in developing strategies to boost employee morale, retention, and engagement (Sharma et al., 2022). For example, Google's implementation of HRA through "Project Oxygen" successfully pinpointed essential behaviours of effective managers, resulting in enhanced management practices and increased employee satisfaction (Dubey, 2023). Thus, organisations that integrate HRA can improve workplace culture, enhance morale, and boost employee retention, ultimately fostering a more rewarding and productive environment for their workforce (Srivastava, 2025). IBM utilised predictive analytics in its talent management strategies, which significantly decreased employee turnover by identifying individuals at risk of leaving and applying targeted retention measures (Dubey, 2023). Additionally, it provides insights into the effectiveness of various HR practices, highlighting areas that are successful and those that require modification (Krishna et al., 2022).
Competitive edge
Dubey (2023) states that organisations embrace new working methods and diverse skill sets to leverage emerging technologies and AI in pursuit of their strategic goals. As a result, organisations have shifted from relying exclusively on office information to leveraging individual data for optimal advantage. This transition allows them to harness insights that enhance decision-making and improve overall efficiency (Alam, 2024). The use of HRA plays a crucial role in informing key decisions related to human capital and business operations, thereby providing a significant competitive edge (Dubey, 2023). A significant majority of business leaders, specifically 94%, believe that HRA enhances the HR profession (SHRM, 2021). Additionally, 71% of HR executives utilising these analytics consider them crucial to their organisation's HR strategy (SHRM, 2021). Srivastava (2025) elucidates that HRA empowers organisations to attract, retain, and nurture top talent through data-driven insights. HRA can help organisations to proactively detect workforce trends and challenges, refine their recruitment and talent management approaches, and improve both organisational performance and employee satisfaction (Okatta et al., 2024). This approach fosters innovation, enhances workforce efficiency and ensures that HR strategies are aligned with overarching business objectives resulting in organisations gaining a competitive advantage in the marketplace (Srivastava, 2025). Krishna et al. (2022) therefore, argue that HR analytical tools serve as a crucial alert system for incidents requiring immediate attention from top management. For instance, if the resignation rates for the current year surpass those of the previous year, it becomes imperative for management to investigate the underlying causes of employee turnover and develop strategies to address this issue effectively (Krishna et al., 2022).
Moreover, organisations can utilise analytics to evaluate the effectiveness of HR initiatives, such as training programs, by examining employee performance metrics. This analysis helps leaders identify successful programs and those that require enhancement (Alam, 2024). Sharma et al. (2022) note that a clear understanding of the desired employee profiles enables organisations to better predict performance and productivity, enhancing overall corporate outcomes. Utilising data analytics in recruitment allows organisations to move beyond intuition, fostering environments and policies that motivate employees effectively (Sharma et al., 2022). Diez (2018) argues that organisations that will excel in the competitive landscape for talent will be those that effectively identify and retain essential personnel, inspire high performance, nurture employee development, and accurately forecast future workforce needs.
Time reduction
Managing employees in contemporary organisations demands more than traditional methods. The rise of technology has led to the development of HRA tools, which facilitate the online management and monitoring of employee performance (Dubey, 2023). Srivastava (2025) explains that HRA plays a crucial role in enhancing organisational efficiency by streamlining development processes, optimising reclamation strategies, and significantly reducing training expenses. HRA streamlines tasks such as data entry, calculations, and verification, enabling employees to work more efficiently. When workers feel they have control over their time, their performance tends to improve. Additionally, a data-driven approach encourages employees to adopt a more inquisitive and analytical mindset. HR professionals can identify areas where development efforts may be lagging, allowing for targeted interventions that foster employee growth and productivity. Furthermore, analytics can illuminate the reclamation processes, enabling organisations to refine their approaches to talent management and retention, thereby ensuring that valuable skills and knowledge are not lost. According to Katfi et al. (2023), HRA provides a range of benefits, including the optimisation of recruitment processes, a decrease in employee turnover, improved productivity, effective talent management, and strategic workforce planning. HRA helps organisations to enhance their understanding of the workforce, recognise emerging trends and challenges, and implement proactive strategies to refine their HRM practices (Katfi et al., 2023). Chowdhury et al. (2023) comment that HRA uses AI, machine learning, and cognitive computing to empower HR professionals in addressing complex HR challenges and enhanced decision-making in a time record by leveraging extensive data analysis (Chowdhury et al., 2023). HRA facilitates the gathering, examination, and interpretation of data concerning employees, their performance, satisfaction levels, and various other HR elements timely. The main aim of HRA is to improve decision-making by supplying managers and business leaders with accurate and reliable information (Katfi et al., 2023). Big data serves as a prime example of how organisations can enhance internal employee efficiency and achieve targeted marketing goals, ultimately leading to improved decision-making processes (Sharma et al., 2022).
Challenges faced by HR practitioners
Tuli et al. (2018) revealed that data-driven decision-making poses several challenges for organisations as shown in Figure 6.
Data quality, integrity and accuracy
One significant issue is the quality and integrity of data; as organisations utilise various digital tools such as HRIS, performance management systems, and employee surveys, they generate vast amounts of data. Data quality and integration are essential components for successful HRA (Jandaly & Khojah, 2024). They may appear similar, leading to potential confusion in their usage as displayed in Table 1.
Table 1 shows that data integrity focuses on maintaining the quality and consistency of data throughout its lifecycle. In contrast, data quality encompasses the attributes of correctness, completeness, consistency, and reliability of the data itself (Novogroder, 2024). Data quality encompasses various characteristics, such as accuracy and timeliness, that define the nature of data (Fu et al., 2024). The authors explain that each of these characteristics can be referred to as a variable, factor, or, more commonly, a dimension. Diván (2017) posits that when decision-making relies on data, the quality of that data plays a crucial role in shaping the outcomes of the process; hence, it is essential to monitor every phase of the data life cycle. This entails implementing organisational practices that oversee data acquisition, processing, analysis, preservation, and eventual reuse or deletion. Such vigilance ensures that decisions are informed by reliable and accurate data, thereby enhancing the overall effectiveness of the decision-making process (Diván, 2017). Fu et al. (2024) state that in a broader context, data quality is also expected to reflect how well each dimension meets the specific needs of data users within a particular setting. Jandaly and Khojah (2024) stress that organisations need to prioritise the accuracy, completeness, and consistency of their data to produce trustworthy insights. Insufficient data quality can significantly hinder the overall effectiveness of data utilisation (Fu et al., 2024). Dubey (2023) underscores that data quality and integrity challenges can significantly impede the effectiveness of HRA.
In the past, organisations relied solely on data from their systems, but now they leverage a vast array of big data sources, including internet and IoT data, which encompass unstructured, semi-structured, and structured data, with unstructured data alone accounting for over 80% of the total data available (Cai & Zhu, 2015). Organisations today are challenged with the task of consolidating data from diverse HR systems and sources to achieve a holistic understanding of their workforce, which facilitates more precise analysis and forecasting (Jandaly & Khojah, 2024). Dubey (2023) confirms that organisations frequently grapple with a variety of disparate data sources, complicating the process of consolidating and analysing information efficiently. Additionally, Pandey and Pandey (2020) argue that HR data is frequently dispersed across various systems, resulting in discrepancies and inaccuracies. This fragmentation can hinder effective decision-making and undermine the reliability of HR processes (Srivastava, 2025). Moreover, Khaliq and Saritha (2023) add that HR data is frequently dispersed across various systems, including HRIS, performance management tools, and recruitment platforms, leading to the creation of data silos and inconsistencies. Hence, the challenge of obtaining highquality data on time is exacerbated by the significant volume of unstructured data in big data, which requires extensive transformation into structured formats before further processing can occur (Cai & Zhu, 2015).
HRA serves as both a tool and a process aimed at generating valuable insights. It operates under the principle that the quality of input data directly influences the output; thus, subpar data, even when analysed with sophisticated techniques, yields minimal benefits. The rapid evolution of big data means that organisations must collect and analyse information in real time; otherwise, they risk relying on outdated data, which can lead to erroneous conclusions and poor decision-making by governments and organisations (Cai & Zhu, 2015). This underscores the importance of ensuring data integrity to maximise the effectiveness of analytics in HR (Sandeep & Bhambu, 2023). Moreover, the presence of fragmented, outdated, or inaccurate information complicates the task of ensuring data accuracy, completeness, and consistency. Fu et al. (2024) compliment that even when data is deemed "accurate" due to meticulous record-keeping or the asset's unchanged state, the inherent uncertainty surrounding its accuracy necessitates validation by on-site personnel before it can be deemed reliable.
Data privacy and security
In recent years, numerous large corporations worldwide have increasingly embraced HRA. However, a significant 81% of HRA projects face challenges due to ethical, privacy, and data protection issues (Wijesingha & Wickremeratne, 2020). This trend has raised significant concerns regarding the protection of employee privacy, as the analysis often involves sensitive personal information (Calza, Pagliuca, Risitano & Sorrentino, 2020). Data protection involves safeguarding critical information from corruption, compromise, or loss. This is particularly relevant when it comes to sensitive employee data, such as details regarding race, ethnicity, sexual orientation, personal relationships, and political beliefs, which individuals may be reluctant to share with third parties. Likewise, Khaliq and Saritha (2023) underline that HR data frequently includes sensitive personal information about employees, such as performance evaluations, salary information, and feedback from employee surveys. As a result, the need to safeguard this data and prevent unauthorised access has become a critical issue for organisations implementing these analytical practices (Jha, 2022). Data security is a critical concern in HR technologies due to the vast amounts of sensitive employee information they manage, including social security numbers, banking details, and health records (Lodha, Deshpande, Kakade, Sundaram & Deshmukh, 2024). It is crucial to prioritise data security, particularly when utilising cloud-based or centralised information systems (Wijesingha & Wickremeratne, 2020). When HR gathers data on individuals, particularly from external sources, it is crucial to prioritise privacy considerations. The collection of sensitive information, such as personal health records or details regarding sexual orientation, can place HR in a precarious legal position concerning protected characteristics (Krishna et al., 2022).
The balance between necessary data collection and employee privacy is a critical concern that HR must navigate carefully (Wijesingha & Wickremeratne, 2020). Similarly, Schein (2017) states that the collection and utilisation of employee data bring forth significant concerns regarding privacy and the potential for ethical violations. This makes HR systems attractive targets for cybercriminals, as data breaches can lead to serious consequences such as identity theft, financial fraud, and damage to the reputation of both employees and organisations (Lodha et al., 2024). Wijesingha and Wickremeratne (2020) assert that despite the implementation of precautionary measures by organisations, incidents of data breaches continue to occur. The authors explain that when such breaches happen, organisations may face substantial fines imposed by regulatory bodies, resulting in significant financial repercussions. Cyber-physical systems are characterised by a significant lack of built-in security features, which heightens concerns about data security and privacy (Lodha et al., 2024). This shortcoming can result in unauthorised access to services and sensitive information, exposing critical components of enterprises to various threats. Additionally, these incidents can erode the trust and credibility of the organisation among its stakeholders, potentially damaging its employer brand and overall reputation (Xuereb, Grima, Bezzina, Farrugia & Marano, 2019). Additionally, concerns regarding information security and data management further complicate the adoption of these analytical tools (Kaur & Dhawan, 2021). Therefore, HR, as crucial change agents, are primarily tasked with overseeing communication, privacy, and security issues that arise between employees and mediated systems. They also play a vital role in disseminating best practices aimed at improving organisational communication, privacy, and security (Strohmeier, 2020). Organisations are required to handle data with stringent privacy protocols and adhere to both legal and ethical standards (Khaliq & Saritha, 2023). In addition, the presence of privacy regulations across different countries, such as the Fair Credit Reporting Act in the United States, adds complexity to HR data management. This necessitates that organisations carefully navigate these legal frameworks to ensure compliance and protect sensitive information (Krishna et al., 2022). Such complexity may lead to vulnerabilities that may lead to the compromise of sensitive data, increased risk of denial-of-service attacks, backdoor exploits, and malware incidents, ultimately jeopardising essential HR infrastructure (Lodha et al., 2024).
Lack of data literacy and skills
A major obstacle faced by HR professionals is their limited analytical skills. Relatedly, Kaur and Dhawan (2021) noted that a significant barrier is the lack of necessary expertise and understanding required to effectively utilise HRA technology. Many HR professionals do not possess the necessary technical skills to effectively analyse and interpret data (Kavanagh & Thite, 2018). Likewise, Dubey (2023) found that many individuals in this field lack training in data analysis, which can hinder their ability to effectively interpret complex data sets. The availability of HR professionals equipped with essential analytical skills and competencies is a vital element in organisational success (Jandaly & Khojah, 2024; Eze, Gleasure & Heavin, 2021). Khaliq and Saritha (2023) emphasise that the implementation of HRA necessitates a workforce adept in data analysis, statistical modelling, and data visualisation. HR professionals must possess advanced skills in data analytics, statistical modelling, and technology to effectively navigate digitalisation, which can be developed through targeted training programs. However, organisations often encounter difficulties in attracting, hiring and keeping a talent pool equipped with the requisite analytical skills within the HR department. Moreover, Krishna et al. (2022) is of the view that despite data scientists leading the charge in the data and analytics revolution, there exists a prevalent misconception among hiring managers and HR professionals that HRD play a minimal role in data analysis. This misunderstanding often leads to reluctance in embracing and utilising analytical tools, primarily due to the perceived complexities and the daunting nature of the learning curve (Krishna et al., 2022). Furthermore, Wijesingha and Wickremeratne (2020) assert that HR are essential in managing data security; however, the inherent curiosity or lack of skills of HR professionals can sometimes lead to the misuse of sensitive information. This situation can create a sense of unease among employees, particularly when they are asked to share personal details such as family information, previous employment history, and other private data (Wijesingha & Wickremeratne, 2020). Consequently, to effectively utilise HRA, HR professionals must enhance their existing skills or acquire new competencies (Khaliq and Saritha, 2023).
Lack/inadequate technological infrastructure
The successful execution of data-driven decision-making relies on a solid technological infrastructure, including hardware, software, and data management systems, which may be financially out of reach for many organisations. Technology infrastructure and tools play a crucial role in the successful implementation of HRA (Lim, Kim, Kim, Kim & Maglio, 2018). Krishna et al. (2022) posit that data analysis often demands significant IT resources, which can be a challenge for smaller businesses that may not have the necessary infrastructure to support platforms like Hadoop and other analytics tools. Organisations are encouraged to invest in HR information systems, analytics platforms, and data visualisation tools to enhance the processes of data collection, analysis, and reporting. By utilising user-friendly interfaces and intuitive tools, HR professionals can effectively explore and interpret data, thereby enabling them to make well-informed decisions (Jandaly & Khojah, 2024). Krishna et al. (2022) observed that for small and medium-sized businesses looking to conduct their own data analysis, public cloud services can provide a viable solution. However, opting for a Software as a Service (SaaS) offering from a provider experienced in unstructured data analysis may be a more advantageous choice for those lacking the time, technology, or in-house expertise to manage these processes effectively (Krishna et al., 2022). Sharma et al. (2022) add that R-Studio is a powerful tool for statistical analysis and data exploration, providing precise insights from large datasets. While Python is often favoured for its ease of learning, Excel remains the most accessible option for HR professionals, and Power BI facilitates data access from various sources, whereas SPSS, though requiring some statistical knowledge, offers straightforward data examination capabilities (Sharma et al., 2022).
Conclusion
Harnessing the capabilities of human resource analytics can significantly improve decision-making processes within organisations, presenting both opportunities and challenges. The integration of data-driven insights into HR practices allows for more informed choices regarding talent acquisition, employee engagement, and performance management. However, the literature highlights ongoing debates surrounding the ethical implications of data usage, privacy concerns, and the potential for bias in algorithmic decision-making. Recent studies emphasise the need for a balanced approach that not only leverages analytics for strategic advantage but also addresses these ethical dilemmas. As organisations continue to navigate this complex landscape, the discourse around best practices and the responsible use of HR analytics remains a critical area of focus, underscoring the importance of fostering a culture of transparency and accountability in data utilisation.
Implications
The adoption of HRA offers organisations significant benefits, such as enhanced decision-making capabilities, improved alignment with overarching strategic objectives, a more positive employee experience, a heightened competitive advantage, and a reduction in time spent on various processes. These advantages stem from the ability to leverage data to inform HR strategies and initiatives, ultimately leading to more effective workforce management. However, the transition to data-driven decision-making is not without its challenges. Organisations may encounter obstacles such as data quality issues, the need for advanced analytical skills among HR professionals, and infrastructure limitations. The implications of these findings underscore the necessity for organisations to invest in both technology and training to fully harness the power of HRA. Furthermore, HRM must evolve to integrate these analytical insights into everyday practices, ensuring that data informs not only strategic planning but also operational execution. This dual investment on technology and human capital development can help organisations thrive in an increasingly competitive landscape.
Recommendations
To successfully implement data-driven decision-making within an organisation, the study advocates fostering cross-functional collaboration. This collaboration should span various departments, particularly between HR and Information Technology (IT). These departments can work together to ensure that HR data is accessible and effectively utilised. HR practitioners must engage with IT to streamline data management processes. This synergy will enable HR to leverage predictive insights, ultimately enhancing HRM practices and aligning them with broader business objectives.
In addition to interdepartmental collaboration, involving managers and staff in the implementation of data-driven strategies is crucial for fostering a culture of acceptance and enthusiasm towards data utilisation. When employees at all levels are included in the process, they are more likely to feel a sense of ownership and commitment to the changes being made. This can be achieved through workshops, training sessions, and open forums where employees can voice their concerns and suggestions. Investing in training and development initiatives to strengthen the analytical capabilities of HR professionals is crucial for the effective application of HRA. Thus, the cultivation of a data-driven mindset can be done through the promotion of a culture of ongoing learning among HR practitioners, which can lead to successful implementation (Jandaly & Khojah, 2024). This staff engagement can lead a more datacentric mindset that encourages innovative thinking and problem-solving within the organisation.
To address the challenges related to data dispersion and fragmentation, organisations must prioritise investments in data integration initiatives that will enable them to consolidate and standardise information from diverse sources (Khaliq & Saritha, 2023). Furthermore, when integrating diverse data from multiple sources, establishing a framework to evaluate the quality of the resulting dataset is essential (Fu et al., 2024).
Privacy concerns and ethical considerations present further obstacles, as it is crucial for organisations to handle employee data responsibly and transparently (Dubey, 2023). Moreover, the use of "poor data" in data-centric applications can result in severe consequences. Therefore, it is essential for decision-makers, particularly those engaged in data-driven processes, to implement controls within their workflows. These controls should focus on ensuring that data adheres to established quality standards from the moment it is generated and collected, assisting staff in choosing the most appropriate tools for engaging with available data resources, and fostering a comprehensive understanding of the changing value of data over time, as both the data and the industry evolve (Fu et al., 2024).
To mitigate the data security risk, HR technology providers need to implement strong security protocols, such as encryption, firewalls, and intrusion detection systems (Lodha et al., 2024). Regular security audits and penetration testing are essential for uncovering vulnerabilities and preventing unauthorised access. Additionally, training employees on cybersecurity best practices is crucial for reducing the chances of data breaches. Organisations can safeguard their sensitive data by promoting ethical behaviour among employees, implementing a robust data security policy, or adhering to the regulations set forth by local and international data protection authorities (Wijesingha & Wickremeratne, 2020). Furthermore, organisations may implement awareness sessions, training programs, audits, and various risk assessment matrices to ensure compliance with established standards.
Furthermore, organisations should prioritise the development of operational infrastructure that supports data accessibility and analysis. This includes investing in user-friendly data management systems and analytical tools that empower HR professionals to extract meaningful insights from data. Regular training and upskilling opportunities should be provided to ensure that all employees are equipped with the necessary competencies to interpret and utilise data effectively.
Acknowledgement
Author Contributions: The author is the sole author.
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.
Conflicts of Interest: The authors declare no conflict of interest.
Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable groups or sensitive issues.
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