In today's world managing employees in organization is not a one-man task. With the evolving business and advancement in technologies managing employees and tracking their performance can be performed online with the help of HR analytic tools. The use of HR analytics has improved employee performance and increased efficiency in business like, improvement of quality of recruitment, talent management, employee productivity and decreasing employee turnover. In this paper we are going to study about HR analytics, its tools, and its application in different organizations. In this paper we study various use of HR Analytics in different organisations and the benefits of the use of HR Analytics. With the help of analytical tools the organisations can recognise the issues like performance, employee turnover and retention employee behaviour, etc by using the data available with the organisation. This research is conducted because of the lack of use of HR in many organisations. The use of HR is undermined in many organisations but in this modern technological world various analytical tools have been developed which are used by huge corporations.

Performance  Management  is  an  important  aspect  in  Human  Resources  as  it  is  a  continuous communication process between managers and employees to achieve organizational goals as well as develop personnel skills of employees. This entire communication  process  involves defining  clear specific  expectations,  establishing  goals,  providing  continuous  feedback  and  examining  results. Performance Management builds a communication system between a manager and employee that is built throughout the year in hope of accomplishing  organizational  as  well as  individual  goals. To understand employee managers, go through all the collected data and addresses the performance gaps through the given data. Various tools are used to gather such data like HR Analytics. 

HR Analytic is the collection and application of talent data to improve critical talent. It is basically used for  decision  making  using  the  available  data,  to  predict  employee  turnover  and  identify  better performers or predict skills that need to be Improved. HR Analytics is also known as people analytics. It enables your organization to measure the impact of HR metrics on overall business performances and make decision based on the data. 

Human resource management is focused on the effective use of people to achieve organizational and personal goals. It basically focuses on recruiting, managing, exit related functions in the organization. To keep employees fuelled and to keep the productivity rising human resource’s evaluate employee performance and develop new training programs for them human resource came into light as a specific field in the early 20th century, inspired by frederick winslow taylor (1856 to 1915) john R. Commons an american institutional economist first used the term ‘human resource’ in his book ‘The Distribution Of Wealth’ that was published in 1893. However, it was not until the 20th century that human resource departments were formerly developed to manage the relationships between employers and employees.

Human resource analytic is the collection and application of talent data to improve critical talent and used for decision making using the available data to predict employee turnover and identify better performers or predict skills that need to be improved. Human resource analytics is also known as people analytics. It enables your organization to measure the impact of human resource metrics on overall business performances and make decision based on the data.

Human resource analytics can be defined as to understand relationship between performance of organization and human resource practices. In case of effective human resource practices it leads to employee satisfaction and provides strong foundation where decisions regarding human capital and business strategy can be performed. Analytics enabled organization bring precision in decision making. It is possible with the use of statistical techniques and experimental approach. Human resource analytics tools comprise software that enables human resources professionals to gather, evaluate crucial metrics related to the performance and behaviour of personnel. These tools evaluate the influence of the human resource department on company performance by combining business data with data related to personnel. Companies may use human resource analytics software to identify inefficiencies forecast productivity and improve staff management processes.

The concept and application of data and analytics in management have seen increasing attention as researchers and professionals aim to understand how data can be transformed into actionable insights leading to improved organizational performance (Chierici et al., 2019; Ferraris et al., 2019; Santoro et al., 2019; Singh and Del Giudice, 2019). Consequently, this interest has transcended various management disciplines, including human resources management (HRM), which is evidenced by the growing number of HR departments implementing HR analytics to improve decision-making (Marler and Boudreau, 2017; Fernandez and Gallardo-Gallardo, 2020; McCartney et al., 2020). Despite its increased popularity, HR analytics is not an entirely new concept (Huselid, 2018). Rather, HR analytics has emerged from previous research on the impact of HR practices such as selection, training and performance management, which has a long history in social sciences, including industrial and organizational psychology, HRM and organizational behaviour [1]. What is new, however, is that HR analytics in contemporary organizations has shifted from “assessing the levels associated with a particular workforce attribute (e.g. what is our cost per hire?) to understanding the impact of the workforce on the execution of firm strategy (e.g. how might an increase in the quality of our project managers affect our new product cycle time?)” (Huselid, 2018, p. 680). In other words, HR analytics not only centers on investigating and improving elements of human capital but also applying analytical techniques coupled with people data to inform organizational strategy and improve performance.

Furthermore, the significant growth of access to HR technology, including human resource information systems (HRISs), cloud platforms and apps, has offered HR departments the ability to collect, manage and analyze large volumes of employee data, compared to earlier legacy IT systems (Bondarouk and Brewster, 2016; Marler and Boudreau, 2017; Kim et al., 2021). Such shift has also acted as a driver of HR analytics and increased its adoption within HR departments. For example, through the use of advanced HR technology to gather and analyze candidate and employee data, Google's HR analytics team has developed an evidence-based approach to improve its recruitment and selection process by identifying several elements of high performance that could predict a candidate's likelihood of success (Harris et al., 2011; Shrivastava et al., 2018). Similarly, in addition to recruitment and selection, HR analytics offers organizations the ability to address various other HR challenges, including employee engagement, diversity and inclusion, and turnover (Harris et al., 2011; Andersen, 2017; Buttner and Tullar, 2018; Levenson, 2018; Simón and Ferreiro, 2018).

To date, the extant HR analytics literature has focused on many areas, including the current limitations and challenges facing the development of HR analytics (Boudreau and Cascio, 2017; Levenson and Fink, 2017; Huselid, 2018; Minbaeva, 2018; Jeske and Calvard, 2020), best practices in developing and utilizing HR analytics (Green, 2017; Falletta and Combs, 2020), and the impact and importance of analytical skills (Kryscynski et al., 2018; McCartney et al., 2020). In addition, several reviews have been published offering a holistic view of the current state of HR analytics research (Marler and Boudreau, 2017; Tursunbayeva et al., 2018; Fernandez and Gallardo-Gallardo, 2020; Margherita, 2020). Despite the advancement of HR analytics literature and the number of case studies claiming that HR analytics allows organizations to improve their performance (Marler and Boudreau, 2017; Fernandez and Gallardo-Gallardo, 2020; Margherita, 2020), research investigating how and to what extent HR analytics impacts and influences organizational performance remains scarce (Huselid, 2018; Minbaeva, 2018). On this basis, this study seeks to understand how and why HR analytics influences organizational performance by theorizing and testing its underlying mechanisms.

This study draws on evidence-based management theory (EBM, Rousseau and Barends, 2011; Baba and HakemZadeh, 2012; Bezzina et al., 2017), the resource-based view of the firm (RBV, Barney, 1991) and dynamic capabilities (Teece et al., 1997; Winter, 2003) as the underlying frameworks linking access to HR technology, HR analytics, EBM and organizational performance. These theoretical frameworks are justified as EBM is concerned with incorporating and deploying scientific and organizational facts coupled with expert and stakeholder judgment to make managerial decisions (Rousseau and Barends, 2011; Baba and HakemZadeh, 2012). At the same time, HR analytics contributes to organizational evidence creation through acquiring and translating high-quality workforce data into information, resulting in critical organizational insights (Marler and Boudreau, 2017; Minbaeva, 2018; Coron, 2021). Further, in line with previous studies exploring the performance impact of HR (Delaney and Huselid, 1996; Guthrie, 2001; Fu et al., 2017), this study integrates an RBV (Barney, 1991) and dynamic capability (Teece et al., 1997) perspective to propose a chain model demonstrating that access to HR technology enables HR analytics (resource) which facilitates EBM (capability) which in turn enhances organizational performance.

By theorizing the chain model between access to HR technology, HR analytics, EBM and organizational performance, this study extends our understanding of why and how HR analytics leads to higher organizational performance. Additionally, this study addresses the conditional effect of HR technology as an antecedent of HR analytics. Finally, the study adds empirical evidence linking EBM to organizational performance, which at present is rare (Baba and HakemZadeh, 2012). Together, these contributions offer a solid foundation for the strategic importance of HR analytics and EBM.

This paper's subsequent sections are structured as follows: First, the literature review and hypotheses section will summarize existing research in HR analytics, outline the five hypotheses tested within the paper and present the theoretical model. Second, the research methodology will describe the data collection process and offer a detailed explanation of the survey measures. Third, the research findings are presented, providing analysis and support for each of the hypotheses tested. Lastly, the paper's theoretical contributions to HR analytics and EBM are presented, implications for practice, limitations and areas for future research are discussed.

HR Analytics: Definition and Development

As a result of the ongoing digital transformation, many HR departments have begun to engage with workforce data to make data-driven decisions in areas such as recruitment and selection, performance measurement, diversity and inclusion and workforce planning (Harris et al., 2011; Kane, 2015; Rasmussen and Ulrich, 2015; Marler and Boudreau, 2017; Hamilton and Sodeman, 2020; Tursunbayeva et al., 2021). This application of using workforce data to improve decision-making has been synonymously referred to by scholars as HR analytics (Aral et al., 2012; Rasmussen and Ulrich, 2015; Angrave et al., 2016; Marler and Boudreau, 2017; McCartney et al., 2020), people analytics (Kane, 2015; Green, 2017; Nielsen and McCullough, 2018; Tursunbayeva et al., 2018; Peeters et al., 2020), talent analytics (Harris et al., 2011; Sivathanu and Pillai, 2020), human capital analytics (Andersen, 2017; Boudreau and Cascio, 2017; Levenson and Fink, 2017; Minbaeva, 2018) and workforce analytics (Huselid, 2018; Simón and Ferreiro, 2018).

Regardless of the term used, consistency exists in both academia and practice for the strategic importance of HR analytics as it provides organizations with data, information and insights to effectively make informed data-driven decisions (Huselid, 2018; Minbaeva, 2018). For example, according to van den Heuvel and Bondarouk (2017), HR analytics is the systematic identification and quantification of the people drivers of business outcomes to make better decisions. Equally important is the notion that these insights can be generated at varying levels of technological sophistication (Margherita, 2020; Sivathanu and Pillai, 2020). For example, according to Margherita (2020), HR analytics follows a linear three-stage maturity model. At its lowest level, “descriptive,” HR analytics focuses on using HR technology to generate reports and dashboards to answer questions concerning what has happened. Next, the “predictive” stage utilizes statistical techniques, advanced algorithms and machine learning to anticipate what might happen in the future and why. Lastly, the “prescriptive” stage centers on determining the optimal action that should be taken in response to the analysis.

This study adopts the HR analytics definition proposed by Marler and Boudreau (2017), where HR analytics is “an HR practice enabled by information technology that uses descriptive, visual, and statistical analyses of data related to HR processes, human capital, organizational performance, and external economic benchmarks to establish business impact and enable data-driven decision-making” (p. 15). In light of this definition, this paper operationalizes HR analytics through the adoption of the human capital analytics framework (Minbaeva, 2018), where HR analytics comprises of three dimensions: high-quality data, analytical competence and strategic ability to act.

According to the human capital analytics framework, the dimension of high-quality data suggests that the data used for analytics needs to be accurate, consistent, timely and complete. For instance, organizations need to ensure that the data being used to conduct HR analytics is accurate. Without accurate data, the insights gained from the analytics will be unreliable and offer no benefit to the organization (Minbaeva, 2018; Wamba et al., 2019; Peeters et al., 2020). Alternatively, inaccurate data could lead to implementing solutions that do not address the business's actual challenges. HR analytics also requires a high degree of analytical competence, referring to the analytics team's ability to apply statistical analysis and techniques to workforce data to transform data into valuable insights (McCartney et al., 2020). For example, the analytics team needs to frame relevant research questions and answer them through developing causal models and performing sophisticated statistical analysis (Minbaeva, 2018). Moreover, the team needs to translate the insights gained into a compelling analytics narrative or story (Andersen, 2017; Minbaeva, 2018; McCartney et al., 2020). Lastly, the strategic ability to act refers to having the required managerial support to make decisions and implement solutions based on the data, information and insight gathered from HR analytics.

Furthermore, we regard HR analytics as a valuable, rare, inimitable and non-substitutable resource for organizations based on the data, information and insights generated by HR analytics. This argument is justified by the many parallels that can be drawn and have been indirectly implied by scholars when it comes to HR analytics as an organizational resource (Marler and Boudreau, 2017). For example, researchers and practitioners have discussed the value offered by HR analytics through its ability to allow HR to identify and address workforce challenges (Marler and Boudreau, 2017; Huselid, 2018; Kryscynski et al., 2018; McIver et al., 2018; Minbaeva, 2018). In addition, the HR analytics literature has also referred to the rarity of high-quality HR analytics programs suggesting that many organizations struggle to utilize workforce data only offering basic reporting and descriptive statistics (Angrave et al., 2016; King, 2016; Andersen, 2017; Green, 2017; Levenson and Fink, 2017; Minbaeva, 2018). As such, effective HR analytics programs are rare at present. Concerning the imitability of HR analytics, according to Minbaeva (2018), to utilize and conduct value-adding HR analytics, organizations need to have high-quality data, analytical capabilities and the strategic ability to act. However, it is difficult for HR departments to have all three elements given the low levels of technology, poor data quality, few resources, lack of analytical competencies and a lack of buy-in from senior management (Andersen, 2017). Finally, HR analytics is its own stand-alone practice, meaning no available alternatives or substitutes can gain similar insights (Falletta and Combs, 2020). Taken collectively, HR analytics meets the requirements set out by RBV, suggesting that HR analytics and the data, information and insight it creates, is a valuable resource for organizations with the potential to generate competitive advantage.

Linking HR analytics to EBM

The data, information and insights generated from HR analytics are not enough to generate competitive advantage alone. Instead, organizations must also deploy and incorporate the evidence effectively (Sirmon et al., 2007; Lin and Wu, 2014; Fu et al., 2017). This idea is consistent with dynamic capabilities, which suggests that competitive advantage depends on an organization's capacity to successfully incorporate, develop and reconfigure its resources (Teece et al., 1997). Similarly, according to Baba and HakemZadeh (2012), EBM is a dynamic process where evidence is first gathered and then interpreted, forming the foundation of managerial decision-making. Accordingly, this study adopts EBM theory to argue that the evidence and organizational facts generated by HR analytics can be used to make strategic decisions and facilitate EBM.

The idea of making decisions based on several sources of information, including organizational facts such as analytics, is a foundational element in evidence-based practice (Walshe and Rundall, 2001; Briggs and McBeath, 2009; Rousseau and Barends, 2011; Baba and HakemZadeh, 2012; Coron, 2021). This decision-making methodology originated within the healthcare profession to better use scientific research to inform decision-making concerning patient care (Walshe and Rundall, 2001; Pfeffer and Sutton, 2006; Briggs and McBeath, 2009; Baba and HakemZadeh, 2012; HakemZadeh and Baba, 2016). More recently, this approach to decision-making has been advocated for by various scholars suggesting that management decisions should be based on the combination of critical thinking coupled with the best sources of evidence (Rousseau, 2006). These sources of information include scientific evidence found in peer-reviewed academic papers, organizational facts such as metrics and analytics, professional experience and judgment, and considering the outcome on affected stakeholders (Rousseau, 2006; Rousseau and Barends, 2011; Baba and HakemZadeh, 2012; Bezzina et al., 2017; Cassar and Bezzina, 2017). Moreover, according to Barends et al. (2014), EBM comprises of six activities, including asking, acquiring, appraising, aggregating, applying and assessing. For example, organizations must translate an issue or problem into an answerable question (asking), systematically search for and retrieve the best available evidence (acquiring), critically judge the trustworthiness and relevance of the evidence (appraising), weigh and pull together the evidence (aggregating), incorporate the evidence into the decision-making process (applying) and evaluate the outcome of the decision (assessing).

As indicated previously, HR analytics generates evidence through organizational facts allowing managers and senior leaders to make more informed decisions (Kapoor and Sherif, 2012; Ulrich and Dulebohn, 2015; Marler and Boudreau, 2017; Levenson, 2018; McIver et al., 2018; Shrivastava et al., 2018). For example, according to Coron (2021), evidence-based human resource management relies on using people data and metrics to increase knowledge and, in turn, improve HR decision-making. Similarly, according to van der Togt and Rasmussen (2017), it is the individual experience, beliefs, intuition and facts acquired through HR analytics that serves as another source of evidence HR professionals can use to enhance decision-making capabilities and better organizational results. Accordingly, we argue that HR analytics contributes to evidence creation by generating organizational facts from workforce data and anticipate a positive relationship between HR analytics and EBM.

HR analytics is positively associated with organizational EBM

Linking EBM to organizational performance

Every day, managers and senior executives are faced with making critical decisions to improve the success of their organizations. Although some decision-makers will utilize a wide range of evidence to support their decisions, many justify their decisions based on gut feeling, outdated information, personal experience or a combination of the three (Rousseau and Barends, 2011; Baba and HakemZadeh, 2012). As such, management scholars have urged for a shift in management decision-making, placing considerable emphasis on promoting EBM (Pfeffer and Sutton, 2006; Rousseau, 2006; Briner et al., 2009; Morrell and Learmonth, 2015; Rynes and Bartunek, 2017). This comes as a result of significant developments being made in healthcare toward the performance impact of EBM; specifically, the literature centered on healthcare quality and patient and hospital outcomes (Melnyk et al., 2014; Aloini et al., 2018; Janati et al., 2018; Roshanghalb et al., 2018).

In a review conducted by Roshanghalb et al. (2018), they identify 20 empirical studies that demonstrate the effect of EBM on various patient outcomes. For example, in a study conducted by Grundtvig et al. (2011), two sources of evidence (patient data and expert experience) were used in making medical decisions surrounding patients with chronic heart failure. As a result, hospitalization rates and the number of days spent in hospital were significantly reduced (Grundtvig et al., 2011). Based on this review and evidence from the healthcare literature supporting that EBM enables better decision-making leading to better performance outcomes,

Organizational EBM is positively associated with organizational performance

Building on this notion, HR analytics also generates evidence in the form of organizational facts, providing managers and executives with actionable insights which can be used as evidence in decision-making. Similarly, when organizations use and deploy the insights derived from HR analytics coupled with other sources of evidence to make decisions, it is likely to improve decision-making effectiveness, leading to higher organizational performance. This is evidenced in various case studies that have focused on how HR analytics facilitates evidence-based decision-making to improve HR and business performance (Harris et al., 2011; Rasmussen and Ulrich, 2015; Marler and Boudreau, 2017; Buttner and Tullar, 2018; Gelbard et al., 2018; McIver et al., 2018; Minbaeva, 2018). For example, a recent case study conducted by Simón and Ferreiro (2018) describes developing and implementing an HR analytics program at Inditex, a large Spanish multinational fashion retail group. In collaboration with the authors, Inditex developed key performance indicators centered around workforce analytics. Doing so led HR managers at Inditex to gain and apply critical evidence to make more informed decisions around their workforce, resulting in higher overall store performance (Simón and Ferreiro, 2018). Similarly, Bank of America, working in collaboration with Humanyze (an HR analytics software provider), used HR analytics to improve HR and business outcomes (Kane, 2015). To do so, Humanyze designed and developed ID badges for Bank of America employees, adding microphones, Bluetooth and infrared technology to facilitate workforce data collection (Kane, 2015). Their findings determined that how employees interacted with their coworkers was the most significant factor in predicting productivity (Kane, 2015). Based on this evidence, Bank of America implemented solutions to the working environment that led to increased team cohesion by 18%, a reduction in stress by 19% and a 23% increase in productivity (Kane, 2015).

As shown in the above examples, HR analytics “uses descriptive, visual, and statistical analyses of data related to HR processes, human capital, organizational performance, and external economic benchmarks to establish business impact and enable data-driven decision-making” (Marler and Boudreau, 2017, p. 15). As such, we propose a mediating role of organizational EBM in the HR analytics and organizational performance link. Therefore, we

Relationship between HR analytics and Organizational Performance

Access to HR technology enabling HR analytics

The rapid advancement of information technology has sparked a digital revolution, with organizations taking advantage of big data to address previously unknown opportunities (Wamba et al., 2015; Wamba et al., 2017; Dubey et al., 2019; Kim et al., 2021). This is no exception for HR, as departments have shifted toward a more technology-enabled HR department (Boudreau and Cascio, 2017; Marler and Boudreau, 2017; van den Heuvel and Bondarouk, 2017; McCartney et al., 2020).

Accordingly, access to HR technology such as HRISs and other forms of electronic HRM (e-HRM) platforms have been a driving force in the implementation and growth of HR analytics (Ashbaugh and Miranda, 2002; Dulebohn and Johnson, 2013; McIver et al., 2018; Schiemann et al., 2018; Kim et al., 2021; Zhou et al., 2021). For example, HRIS allows for capturing, storing, manipulating, retrieving and distributing HR data and are equipped with the functionality to generate reports on key performance indicators (KPIs) (Hendrickson, 2003; Stone et al., 2015). Furthermore, these systems can add more advanced analytics and reporting modules to predict short- and long-term workforce trends by incorporating big data, business intelligence and statistical applications (Kapoor and Sherif, 2012; Stone et al., 2015; van den Heuvel and Bondarouk, 2017; McIver et al., 2018; Garcia-Arroyo and Osca, 2019; Mikalef et al., 2019). More recently, advancements in HR technology platforms have led to integrating artificial intelligence (AI) solutions, including chatbots, for streamlining HR processes (Buck and Morrow, 2018; van Esch and Black, 2019; Black and van Esch, 2020). As can be seen, HR technology evolves along a continuum from basic data collecting and storage (i.e. HRIS) to more robust platforms with AI and analysis capabilities. Although it is essential to distinguish among these differences, this study does not focus on the sophistication level of HR technology. Instead, this study aims to understand whether HR departments that have access to HR technology at any point on the continuum can adequately leverage it to enable HR analytics. As such, this study defines access to HR technology as an HR department that invests in and implements an HR software tool that allows for the recording, storage and perform analysis of data surrounding an organizations human resources that can be used by members of the department (Aral et al., 2012; Stone et al., 2015; Marler and Boudreau, 2017; Kim et al., 2021; Maamari and Osta, 2021).

Building on that notion, this study argues that access to HR technology is a driving force in HR analytics adoption and enables and acts as an antecedent to HR analytics. The justification for this is two-fold. First, HR technology serves as the foundation for HR analytics as it allows HR professionals timely access to workforce data that can be used to make more informed and data-driven decisions (Lengnick-Hall and Mortiz, 2003; Johnson et al., 2016; King, 2016; McIver et al., 2018). For example, according to McIver et al. (2018), HR technology enables the collection, cleaning and manipulation of various data types from several data sources that can be used to aid organizational decision-making. Thus, meeting the first element of the HR analytics framework high-quality data (Minbaeva, 2018).

Second, HR technology facilitates the process of transforming workforce data into information, where executives, HR professionals and line managers can make strategic workforce decisions through its ability to conduct statistical and predictive analysis (Aral et al., 2012; Fernandez and Gallardo-Gallardo, 2020). According to van der Togt and Rasmussen (2017), insights derived from HR analytics are enabled by HR technology as they can perform sophisticated statistical analyses such as regression on longitudinal and cross-functional data. Moreover, HR technology allows HR professionals to aggregate and perform predictive analytics, which would otherwise not be possible without HR technology (Ulrich and Dulebohn, 2015). Current HR technology platforms also offer a wide range of functionality, allowing HR professionals to translate data into meaningful insights through their ability to generate dashboards, scorecards and data visualizations (Ulrich and Dulebohn, 2015; Angrave et al., 2016; Marler and Boudreau, 2017; McIver et al., 2018). For instance, according to Ulrich and Dulebohn (2015), dashboards and scorecards are descriptive analytics that HR professionals can utilize to compare and visualize various HR metrics over time. Similarly, McIver et al. (2018) suggest that dashboards offer HR professionals a way to efficiently illustrate workforce trends to help drive questions and take advantage of emerging workforce opportunities. Thus, meeting the two final elements of the HR analytics framework (analytical competence and strategic ability to act) (Minbaeva, 2018).

As can be seen, access to HR technology plays a critical role in offering HR professionals the ability to gather, analyze and visualize data, enabling senior management to make more informed decisions (Kapoor and Sherif, 2012; Ulrich and Dulebohn, 2015; Marler and Boudreau, 2017; McIver et al., 2018). Therefore, this study argues that access to HR technology will enable HR analytics by acting as a facilitator for transforming workforce data into organizational knowledge and insights.

In today’s business world managing employees in organization is not a one-man task. With evolving business advancement in technologies managing employees and tracking their performance can be performed online with the help of Human Resource analytic tools. The use of Human resource analytics has improved employee performance and increased efficiency in business life, improvement quality of recruitment talent management employee productivity and decreasing employee turnover. With the help of analytical tools the organization can recognize the issues like performance, employee turnover and retention employee behaviour etc, By using the data available with the organization. The use of human resource is undermined in many organizations but in this modern technological world various analytical tools have been developed which are used by huge corporation. In this paper we are going to see human resource analytics its tools and its application in different organization, such uses of human resource analytics in different organizations and how the use of human resource analytics helped the organization as well as employees in monetary ways and change the business strategy around people centric way.

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