Bridging the Gap: The Evolution of HR Practitioners into Data-Driven Analysts

There’s a rapid evolution taking place within the human resource management (HRM) landscape – a transformation from traditional HR roles, driven by changing work dynamics, advancements in technology, and shifts in employee expectations. HR is experiencing disruption and change on an unprecedented scale, based on the observed convergence of mega-trends (Harney & Collings, 2021). Some of these mega-trends observed in technological advancements and changes in employee expectations and working dynamics include: agile HR, HR disruption, HR co-creation, strategic human capital, global flexible working arrangements, and employee health and safety. In order to successfully navigate this changing landscape, HR professionals need to remain knowledgeable and adept with the latest trends and innovations. (Harney & Collings, 2021). In the modern dynamic business environment, competitiveness in the market place requires organizations to undertake constant internal reflections and audits with an aim of re-evaluating their current operational processes and procedures in order to achieve more effective and efficient alternatives to the management of their limited resources – a factor informed by the desire of improved productivity. As an organization’s most valuable asset, human capital is essential in stimulating organizational growth, sustained competitive edge, creativity, and innovations (Yu et al., 2022). This paper’s main objective is to analyze the skills gap between tradtional HR practitioners and the 21^stcentury analysts equipped with data analytic skills – the paper will also highlight the strategic value of this modern version of the HR practitioner (Strategic Human Resource Management).

Literature Review

The Traditional HR Practitioner

The roles and responsibilities of traditional HR practitioners is concerned with the management of people in the workplace, but to a comparatively lower or inferior scope, objectives and methods, compared to strategic human resource management that leverages technology through data analytics. The traditional HR focuses primarily on administrative tasks and routine activities associated with the basic conceptual framework of human resources – a factor that makes this role transactional and reactive, with a primary objectve of ensuring compliance with employment laws. This primarily involves issues revolving around personnel (recruitment and selection), welfare (compensation and benefits administration), and industrial relations (performance management). Traditonal HR management model makes it difficult for management to supervise and constrain employees – especially given the volatility of the modern business environment that’s plagued with unexpected events such as financial crises, natural disasters, trade embargoes, industrial accidents, and cyber or terrorist attacks (Yu et al., 2022).

The traditional HR practitioner tends to adopt a functional approach – administrative functions that consist of policy and procedure enforcement, record-keeping, and ensuring legal compliance – which results in the isolation of HR functions and tasks from other departments with limited integration (top-down and hierarchical decision-making processes). This alienation of HR functions from organizational strategy and goals creates a focus on immediate operational needs and the resolution of currennt HR issues – a short-term oriented approach that significantly undermines the optimization of organizational performance due to the general lack of long-term planning and forecasting (Harney & Collings, 2021). Another unique point of difference between tradtional HR practitioners and data-driven analysts (HR analytics) can be observed in their measurement of success, whereas HR analytics is focused on the impact of HR on organizational performance, traditional HR practitioners measure success in terms of the efficiency of HR processes – these are deternined by cost control, time taken to fill vacancies, and compliance with legal requirements (Singh & Sarkar, 2021).

The rise of data analytics in HR

In the 21^st century, modern management practices emphasizes the need for every aspect of the business or organization to link their strategies and practices with organizational strategy as a framework of realizing higher performance. Strategic human resource management is tasked with the primary objective of achieving the organization’s strategic goals – which requires the integration of strategies that prioritize and enhance organizational performance, long-term competitiveness (organizational resilience), and employee engagement (Yu et al., 2022). The incorporation of data analytics in human resource management indicates the potential for massive positive impact on business outcomes with a strong influence in operational and strategic decision-making (data-driven decision-making). HR analytics is characterized by the integration with data and IT infrastructure from across disciplines and organizational boundaries – with a capability of transcending individual disciplines such as marketing, finance, and HRM (van den Heuvel & Bondarouk, 2017).

The integration of data-driven analysts within HR processes results in a holistic and integrated approach whereby strategic HR decision-making acknowledges the existing interdependencies between HR functions and the need for cross-functional collaboration in improving organizational performance and achieving organizational objectives. Data-driven analysts posses a unique capability of integrating HR with business strategy (McCartney & Fu, 2022). Through HR analytics, data-driven anlysis promotes a long-term perspective by engaging strategic workforce planning, talent management, and the planning of employee turnover, which allows for a sustainable suppliy of skilled employees. Strategic HRM revolutionizes the role of HR to function as a strategic partner to the organization – essential to business strategy, employee engagement and development, and the realization of a positive work culture. In terms of measurement of success, the integration of data-driven analysis into HR processes signiificantly expands the scope of the department’s objectives – primarily focusing on the impact of HR initiatives on organizational performance and competitiveness (Singh & Sarkar, 2021). In terms of assessment, data-driven analytics allows for the measurement of metrics such as employee productivity, engagement levles, talent retention, and alignment of HR practices with business outcomes (van den Heuvel & Bondarouk, 2017).

Area of Difference	Traditional HR Pactitioner	Data-driven Analysts (Strategic HRM or HR analytics)
Focus	Primary focus on execution of administrative tasks and day-to-day activities related to HR. This creates a transactory and reactive environment – with the objective of ensuring compliance with regulatory framework and employment laws.	Primary focus is to align HR functions with the overall strategic goals and objectives of the organization. Through data analytics, the strategic value of employees is emphasized with increased opportunities of leveraging employee skillls and expertise to establish a competitive advantage (McCartney & Fu, 2022).
Aims/ Objectives	Primary objective efficient handling of routine administrative tasks associated with HR operations. Specifically, these include tasks such as employee recruitment, selection, performance management, compensation, and the administration of employee benefits.	The integration of data analysts into HR operations is guided by the primary objective of aiding the department to contribute toward fulfilment of organization’s strategic goals. Strategic HRM is tasked with the development and implementation of HR strategIes that enhance employee engagement, organizational performance and long-term competitiveness (Yu et al., 2022).
Approaches	Based on their aforemention primary focus on R functions, traditional HRs adopt a functional approach whereby HR tasks are performed in isolation. This approach allows for very limited integration between HR and other departments with top-down, hierarchical decision-making frameworks (Harney & Collings, 2021).	Integration of data analysts into HR processes allows for comprehensive and integrated approaches. Strategic HRM acknowledges and appreciates the interdependencies between HR functions and the effect on organizational success. Integration of HR functions with business strategy enables cross-functional collaboration and particpation in strategic decision-making by HR professionals (McCartney & Fu, 2022).
Short-term/Long-term perspective	Traditional HR features a short-term perspective with a primary focus on the execution of immediate operational needs and the resolution of ongoing HR concerns. Focus on routine operations fails to appreciate the importance of long-term planning and forecasting (Harney & Collings, 2021).	The integration of data analysts into HR processes allows for strategic approaches that facilitate and promote long-term perspectives – focus on the perpetuity of the organization. Through strategic decision-making, informed by HR analytics, factors such as talent management, workforce planning, and turnover are handled efficiently to guarantee a sustainable supply of skilled employees.
Roles/Responsibilities	Due to their functional approach, traditional HRs are acknowledged as enforcers of policies and procedures. Additionally, they are tasked with maintenance of employee records and the monitoring of legal compliance (Harney & Collings, 2021).	Due to their integrated approach to HR operations, data analysts, through HR analytics, act as strategic partners to organizations. In contributing to the organization’s strategic goals, strategic HRMs are tasked with nurturing a positive work culture, identification of talent requirements, and overseeing employee development.
Measurement of Success	Due to their primary focus on the execution of administrative tasks associated with HR processes, traditional HRMs measure success based on the effiencies of these processes – compliance to legal requiremments, reduced time for filling vacancies, and cost control (Singh & Sarkar, 2021).	Due to their primary focus on the alignment of HR functions with organizational objectives, data analysts (HR analytics) measure success on the basis of the impact of HR initiatives on organizational performance and competitivesness. Some of these metrics of succes in integration include: employee/organizational productivity, employee engagement, talent retention/ employee turnover, and the lighnment of HR practices with organizational outcomes (van den Heuvel & Bondarouk, 2017).

Table 1 Key differences between traditional HR practitioners and data analysts integrated into HR (HR analytics/Strategic HRM)

The Skills Gap

The major point of difference between traditional HR practioners and their counterparts from strategic human resource management (data-driven analysts) can be observed in the discrepancies in technological proficiency and analytical thinking. The key skills lacking in traditional HR can be attributed to the concepts and applications of data and analytics in management – essentially, traditional HR do not possess the capability to comprehend or apply various strategies aimed at transforming data into actionable insights that result in improved organizational performance (McCartney & Fu, 2022). Both traditional HR practitioners and data-driven analysts (HR analytics) emerge from HR practices driven by HRM and organizational behaviour such as selection, training, and performance management. However, the key differences between these two groups can be observed when it comes to their implementation of HR analytics to improve decision-making. Essentially, whereas traditional HR practitioners can only assess the levels associated with a particular workforce attributes – basic elements of human capital such as cost per hire, HR analytics (data-driven analysts) seek to create an understanding of the impact of the workforce on the execution of firm strategy – an application of analytical techniques that interpret people data to inform organizational strategy and improve performance, such as the effects of increases in quality of project managers on new product cycle times (McCartney & Fu, 2022).

The Strategic Value of the “New HR” Practitioner

The integration of HR analytics functions as a tool of empowerment to management as it provides valuable insights that support proactive workforce planning and development through strategic decision-making. Through the analysis of employee data on engagement, performance, turnover, amongst other key metrics, management is able to identify trends and patterns that impact organizational success. Strategic HRM refers to the alignment of HR policies and practices with the objectives of the organization – these include: employee, financial. Operational, and stakeholder outcomes. To achieve organizational success, strategic HRM is employed to achieve data-driven decisions that are accurate, ethical, fair, and legal – factors that play a signifcant role within the modern era of big data (Chapter 2: Strategic HRM, Data-Driven Decision Making, and HR Analytics, 2021). In utilizing HR data to achieve organizational objectives and outcomes, strategic HRM introduces system thinkimg into HR processes. Essentially, data-driven decision-making of strategic HRM creates an awareness of universal best practices and enables assessments of how different Hr practices fit into the broader HR systen and organizational strategy.

In enabling the consideration of external and internal environments (systems thinking), and the acknowledgement of context, strategic HRM promotes high performance work practices (HR universal practices) and systens perspective (analysis of the interaction and interconnectedness of systems and subsystems). Compared to the functional and isolated framework of traditional HR systems, the integrated strategic HRM framework is quite advanced and superior in tems of efficiency and effectiveness. Research indicates that integrated systems of high-performance work practices outperform well-designed individual HR practices (Chapter 2: Strategic HRM, Data-Driven Decision Making, and HR Analytics, 2021, p. 32). As integrated systems, strategic HRM takes into account the various factors that influence the effectiveness of HR practices within its decision-making process – these factors include those from internal environment (business strategy, culture, and manager characteristics) and external environment (industry characteristics). Through data-driven decisions and HR analytics (people analytics, talent analytics, human analytics, or workforce analytics), and HR professionals who thin like scientists, managers are able to be provided with actionable evidence-based practices that significantly improve their management of people.

Overcoming Challenges and Resistance

Currently, there are growing concerns regarding agility – which makes HR one of the most challenging fields of management. More prominently, these challenges have been observed in the case of state-owned enterprises which face people-related issues relating to employee recruitment (attracting top talent to manage performance), employee engagement, and creating positive employee experiences (Singh & Sarkar, 2021). Additionally, the adoption of data-driven analysis (HR analytics) in practice faces numerous challenges – particularly with respect to misunderstanding of how it can be integrated or leveraged to increase organizational performance (McCartney & Fu, 2022). McCartney & Fu (2022) argues that despite the growth and adoption of HR analytics, a general lack of evidence-based research has created uncertainty about the impact of HR analytics on organizational performance. However, based on the findings of the study, access to HR technology has been observed to enable HR analytics which facilitates evidence-based management method (EBM), which in turn significantly improves organizational performance (McCartney & Fu, 2022).

The Future of HR: Best Practices and Recommendations

Currently, there’s an ongoing convergence of traditional HR practices with data-driven analysis – these have been enabled by the existing collaboration between HR and data science teams. Additionally, the integration of analytics into HR strategy development has been enhanced by the rapid development and significant growth of access to HR technology, such as cloud platfrms, apps, and human resource information systems (HRIS), has provided HR departments with the capability of collecting, managing, and analysing large volumes of employee data – with major area of improvement from earlier legacy IT systems (McCartney & Fu, 2022). As a result the “New HR” practitioner is gradually being introduced to the emerging field of HR analytics which offers evidence-based approaches to improve recruitment and selection processes, in addition to other key determinants of organizational performance such as diversity and inclusion, employee engagement, and turnover. Since data-driven analysis (HR analytics) is enabled by HR technology, there’s need for developing a culture of continuous learning within the field of HR. Retraining of personnel in data analytics and the use of HR technology could significantly improve the collection, manipulation, and reporting of structured and unstructured workforce data (McCartney & Fu, 2022)

Conclusion

Based on the comparison between traditional HR practitioners and strategic HRMs, and the analysis of existing literature, there appears to be an evident transformation from traditional HR roles to “new HR” roles – these have been majorly driven by changing work dynamics, advancements in technology, and shifts in employee expectations. In terms of organizational outcomes, the functional and isolated framework of traditional HR practitioners has been found to be quite limiting and inefficient. The “new HR” practitioner, identified as strategic HRMs that employ data analysis through data-driven decisions and HR analytics, has been obsered to be far superior in terms of efficieny and effectiveness – a factor that has been significantly improved by their integrated approach toward the performance of their HR functions. In an era of big data, the “new HR” is far more adaptable and effective.

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