Believe it or not, in today's fiercely competitive business environment and rapidly evolving conditions, traditional human resources practices often prove inadequate. Various deficiencies such as lack of scientifically based hiring, low employee engagement and motivation, insufficient technical and behavioral training, absence of coaching and mentoring, limited career advancement opportunities, prolonged periods without promotions, increased rates of "soft forced" resignations, and inadequate job security are prevalent in modern organizations.
Doesn't this inevitably lead to diminished business performance and negatively impact operations? Doesn't it indicate the need for HR personnel to consider reworking their approaches? So, what is the solution?
Let's start by posing a question: If you were an HR professional, would you prioritise hiring introverted or extraverted individuals? How can this be discerned during the interview process? What questions might be asked to determine this? How does the interviewee's body language indicate whether they lean towards introversion or extraversion?
But hold on! Are you getting confused between "Analysis" and "Analytics"? Analysis involves looking backward over time, providing a historical perspective on past events, while Analytics focuses on forecasting and modeling future outcomes.
According to Mortensen, Doherty, and Robinson (2015), Analytics is the amalgamation of computer science, decision-making, and quantitative methods aimed at organizing, analyzing, and interpreting the vast amount of data generated by modern society.
In recent years, Human Resource Analytics has gained significant importance. Utilized to analyze an organization's HR data, identify problems, and devise strategies, HR Analytics offers a substantial competitive advantage.
Research indicates that HR analytics allows businesses to transition from traditional spreadsheet-based data repositories to real-time data management, facilitating analysis alongside existing organizational data flows.
Moreover, with the integration of artificial intelligence methods, more businesses are investing in HR analytics, leading to enhanced efficiency and cost savings.
Through the development of HR analytics modules, businesses can effectively manage HR processes, make better hiring decisions, predict employee turnover, and optimise workforce planning for the future.
Modern organisations increasingly prioritizse practical HR tools that align with contemporary HR processes, as evidenced by Sooraksa (2021), who underscores the significance of AI in creating excellent HR analytics opportunities.
How effective is your role as an HR manager? This direct question prompted one HR manager to emphasize the need for reengineering HR processes and tools using modern technology and mindset, hinting at an impending transformation in HR practices.
According to Ulrich (2010), HR must evolve from descriptive metrics to predictive analysis.
Undoubtedly, Human Resources plays a pivotal role in any organization. In today's competitive market, organizational success hinges on aligning business strategies with HR strategies.
A cohesive link between HR strategy and organizational strategy is essential to maximize shareholder value and employee satisfaction.
One definition of HR analytics found in research emphasizes its data-driven nature, employing statistical techniques, data mining models, and machine learning methods to make predictions based on performance-related data at both individual and organizational levels.
As per Masese Omete Fred (2017), HR Analytics involves the fusion of quantitative and qualitative data, responding to the rapid changes and heightened competition experienced by organizations.
According to the report of Davenport et al. (2010b), sophisticated methods for analyzing employee data are increasingly used to gain a competitive edge. Let's explore how the LAMP framework contributes to HR effectiveness. Here is the framework:
Under the LAMP framework, HR may focus on thirteen key areas, including monthly turnover rate, revenue per employee, yield ratio, human capital cost, HR-to-staff ratio, return on investment, promotion rate, percentage of females in management, employee absenteeism rate, workers' compensation costs, overtime per employee, and average employee age.
Regularly reading the Harvard Business Review (HBR) always deepens one's understanding of management. In one of its issues (magazine), Harvard Business Review Analytic Services conducted a study involving HR and other executives in 2014. The findings revealed that organizations were not consistently utilizing predictive analytics. Among the respondents, one-third were HR professionals.
The survey indicated that only 9% of the companies included in the study were using predictive analytics to forecast their workforce trends, while 40% of companies were employing data reactively to inform critical workforce decisions.
To conclude this article, how does the HR professional perceive the impact of HR analytics?
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