Human Resource Metrics and Analytics

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Annotated Bibliography

Anger, O., Tessema, M., Craft, J., & Tsegai, S. (2021). A framework for assessing the effectiveness of HR metrics and analytics: The case of an American healthcare institution. Global Journal of Human Resource Management, 9(1), 1-19.

This article addresses the research gap in the literature on HR metrics. Anger et al. focus on evaluating the challenges and practices of staffing analytics by investigating the approaches of an American Healthcare Institution (AHCI). The study findings suggest that HR metrics’ effectiveness depends on the organization’s willingness to implement ten essential factors. The recruitment analytics of the AHCI under review proves relatively efficient due to the compliance of the company’s practices with most factors. The primary value of this study is the conceptual framework for evaluating the efficacy of staffing metrics and analytics, which can be applied in any industry.

Edwards, M. R. (2019). HR metrics and analytics. In M. Thite (Ed.), e-HRM: Digital approaches, directions & applications (pp. 89-105). Routledge.

In this chapter, Edwards provides an overview of key principles and techniques of HR analytics. A wide range of HR metrics is discussed, along with statistical analysis tools applicable to recruitment practices. The main challenges for the HR function resulting from the increasing importance of staffing analytics and metrics are discussed. This chapter’s content is valuable as it provides fundamental knowledge of the subject in a well-structured fashion and emphasizes the role of HR analytics in collecting and interpreting the data related to human capital performance.

Edwards, M. R., & Edwards, K. (2019). Predictive HR analytics: Mastering the HR metric (2nd ed.). Kogan Page.

This book provides a framework for working with HR analytics by utilizing advanced approaches. The authors cover various aspects of HR metrics and analytics, such as predictive models, recruitment analysis ethics and limitations, the application of advanced tools to anticipate turnover and performance. Edwards and Edwards highlight the role of excellent technical skills in managing human resources within a company. The book offers valuable practical knowledge by presenting and analyzing case studies as well as specific predictive models.

Fitz-enz, J. (2010). The new HR analytics: Predicting the economic value of your company’s human capital investments. AMACOM.

In this book, Fitz-enz provides an overview of the HR metrics’ evolution and presents concepts critical to predicting the value of human capital investments. The author presents a proprietary analytic model used to measure and estimate previous and current returns. Furthermore, the application of analytical tools discussed in the book allows for creating an agile workforce and sustaining performance. Fitz-enz addresses the risks and challenges that emerge in the decision-making process and offers possible solutions to increase returns on human capital investment. The book is especially valuable for its real-world examples and insights from leading HR practitioners.

Sharma, A., & Sharma, T. (2017). HR analytics and performance appraisal system: A conceptual framework for employee performance improvement. Management Research Review, 40(6), 684-697.

This article examines the impact of HR analytics on employees’ readiness to improve performance. In particular, the authors identify the concept of a performance appraisal (PA) system and the associated issues. The paper’s significant findings suggest that the application of recruitment analytics will positively influence employees’ perceived fairness and accuracy, consequently increasing their satisfaction with the PA system. As a result, the staff’s willingness to enhance performance is expected to grow. The article by Sharma and Sharma presents a conceptual framework and propositions regarding performance management and the role of HR analytics, thus, providing value for HR managers.

Stewart, G. L., & Brown, K. G. (2019). Human Resource Management (4th ed.). Wiley.

The book by Stewart and Brown integrates theoretical and practical knowledge of HR management practices. In particular, the authors focus on the role of analytics and prediction in human capital management, address the risks, and explore the opportunities for improving HR processes. Furthermore, the concept of business intelligence is addressed, and the ways of data interpretation of HR metrics are discussed. Stewart and Brown emphasize the role of effective HRM in the company’s success in a continually changing business environment. This book provides value for managing recruitment processes and HR analytics due to its focus on the practical application of strategies.

Metrics and Analytics

Human resource management is critical to any organization since it allows for optimizing the effectiveness of human capital. Thus, the whole company’s productivity significantly depends on its HR strategy. The use of data is becoming even more important for staffing, and statistical approaches are used to show tendencies in employee engagement and HRM. According to Fitz-enz (2010), metrics and predictive analytics are necessary elements of organizational management that help HR professionals enhance their contribution to the business. Collecting and integrating data from various sources provides facts and evidence needed to evaluate how the entire system functions.

Over the recent years, recruitment metrics have evolved, improving access to larger amounts of data. HR metrics determine the efficiency of staffing practices, while HR analytics help businesses to make effective decisions affecting turnover rates and employee engagement. This paper aims to discuss the importance of analytics for HR, suggest the ways of implementing descriptive and prescriptive analytics, and recommend strategies to avoid four common errors made with metrics.

Importance of using human resource analytics

Human resources analytics is changing the field of HR by enabling better decision-making based on data. Workforce metrics imply collecting, organizing, and interpreting information related to HR functions, such as recruitment, selection, development, performance management, training, employee engagement, communication, and rewards (Fitz-enz, 2010). It is important to use HR analytics to provide the company with critical information and visions regarding the workforce, which will allow for effective employee management and the accomplishment of business goals. Key metrics can measure the cost and success of staffing processes and initiatives, thus, allowing the company to track general trends.

Currently, HR analytics heavily relies on predictive analysis, a technology that forecasts behavior based on existing data. The evolution of staffing metrics and techniques is critical to gain a better understanding of employee engagement and satisfaction, as well as predict and prevent turnover intentions. Most hiring decisions are likely to improve with the application of predictive human capital management (HCM), such as Professional/Managerial Ratio, Readiness Ratio, Commitment Ratio, Leadership Rating, Climate-Culture Rating, Training Rating, and others (Fitz-enz, 2010). Furthermore, the role of organizational culture in employee performance becomes more evident due to recruitment metrics. As Sharma and Sharma (2017) report, using HR analytics is essential to overcome bias in the performance appraisal system, thus, positively impacting staff’s willingness to enhance performance. Overall, human resources analytics offer a number of benefits to companies in various sectors by providing a basis for efficient decision-making in a wide range of scenarios.

If you were a human resource manager, how would you make use of descriptive and prescriptive analytics?

Descriptive and prescriptive analytics are two critical stages of data analysis in HR. The former provides information on what happened in the past, while the latter suggests possible actions to undertake to influence future outcomes. If I were an HR manager, I would make use of descriptive analytics by implementing metrics, such as turnover rates, time-to-hire, cost-per-hire, time to productivity, and average employee tenure (Anger et al., 2021). These indicators summarize historical data and report the frequencies and percentages on a monthly, quarterly, or yearly basis. However, since descriptive analytics is regarded as reactive, prescriptive analytics is required to ensure proactive measures in the company. It refers to data intelligence allowing the firm to integrate descriptive analytics with future probabilities to eliminate risks and increase ROIs (Fitz-enz, 2010). For instance, I would address the issue of employee turnover by looking at predictive analytics findings and setting a course of action to retain the individuals who intend to quit. The combination of descriptive and prescriptive analytics can benefit the organization by suggesting relevant measures to enhance employee management and the company’s performance.

Ways to avoid four common errors made with metrics

There are several metrics mistakes in HR analytics that I would try to avoid. For instance, according to Fitz-enz (2010), one common error undermining the efficiency of data analysis is “confusing data with information” (p. 314). To avoid this mistake, I would make sure to present the data clearly and give meaning to numbers and figures. This step is critical to understand the value of the data and identify further actions. Another frequent mistake is valuing inside instead of outside data. To handle this problem, I would focus on reporting on human capital and employee activity rather than the department’s processes. Furthermore, it is critical to choose metrics wisely and ensure that they address relevant business questions to avoid generating impractical data (Fitz-enz, 2010). Finally, another critical mistake is the tendency to measure activity and not the impact. To eliminate this error, I would evaluate the effects of the metrics before presenting them. Doing so will help the company see positive and negative connections, which, in turn, provides a basis for implementing measures.

To conclude, human resource metrics and analytics are essential for smart decision-making in the company. In particular, the main benefits of implementing HR analytics are reducing turnover rates, addressing risks, and enhancing staffing processes based on data analysis. The application of descriptive and prescriptive HR analytics helps model scenarios and design action plans aiming to increase human capital investment returns and, consequently, enhance the company’s performance.

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