Unlock the Power of HR Analytics: Four Levels of Insight - Essay Sample

Introduction

HR analytics provides a basis for evidence-based practice in human resource management. It involves the collection of HR data, which undergoes further evaluation through various metrics to align the data to the HR and organizational goals. According to Fitz-Enz and John Mattox (2014), HR analytics provide descriptive, predictive, and prescriptive approaches to organizational HR performance. The process is based on the four levels of HR analytics. Different organizations operate at different levels, with the four levels used as a measure or organizational HR maturity. The paper defines the four levels of HR analytics, description, and application in real-life HR management.

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Operational Reporting

Operational Reporting entails the use of available data in unfolding and reflecting on past events as well as establishing the mechanism of the occurrence of the events. Basically, it is the descriptive reporting based on a particular event on the company’s HR measures. Big Data offers a vast information base used by HR in the analysis and evaluation of significant HR practices such as talent management, recruitment, training, and performance appraisal (Dahlbom et al., 2019). The quality of data tracked for HR analytics is vital in establishing viable conclusions. According to Dahlbom et al. (2019), 56% of organizations are at the operational reporting level. In this case, it can be deduced that the majority of companies are rooted in the traditional reporting on the headcount, training cost, and labor cost. Operational reporting is based on the preexisting HR systems, learning, and development systems. According to Dahlbom et al. (2019), the operational reporting level of HR analytics derives the meaning from the existing data, which enables the company to initiate a diagnostic level. For instance, using employee attendance data can highlight cases of time management and absenteeism. Organizational success is influenced by the dedication and productivity of its workforce. Information on time management and absenteeism can be used to evaluate employee commitment to organizational goals.

Advanced Reporting Level

The activities of the advanced reporting level coincide with those of descriptive reporting. However, advanced reporting is more intensive, requiring frequent descriptive reporting along with an increase in the tracking trends for reporting. The reporting represents the current information relevant to the organization’s success. At this level, the focus is on the relationship between variables and the impact on the outcomes. This is a proactive process, routine or automated facilitating continuous reporting. The reports are generated to support the decision-making and planning processes. Besides, benchmarking HR data validates the data collected, improving the meaningfulness of the data to the organizational goals.

In some cases, advanced reporting initiates the measurement and comparison processes based on HR metrics. The role of HR analytics in advanced reporting is a comparison of the collected data to the organizational standards and historical norms. The process requires a continuous flow of data and, therefore, a higher frequency of data reporting compared to operational reporting. For instance, an organization can compare absentee data to data from other benchmark companies to establish an acceptable absentee rate for the success of the organization. Organizations require advanced level reporting for improved decision-making and strategic planning process.

Strategic Analytics

Strategic analytics is the first stage of a thorough analysis of the data collected. According to Cheng (2017), 14% of organizations operate at the strategic analytics level. The process may be aligned to establishing the causal models or illuminating the impact of variables on the outcome. The root cause of each variable is formulated with a focus on a specific impact on HR performance (Cheng, 2017). The company’s ability in identification and prediction of the HR practices is experienced at this level with decision making and planning based on the interpretation of statistical data. The value of HR can be improved through the identification of issues and recommending a viable solution. For instance, an organization can identify that a high absentee rate is attributed to long working hours. Therefore, the organization can orchestrate a compensation plan, such as overtime, to improve employee engagement.

Organizations adopt a productive team that can promote values and objectives. Identification of the limiting factors to full productivity of the workforce provides the HR department with a guideline to stimulate employee engagement, satisfaction, and productivity. Analysis of the HR metrics highlights and proposes solutions to employee challenges. Employee turnover rates can be determined along with the drivers for turnover and proposing strategies to curb the problem. The strategies integrate the goals of HR with the company goals.

Predictive Analytics

The predictive analytics is concerned with prediction and problem-solving. According to Fitz-Enz and John Mattox (2014), the process utilizes data from the strategic analytics from which it draws conclusions and corrective measures to the employee challenge. HR analytics offers remedies based on the predictions and the causal effects predicted in the data. The planning process can also be initiated to align future HR operations to the success of the organization. According to Stone, Neely, and Lengnick-Hall (2018), descriptive analytics infers from the findings from strategic analytics and aligning the HR operations to the organizational norms. This is the highest level of HR analytics-focused, mainly in making predictions based on analyzed past data. Departments functioning at this level gather data and use it for prediction and planning purposes only.

Effective integration of strategic analytics with predictive analytics can illuminate the loopholes in the HR and facilitate intervention and improvement of the HR performance (Fitz-Enz & John Mattox, 2014). For instance, evaluating the results obtained in strategic analytics in the evaluation of drivers of employee turnover, the organization can use predictive analytics in establishing the evidence-based framework for countering employee turnover. Employee engagement, stability, and participation in organizational goals are vital in the drive towards the company’s objectives (Stone, Neely & Lengnick-Hall, 2018). Therefore, departments executing descriptive analytics provide a basis for remedies and promotion of HR performance. Descriptive analytics model scenarios oriented to help improve workforce planning.

Conclusion

In conclusion, HR analytics refers to the application of vast data sources in streamlining the operation of the HR department in an organization. HR analytics is classified into four levels, which include operational reporting, advanced reporting, strategic analytics, and predictive analytics. Different levels offer a distinct analysis of HR data. As pointed out in the data above, operational reporting uses HR data to understand past events. Advanced planning creates meaning out of the descriptive data obtained in the operational reporting. However, more parameters are integrated, making the reporting frequent, routine, and in most cases, automated to keep up with the pace of data reporting. Strategic analytics involves intensive analysis offering a platform for predictive analytics to operate. Predictive analytics is focused on problem-solving adapting information from strategic analytics. This is the highest level of HR analytics.

References

Cheng, M. (2017, January). Causal Modeling in HR Analytics: A practical guide to models, pitfalls, and suggestions. In Academy of Management Proceedings (Vol. 2017, No. 1, p. 17632). Briarcliff Manor, NY 10510: Academy of Management.

Dahlbom, P., Siikanen, N., Sajasalo, P., & Jarvenpää, M. (2019). Big data and HR analytics in the digital era. Baltic Journal of Management.

Fitz-Enz, J., & John Mattox, I. I. (2014). Predictive analytics for human resources. John Wiley & Sons.

Stone, C. B., Neely, A. R., & Lengnick-Hall, M. L. (2018). Human resource management in the digital age: Big data, HR analytics, and artificial intelligence. In Management and technological challenges in the digital age (pp. 13-42). CRC Press.