HR ANALYTICS[26].pptx

HR Analytics is the collection and application of talent data to improve critical talent and business outcomes. it helps leaders with essential data to improve function and employee experience.

1. HR ANALYTICS 1

2. INTRODUCTION: HR is not any more pure support and back-office role in companies. The role of HR has evolved into more active responsibilities like shaping business strategies and proactively developing the workforce. But HR teams can’t do this successfully without having the necessary data to support and influence people-related decisions. Analytics is a mental framework, a logical progression first and a set of statistical tools . Analytics is defined as the science of analysis, from the Greek word “ analutika ” , including the principles of mathematical analysis. . In the past century, Human Resource Management has changed dramatically. It has shifted from an operational discipline towards a more strategic one. The popularity of the term Strategic Human Resource Management (SHRM) exemplifies this. The data-driven approach that characterizes HR analytics is in line with this development: “HR Analytics is the systematic identification and quantification of the people drivers of business outcomes”. 2

3. DEFINITION: HR analytics is the process of collecting and analyzing Human Resource (HR) data in order to improve an organization’s workforce performance. The process can also be referred to as talent analytics, people analytics, or even workforce analytics. The core of HR Analytics is the Metric . “Metric” can be said as data that conveys meaning in a given context. For example, if a software engineering firm has high employee turnover, the company is not operating at a fully productive level . It takes time and investment to bring employees up to a fully productive level. HR analytics provides data-backed insight on what is working well and what is not so that organizations can make improvements and plan more effectively for the future. 3

4. DIFFERENCE BETWEEN HR ANALYTICS, PEOPLE ANALYTICS & WORKFORCE ANALYTICS: The terms HR analytics, people analytics, and workforce analytics are often used interchangeably. But there are slight differences between each of these terms. HR ANALYTICS: HR analytics specifically deals with the metrics of the HR function, such as time to hire, training expense per employee, and time until promotion. PEOPLE ANALYTICS : The term “people analytics” may be applied to analytics about the customers of an organization and not necessarily only employees . WORKFORCE ANALYTICS : Workforce analytics is an all-encompassing term referring specifically to employees of an organization. It includes on-site employees, remote employees, gig workers, freelancers, consultants. 4

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6. HOW DOES HR ANALYTICS WORK: 1) To gain the problem-solving insights that HR Analytics promises, data must first be  collected . 2)The data then needs to be monitored and  measured  against other data, such as historical information, norms or averages. 3)This helps identify trends or patterns. It is at this point that the results can be  analyzed  at the analytical stage. 4)The final step is to  apply  insight to organizational decisions. 6

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8. WHY HR ANALYTICS: 8

9. BENEFITS OF HR ANALYTICS; 9

10. PROS & CONS OF IMPLEMENTING HR ANALYTICS: PROS: 1) More accurate decision-making can be had thanks to a data-driven approach, which reduces the need for organizations to rely on intuition or guess-work in decision-making. 2) Employee engagement can be improved by analyzing data about employee behavior. 3) Recruitment and hiring can be better tailored to the organization’s actual skillset needs by analyzing and comparing the data of current employees and potential candidates. CONS: 1) Many HR departments lack the statistical and analytical skillset to work with large datasets. 2) Access to quality data can be an issue for some organizations who do not have up-to-date systems and organizations need access to good quality analytical and reporting software that can utilize the data collected. 3) Monitoring and collecting a greater amount of data with new technologies ( eg. cloud-based systems, wearable devices), as well as basing predictions on data, can create ethical issues. 10

11. THANK YOU 11