“Find out the difference between HR analytics vs HRM and understand how they can fit into your organizational system to improve results.”

Let me clearly state that HR stands for Human Resources, and HRM stands for Human Resources Management. As we go on, we will explain what each term means.

However, it is important to note that human resources management (HRM) and HR analytics were previously considered personnel administration, focusing on administrative tasks such as staff payment, record-keeping, and managing basic employee needs. 

The shift from this administrative function to a more strategic role began with the human relations movement in the early 20th century, which emphasized employee welfare, and the terms “personnel management” or “manpower management” became the standard. 

As businesses grow, so did the role of managing human capital. Today, there is a significant shift towards talent analytics platform, where HR policies and practices are software systems that use data to provide insights into an organization’s workforce, helping with decisions related to recruitment, retention, performance, and planning closely aligned with the overall business strategy.

In this article, we will break down what each of these terms means, how talent analytics platform comes into play, and how you can make use of both in your organizational system.

What is HR Analytics?

Data is a very important commodity in today’s marketplace. There are several digital tools that can provide you with a reasonable amount of information; however, data in its raw form has no value.

Here’s where HR Analytics comes into play, transforming raw data into insights to resolve employee and business challenges.

HR analytics, commonly called people analytics or workforce analytics, is the process of collecting, organizing, and interpreting data about an organization’s human resources to guide better business decisions around factors like performance, engagement, and retention.

This process gives you an understanding of what’s happening with your employees and, at the same time, turns raw data into useful information, allowing you to take action confidently.

It replaces intuition with evidence to optimize areas such as recruitment, employee engagement, retention, and training.

HR Analytics involves several steps, like gathering data from sources like HR systems and surveys, cleaning and integrating it, using statistical and predictive models to find patterns, and finally, translating these findings into actionable recruitment, retention, and performance management strategies.

Types of HR Analytics

There are four main types of HR analytics, which progress from analyzing past events to using data to forecast future outcomes and recommend specific actions.

1. Descriptive Analytics: This involves summarizing historical data to determine what has happened in that system over time.

In this context, questions are asked to understand the what and why to give the user a historical perspective on what happened in the organization regarding relationships, employee turnover rates, or absenteeism.

Descriptive Analytics helps organizations understand specific patterns that have happened in the past and sheds light on current challenges and how to fix them. 

2. Diagnostic Analytics:  Diagnostic Analysis further explains “why” certain events happened within the HR domain.

This approach focuses on identifying the causes of workflow challenges and drawbacks. It looks for relationships and anomalies in the data to understand the contributing factors to past events.

This helps companies understand why problems occur and how to address them before they become a problem. This approach improves staff engagement, productivity, and a successful system.

3. Predictive analytics: Predictive Analysis is a type of HR analytics that addresses “What should we do?” by recommending the best course of action to achieve desired outcomes.

It uses statistical algorithms, data mining techniques, and machine learning models to analyse historical data like skill gaps, the likelihood of an employee leaving, and how much space employees need to predict future outcomes.

Instead of focusing on what has happened in the past and what is happening now, predictive analytics focuses on what will happen in the future.

4. Prescriptive Analytics: Prescriptive Analytics take it a step further from predictive analysis by using data and machine learning techniques to recommend actions that should be taken to optimize their workflow and avert negative outcomes.

It addresses “What should we do?” by recommending the best action to achieve desired outcomes. It uses predictive models and optimization algorithms to provide actionable steps that HR professionals can carry out, such as recommending the most effective training programs to improve productivity.

What is HRM?

Human Resource Management is the strategic approach to managing an organization’s employees to maximize productivity and achieve business goals. It involves several functions, including recruitment, compensation and benefits, training/nurturing, performance management, and fostering employee relations. 

It is a collective term for all the systems in place to help manage employees in an organization. The main goal of an HRM system is to support the organization’s success by efficiently managing its “human capital” and creating a positive, productive work environment for everyone.  

I want you to understand that these terms I have just explained (HR analytics and Human Resource Management) work hand in hand.

How?

HR Analytics is a tool that enhances Human Resources Management by using data to inform decisions and events. At the same time, HRM is the primary function of managing an organization’s workforce. 

HRM handles recruitment and selection, onboarding, training and development, performance management, employee relations, HR planning, compliance, and employer offboarding. While HR Analytics provides data-driven insights to make these processes more strategic, such as predicting turnover or improving hiring decisions. 

So, every organization needs HRM for its primary functions and HR Analytics to optimize those functions for better performance and strategic advantage.

How Talent Analytics Accommodates HRM and HR Analytics

Talent analytics, sometimes called “people analytics,” is a data-driven methodology for making human resources decisions about an organization’s current and potential future workforce. 

Talent analytics is a type of people or HR analytics, the data-driven method of collecting, analyzing, and interpreting employee and organizational performance data. 

A talent analytics platform takes raw data from HR systems and uses statistical and predictive models to transform it into actionable insights for strategic decision-making. It is a specialized subdivision that focuses specifically on data-driven talent management decisions.

These platforms impact every stage of an employee’s journey, from hiring and onboarding to ongoing performance management, development, retention, and offboarding.

Conclusion

Human resources is a valuable organizational asset. Employees are very important to the company’s growth and productivity. The HR professional’s job is to collect and monitor personal data from employees and executives through personnel numbers, payroll, health and safety, and performance management.

HR is viewed as a strategic business partner who adds value to the company. Human resources are the elements that execute various activities critical to the company’s success.

Software Testing Lead can agree that HR analytics cannot work without Human Resource Management, and vice versa, because they are interdependent, not separate functions. HRM handles the operational and strategic execution of people-related tasks, while HR analytics provides the data-driven insights needed to inform and improve those HRM practices.