"What is HR analytics?" is a question asked by many HR professionals who want more data processing in their work. We'll go through what HR analytics is, how to get started, and how it will impact businesses in the future in this post.
What is HR Analytics?
Human Resource Analytics, also known as demographics, staff statistics, or talent statistics, includes the collection, analysis, and reporting of HR data. It allows your company to evaluate the impact of various HR KPIs on the performance of the entire business and make data-driven decisions. Human Resource analysis, in other words, is a way of conducting employee management data.
Human resource analytics is a relatively new instrument. As a result, the scholarly literature on the subject is still substantially unexplored. Heuvel & Bondarouk's definition of scientific Human Resource analytics is the most well-known. Human Resource analytics, they claim, is the methodical discovery and quantification of the human factors that influence business outcomes (Heuvel & Bondarouk, 2016).
Human Resource Management has evolved considerably during the last century. It has changed from an application to a multi-strategy field. This is exemplified by the popularity of the phrase Strategic Human Resource Management (SHRM). Human Resource analytics, which is characterized by a data-driven approach, is in line with this trend.
Example of HR Analytics
Organizations can use Human Resource Analytics in a variety of ways. Consider the following scenarios with common organizational issues:
When employees leave, it's common for them to have no idea why. There may be reports or data on individual circumstances, but there is no way of knowing whether the turnover is due to an overriding reason or trend. Organizations require this insight to prevent turnover from becoming a long-term problem, given how costly turnover is in terms of lost time and profit.
HR analytics can be used to:
- Gather and evaluate historical data on employee turnover to uncover trends and patterns that may indicate why employees leave.
- Gather information on employee behavior, such as productivity and engagement, in order to better understand current employee status.
- Combine the two forms of data to have a better understanding of the reasons that cause a turnover.
- Assist with the development of a prediction model to better track and flag employees who may fit the recognized pattern of quitters.
- Create plans and make decisions to improve the workplace and employee engagement.
- Recognize patterns of employee satisfaction, engagement, and performance.
Organizations are looking for people that not only have the proper talents, but also the correct personality traits that fit with the company's work culture and performance requirements.
Sifting through hundreds or thousands of resumes and making a hiring choice based on the most basic facts is time-consuming, especially when potential prospects are overlooked. One organization, for example, may find that inventiveness is a greater predictor of success than comparable work experience.
HR analytics can be used to:
- Enable quick, automated candidate data collection from numerous sources.
- Gain a detailed understanding of candidates by taking into account a variety of factors, such as developmental opportunities and cultural fit.
- Look for applicants that have characteristics that are similar to those of the company's best performers.
- Avoid habitual prejudice and provide a fair chance for all candidates; with a data-driven approach to recruiting, one person's attitude and opinion can no longer influence applicant consideration.
- Provide analytics on how long it takes to hire for specific roles within the company, allowing departments to be better prepared and informed when the time comes to hire.
- Provide historical data on periods of over-hiring and under-hiring, allowing companies to design more effective long-term recruitment strategies.
How Many Types of HR Analytics?
Human Resource analytics can help a business save money by minimizing the expenses of bad personnel planning. Overstaffing, understaffing, and hiring the wrong people can all hurt your bottom line. The appropriate analytics will help you develop a high-performing organization.
Descriptive, predictive, and prescriptive Human Resource analytics are the 3 categories of Human Resource analytics. Each offers a unique viewpoint on your company's data. Each has its own set of advantages and disadvantages, yet they all complement one another.
1. Descriptive Analytics
Raw data is useless and cannot be used to explain why something occurred. It can, however, once aggregated. Descriptive analytics is the form of analytics you're probably most familiar with. It entails taking historical data and condensing it into something digestible. A headcount report of all employees inside an organization, for example, is a type of descriptive analytics. Even if you broke it down by demographics, it would still fall into the same group.
More advanced data, such as turnover rates or time-to-fill, would also be descriptive. They are based on the past and are attempting to explain why something has already occurred. It's very reactive to focus solely on descriptive metrics. HR should focus on being proactive as it evolves to meet business needs.
2. Predictive Analytics
Predictive analytics work to look ahead, whereas descriptive analytics work backward. To answer the issue of what might happen, statistical models and projections are used. Models are created based on patterns discovered through descriptive analytics. The purpose is to identify the organization's needs ahead of time.
Before someone is hired, predictive analytics can assist talent acquisition teams to evaluate if they will be a good cultural fit for the firm. It could even predict how long the employee will remain with the organization.
3. Prescriptive Analytics
The second question is what can we do once the future has been foreseen. Prescriptive analytics makes suggestions based on what has happened in the past and what has been predicted.
For businesses that experience peak or busy seasons, this analytical approach can be extremely advantageous. During the holidays, a retailer will need to determine how many individuals to hire. Alternatively, a park could need to know how many people to hire over the summer. Prescriptive analytics could also assist in determining how to effectively onboard a new hire based on their talents and strengths, as well as throughout their employee life cycle.