What Is HR Analytics And Why Is It Important? (With Types)
By Indeed Editorial Team
Published 8 August 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Human resource (HR) professionals use HR analytics to evaluate how investing in human capital assets can help improve an organisation's workforce. This technique also helps them contribute to better decision-making and improves business outcomes. If you are an HR professional or hiring manager, you can benefit from learning about HR analytics and how you can use it at an organisation to improve its human resource management. In this article, we answer the question, 'What is HR analytics?', list its types and benefits and explain how you can effectively implement it in the workplace.
What Is HR Analytics?
The answer to 'What is HR analytics?' is that it is the process of collecting and analysing data to guide talent, improve the hiring process and increase employee engagement in an organisation. The use of HR analytics allows businesses to make more informed decisions, such as selecting the right employees, revising company policies and practices and strengthening their human resources. Data-driven decisions allow a company to compare its outcomes with previous data and predict future outcomes.
Why Is HR Analytics Important?
The objective of HR analytics is to provide insight into effective employee management and help businesses meet their short-and long-term goals. A better understanding of the workforce allows HR professionals to create policies that support employee development and create a work environment where employees feel appreciated and rewarded. It also allows them to identify the activities that have the greatest impact on employee engagement and develop strategies to promote those activities.
Types Of HR Analytics
Here are some types of HR analytics:
HR professionals use descriptive analytics to gather and analyse data that provides an insight into the current state of events at an organisation. The technique converts complex data from multiple sources into a format that is easily visualised and understandable. This requires HR professionals to identify reliable sources of data for analysis.
HR professionals use predictive analytics to make forecasts and get insights into what might happen in the future based on past events. This method uses data modelling and machine learning to make accurate predictions. The various parameters to consider while building a model include job history, education, skills, personality traits and competitor data.
After making the forecast, the HR team studies relevant factors to decide on an optimal course of action. Prescriptive analytics can help them devise a data-driven workforce strategy for allocation, optimisation and hiring processes. It is essential that the data used in this step is accurate, complete, reliable and relevant.
Benefits Of HR Analytics
The benefits of HR analytics include:
Improved talent acquisition
The process of talent acquisition involves identifying, evaluating and selecting a suitable candidate for a job role. The benefits of using HR analytics for talent acquisition include:
Identifying candidates based on their job history, skills and personality traits
Predicting candidate success
Eliminate bias while hiring
Improved hiring process
Professionals in human resources use HR analytics to make decisions based on historical data. It also allows them to learn more about a candidate through online databases, job portals, social media profiles, past employment records and resumes. The key metrics to consider during the hiring process are:
Time to hire: The HR team uses this metric to determine how long it takes for a candidate to join a company after applying for a job. It helps measure the efficiency of the recruitment process.
Cost to hire: Cost to hire measures the cost of hiring new employees. Some expenses include sourcing and advertisement, onboarding, accommodation and referral bonus costs.
Quality of hire: It measures the value that newly hired employees provide and their contribution to an organisation's growth and long-term success.
Sources: Sources provide detailed information on where the candidates learned about the job opening. It is possible to consolidate the source-of-hire metric in an HR data warehouse to find out how effective different channels are at advertising jobs.
Application completion rate: This metric compares the number of candidates who have started filling a job application to those who have applied successfully. HR professionals can determine the most effective job postings and those that have the highest drop-out rates.
Sourcing channel cost: This metric measures the effectiveness of ads that organisations run on various media sites and job platforms. This measures the ratio of cost per platform to the number of successful hires made from each platform.
Better employee insight
HR analytics helps professionals get an insight into employee performance by tracking metrics, such as:
Work efficiency: Work efficiency measures the productivity of individuals and various business units of an organisation.
Quality of work: Quality of work measures the accuracy and competence of employees in an organisation.
Teamwork: This metric measures how well employees collaborate with their peers to fulfil business objectives.
Learning curve: The learning curve assesses how training impacts an employee's performance.
Adherence to timelines: This metric measures how well a project follows its planned schedule.
HR professionals can use the metrics to identify performance gaps and help employees improve their skills and productivity.
Reduced attrition rate
HR professionals can use various techniques, such as descriptive analytics and predictive analytics, to gain a better understanding of the workforce. Using HR analytics can help them discover factors that result in a higher turnover at an organisation. By using this information, they can devise various strategies to retain employees. Some strategies may include:
Hiring the right candidates suitable for a job role
Providing fair compensation and benefits
Offering flexibility to employees
Focusing on work-life balance
Providing opportunities for employee development
Developing career paths for employees
How To Implement HR Analytics
Follow these steps if you want to implement HR analytics in the workplace:
1. Choose metrics to monitor
The first step in implementing HR analytics at an organisation is to select indicators and metrics for HR systems with predictive capabilities. Consider these aspects when choosing or designing metrics:
The value that the metric tracks or measures
The formula to calculate or generate the metric
The data type representing each metric
The frequency with which it is necessary to collect the metric
The various data sources using which you can perform predictive analytics
For example, you can consider various indicators such as social media posts, compensation, team size, client feedback, net promoter score and information from surveys.
2. Define the data sources
In the second step, it is necessary to define the data sources from which you could collect the data. Some examples include applicant tracking systems, employee scheduling software, talent management software and the HR module in enterprise resource planning (ERP) software. Since the data may contain confidential employee information, ensure that it is secure and restricted to certain individuals.
3. Decide the tool
After choosing the metrics to monitor and defining the data sources, the third step is to use a third-party tool or design in-house software. Several third-party vendors offer artificial intelligence (AI) based solutions for business planning, forecasting, talent acquisition and predictive analytics. These tools are preferable for their ease of use, scalability and efficiency.
Since third-party solutions may require complex customising, several companies may prefer using in-house software. In-house HR systems allow companies to design systems that meet their unique needs and make adjustments as needed. The predictive model of in-house software uses company data as its input, which provides better predictions.
4. Develop the application
If a company decides on using in-house software for HR analytics, the team involved in the development can include:
HR manager: The HR manager is responsible for the development and maintenance of HR operations and processes. They also select indicators and metrics for HR systems.
Data engineer: Data engineers build a pipeline to streamline the data.
Data analyst: Data analysts define ways to retrieve data from various sources, perform data analysis using various statistical techniques and present their findings to the management.
Data scientist: Data scientists build models using various techniques, such as data pre-processing, machine learning and deep learning, to make accurate predictions and forecasts.
ETL developer: Extract, transform and load (ETL) developers manage the extraction, transformation and loading of data.
Front-end developer: Front-end developers build the user-side of the application and ensure that the interface is easy-to-use and interactive.
UI and UX designer: UI designers ensure that the interactions with the product are efficient and optimal. UX designers focus on prototyping, styling, usability, testing and branding of the in-house software.
5. Train employees
After the development of the in-house software, it is essential that the HR team is comfortable using the tool. Companies can conduct training sessions where users get hands-on experience using the software. Detailed documentation with clearly defined steps, screenshots and video tutorials can help non-technical users get acquainted with the software. If a company prefers using third-party tools, users can review documentation and tutorials provided by the vendor to get familiar with the software.
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