This article provides a practical explanation of HR Analytics. After reading, you will understand the basics of this powerful human resource management tool.
What is HR Analytics?
HR Analytics is the application of data mining, statistics, analysis, and modelling of personnel data, the purpose of which is to improve productivity. HR Analytics is also called talent analysis or people analytics. It helps the human resource manager to make decisions based on data about employees, and it helps to recruit, retain, and train employees. If utilised efficiently, HR Analytics can have a major impact on business performance.
In this area of Human Resource Management (HRM), many analyses and studies are conducted that are relevant to employee performance. This involves recording a lot of data. It’s usually about a specific factor that is relevant to HRM. Examples can be productivity per employee, revenue per employee, training costs, and so on.
HR Analytics is not just about collecting data and data analytics on employee performance, but also tries to offer insight into different processes within the organisation. It does so by collecting information about the process. This information can then be used to improve the process with Business Process Re-engineering (BPM).
Value for the organisation?
Human Resource Management has access to very valuable data on employees. If used efficiently, this data can be used to enable change in the organisation and improve performance. The data is used to keep employee experience and satisfaction high, monitor performance, and draw conclusions about results.
The data can also be used to form recommendations about investments in training that will help employees develop the right competencies. Employees can be retrained for many different reasons. They might be getting a new job, or the revenue model of the organisation might be changing. The learned competencies will then be crucial for the new revenue model.
What does HR Analytics measure?
In order to be able to effectively offer insights to the organisation, one must determine which data has to be recorded to model possible outcomes and predict scenarios. This is a multi-disciplinary process that requires the presence of both the human resource manager and someone from management who makes strategy decisions. Based on the organisation’s overall strategy, a strategy will then be created for human resource management.
HR Analytics can then be used to suggest certain data that should be analysed based on Key Performance indicators (KPIs). Certain statistics that are used a lot in HR Analytics are:
Income per employee
The income per employee is calculated by dividing the company’s total revenue by the total number of employees. The result shows how much money is being generated per employee on average. It is a measurement that can be used to determine the efficiency of the organisation as a whole.
The training efficiency can be measured by conducting an analysis of multiple sets of data, such as test scores and performance improvements.
Training costs per employee
The training costs per employee are calculated by dividing the total training costs by the total number of employees a company has.
The turnover rate is the rate in which employees voluntarily decide to quit their jobs. The ratio can be calculated by dividing the total number of employees leaving the organisation by the total number of employees that work at the organisation.
The same principle can be applied to involuntary turnover rate. This is when an employee is fired.
Absence due to illness
Absence is an often measured statistic and for good reason. Absence due to illness tells you something about the productivity and is measured by dividing the number of missed days by the total number of working days. If there is little absenteeism, this can be an indication of satisfied employees. If it’s high, it can indicate stress, dissatisfaction or other negative factors.
Types of data in HR Analytics
The data that HR needs for the HR statistics mentioned above is gathered from internal and external sources.
Internal data specifically applies to data that has been obtained from the human resource department. The HRM system contains various data sets that are used for HR Analytics. Some examples of internal data are:
- Employee appointment
- Employee compensation
- Employee training history
- Employee performance reports
- Details regarding potential top employees
- Disciplinary action against employees
External data is obtained from links with other positions in the organisation. Data from outside HRM is important because it offers a general perspective. Some examples of external information an organisation can use for HR Analytics are:
To calculate the costs per employee for instance, HRM requires broad financial data. This information is requested from the financial department or the accountant.
Employees generate a lot of data over time that is constantly being recorded. Examples of data that might be available are from when they were initially approached for the job or when the employee applied. The social media channels of an employee are also sometimes checked, as well as the input they provide in feedback surveys.
Historical data is used to recognise patterns in the behaviour of employees in different times. Think for instance of global, economic, or political events. Such large-scale trends cannot be established only using internal information. An example of an event that changed the way employees thought about their jobs was the 2008 recession. This type of data helps companies predict how people will react in future situations.
HR Analytics in 4 steps
Developed and effective human resource departments have a large number of tools, procedures, rules, and frameworks at their disposal to analyse the results. In smaller organisations, however, the scale is often a lot smaller. To get started, you may follow the four steps below.
1. Create an action plan
The most important requirement for using HR Analytics to make progress is to define goals. What is the goal of the data analysis? What areas need to be investigated? Which systems and sources are necessary, and how can they be accessed? By answering these questions and including as many relevant matters as possible, a complete and focused action plan can be formed.
2. Collect data
The second step is actually gathering the data. Collect all the information that was asked in step 1. In order to get access to data such as absenteeism, the relevant departments will have to be contacted. Don’t forget to include all sources of information from the previous chapter. And don’t forget to consider the following:
- Employee privacy
- Employee permission
- IT Security
3. Combine skills and technology
Few people are able to zoom in on very complex data and subsequently perform thorough analyses. Supporting technologies can help. For example, these can easily process the date into manageable reports and visualisations. Afterwards, let the HR expert interpret the data.
4. Analyse and develop
Once the HR department has access to the sources of information and technologies, powerful and valuable insights will become clear for the entire company. These insights can help the organisation increase productivity and strive for future successes.
Advantages of HR Analytics
Employees make up a large proportion of any organisation. Companies are aware that the success of their organisation largely depends on its employees. HR analyses help the managers of these companies to make proper, immaterial decisions by providing:
- A better selection process
- Lower retention
- Process improvement
- Improved employee satisfaction
- Better staff planning
- Better trained personnel
Now it’s your turn
What do you think? Are you familiar with this explanation of HR Analytics? What do you think are some of the pros and cons of this human resource management tool? Does your workplace also record and monitor data regarding employee performance?
Share your experience and knowledge in the comments box below.
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- Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11.
- Rasmussen, T., & Ulrich, D. (2015). Learning from practice: how HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236-242.
- Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3-26.
- Lawler III, E. E., Levenson, A., & Boudreau, J. W. (2004). HR metrics and analytics–uses and impacts. Human Resource Planning Journal, 27(4), 27-35.
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