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HR Analytics

2 min read
December 4, 2025
HR Analytics

HR Analytics is a tool for organizations to optimize business operations and employee experience based on facts. HR Analytics distinguishes itself as a tool by using HR data to generate insights and recommendations. Given the impact employees have on executing organizational strategy, as well as the scale of personnel costs, HR Analytics is a discipline in which every modern organization should be proficient.

 

Why HR Analytics?

  • Organizations that base their decisions on data-driven insights perform better. Using data to make decisions about an organization’s human capital, organizational structure, and HR function performance is called Human Resource (HR) Analytics, or data-driven HR.
  • Since the success of many organizations is determined by the people who work there, and because the direct and indirect costs of employing these people make up a significant portion of expenses, it’s a field of vital importance for every organization.
  • Since the beginning of the 21st century, the field has undergone tremendous development.

What is HR Analytics?

The term ‘HR Analytics’ suggests that the field revolves around (statistical) analysis. However, this isn’t entirely accurate. The broader goal is to deliver relevant insights regarding human and organizational issues. Depending on the question and its context, different tools are employed. This could be a (statistical) analysis, but it could just as well be an employee survey or HR Dashboard. The goal isn’t necessarily to build a predictive model. The goal is to answer the question as effectively as possible by using the right HR data and tools according to the context.

Success with HR Analytics

Many organizations already have the right people to get started with this. Think of Business Intelligence Developers, Data Scientists, and Research Analysts. Yet many organizations fail to create tangible value with the HR Analytics efforts made by these people.

To make the transition to data-driven HR, more is needed than just hiring technical profiles. To be successful, it’s necessary to play chess on multiple boards!

 

Some examples:

  • It’s important to align HR Analytics efforts (supply) with line management demands (demand).
  • The right technology must be selected and activated.
  • An HR Analytics target operating model is needed, which must include the Translator role.
  • Data will need to be generated and administered.
  • Various matters regarding Privacy and Security will need to be arranged.
  • And finally, but not least importantly, HR professionals will need to be trained to work with the insights.

How do you apply HR Analytics?

Like Rome, success with HR Analytics isn’t built in a day. Its adoption is like a journey that starts with a goal and is then divided into stages.

Baseline Assessment

If an organization has serious plans, it’s best to start with a baseline assessment. Where does the organization stand in areas such as data, operating model, technology, privacy and security controls, leadership, skills, strategy/demand? And have any successes already been achieved?

Goal & Stages

Once there’s a clear picture of the starting point, a goal can be formulated, the journey can be divided into stages, and within those stages, an Agile approach can be used to work toward the completion of each HR Analytics stage.

HR Data Analysis Examples

  • A financial institution used HR Analytics during a reorganization to map employees to new roles, including a matching score. The use of HR analysis greatly accelerated this massive task and resulted in better matching. This specific form of HR data analysis is called prescriptive analytics, where smart algorithms make suggestions to humans (in this case, the HR professional).

  • A retailer uses HR analysis to develop smart engagement, turnover, and absenteeism standards for store managers. The algorithm considers factors such as team composition and time of year to indicate realistic engagement, turnover, and absenteeism levels for each manager. The major advantage of this smart standard-setting is that managers no longer receive meaningless general standards but can focus on realistic targets. And the smart standard enables HR professionals to coach managers who consistently fall below the smart norm.

  • A government institution uses predictive HR analytics to forecast whether they will meet their self-imposed diversity ambition for the male/female ratio. The analysis showed that despite considerable diversity and inclusion efforts, work remained to be done. This analysis resulted in renewed urgency for the topic and the implementation of additional initiatives.

  • Various healthcare institutions use diagnostic HR Analytics to identify which employee segments have an increased risk of absenteeism and the underlying causes. This form of HR Analytics helps them to target their available absenteeism budget effectively on causes and risk groups.

  • A financial institution uses HR Analytics to measure the effects of a program for management development. For example, does such a program lead to higher employee satisfaction among the employees of participating managers, versus those of managers who haven’t participated? Such HR Analysis helps HR eliminate interventions and programs that don’t add value.

  • A high-tech company uses HR Analytics process mining to map the flow of candidates through the recruitment process and internal mobility of employees. The insights from this HR analysis help optimize processes and correct stakeholders where the process isn’t being followed.

  • A building materials production company uses prescriptive analytics for talent identification. This way, fewer talents are overlooked, which contributes to turnover reduction and thus cost control.

HR Analytics Examples – Employee Survey

  • An automotive organization scans anonymized employee emails within GDPR rules and applies text analysis to determine employee sentiment in the organization. This method of employee research ensures that employees and managers aren’t disturbed in their work, while HR and Management can still monitor how sentiment develops and whether there are bottlenecks within specific employee segments or departments.

  • A financial institution categorizes cases logged by employees and managers to determine which Employee Journeys have many questions/challenges. This method of employee research ensures that employees and managers aren’t disturbed in their work, while HR can identify which Employee Journeys face challenges.

  • A retailer sends an online questionnaire within one day of resignation to every departing employee whose departure is not desired to understand why the employee is leaving and where they are going. This way, they gain insight into causes of unwanted turnover.

  • A waste management company conducts quarterly pulse surveys inquiring about engagement (eNPS), critical engagement drivers, and current themes. Through this research, the organization has managed to substantially improve employee engagement and provided its managers with a method to listen to employees and engage in dialogue.

  • A shared services organization used employee research to determine new cultural values and desired behavior, but also to guide the transition from current culture and behaviors to desired ones.

  • A Dutch municipality (top 10 city) used Highberg’s labor market panel combined with internal employee data to gain insight into young people’s perception of the municipality as an employer and the difference in perception between employees and outsiders.

  • A media company used an online survey to gain insight into its own diversity, employees’ views on the value of diversity and inclusion for the organization, and the extent to which the organization is inclusive.

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