The Importance of HR Analytics

What is HR Analytics?

Within the domain of analytics, the Human Resource analytics (HR Analytics) field deals with people analysis and applying analytical processes to the human capital within an organisation. The overall objective is to improve employee performance and reduce employee attrition / increase employee satisfaction.

A quick example can explain the value-added nature of HR Analytics over HR Data and HR Metrics:

● HR Data: Employee attrition this year is 20 percent
● HR Metric: This is a 3 percentage point increase from last year
● HR Analytics: Improper appraisal process contributed 2 percentage points to the increase

Need for HR Analytics

The goal of HR Analytics is closely tied to the business goals. Need of planning and interpreting “people metrics” has rapidly grown in order to steer performance of employees, teams, and organisations. HR Analytics aims to provide insight into how to best manage employees and reach business goals. It is a value-added business partnership approach focused on improving HR functions. HR Analytics is playing a critical role in adding value to changing HR functions in organisations. But the challenge then is to understand HR Analytics in order to uncover meaningful insights and improve decision making about people. Thus, the importance of HR Analytics has been widely realized in the corporate world and in academia and/ or the research fraternity.

Utility of HR Analytics

  • RECRUITMENT: Target and select the best applicants and match them to the most appropriate roles.
  • PRIORITISING RESOURCES: Forecast workforce requirements in a more effective manner and deploy as efficiently.
  • FOCUS ON BUSINESS GOALS: Link workforce efforts to strategic and financial goals for improved business performance.
  • PRODUCTIVITY: Identify and focus on employee satisfaction and productivity measures.
  • RETENTION: Identify reasons for attrition and focus on high value employees who may be considering leaving for these reasons.
  • DEVELOPMENT: Establish more effective training and career development initiatives across the company.

In this manner, HR Analytics becomes a strategic and predictive tool that enables achievement of business goals

Techniques of HR Analytics

Descriptive Analytics

This is limited to describing what has happened in the past using HR data analytics tools. For example, analytics on attendance records, attrition rates, productivity, etc.

Predictive Analytics

Predictive analytics tries to forecast what can occur in the future based on past trend data. To take the example of attendance records, predictive analytics could show impact on production of rising truancy.

Golden Path Analytics
This takes predict analytics a step further. For example, if late attendance and truancy is identified as above average in a particular department then it can be narrowed down to a difficult department manager or supervisor. Once identified, corrective action can be taken.
Prescriptive Analytics
Prescriptive analytics refers to the corrective action and interventions for the situation that is required once the source of the deviation is identified.

HR Analytics Courses

HR Analytics is a dynamic field and academia has stepped up to train both researchers as well as HR professionals in the specific and specialised techniques that are used in this domain.
FORE School of Management, New Delhi conducts a Faculty Development Programme (FDP), which is designed with an aim to familiarise the participants with the conceptual underpinnings and importance of HR Analytics and its practical aspects. Along with it, develop knowledge in various statistical tools & techniques used for HR Analytics, and their applications. This program equips participants with hands-on experience on the application of analytical tools to analyse business situations with the help of lab-assignments. The course content includes:

● Overview of HR Analytics
● Vocabulary of HR Analytics
● Data, Data Source & Metrics
● Data visualization
● Analysing data using descriptive statistics
● Projecting & Predicting data using advanced statistical analysis.

The programme is delivered through a combination of:

● Interactive Lectures, Cases & Discussions, Activities
● Using tools for analysis: Excel, SPSS, R

For more details on the FDP, visit: