Use Cases

May 27, 2024

How Infosys Utilizes HR Analytics to Build a World-Class Workforce

Here's a step by step Power BI use case guide on Workforce Analysis.

Let's start with a bit of background on Infosys. It's one of the leading global consulting and IT services companies, with a storied legacy dating back to 1981. Over the decades, Infosys has grown into a multinational powerhouse, serving clients across 50 countries and employing over 300,000 professionals worldwide.

That's an incredible journey, right? And a big part of their success story is their focus on building a world-class workforce through advanced HR analytics. By collecting and analyzing vast amounts of employee data, Infosys has been able to uncover valuable insights into talent acquisition, retention, and development strategies.  

So, how did they achieve this? By embracing the power of HR analytics and using it to shape a highly engaged, productive, and future-ready workforce – one that is well-equipped to drive innovation and deliver exceptional value to their clients.

About Dataset

The HR employee dataset offers a comprehensive view of the workforce, capturing demographics (Age, Gender, Marital Status, Education Field), job details (Department, Job Role, Job Level, Distance From Home), satisfaction metrics (Job Satisfaction, Environment Satisfaction, Relationship Satisfaction, Work Life Balance), and tenure information (Years At Company, Years In Current Role, Years Since Last Promotion, Years With Curr Manager).

Transaction Information
Column Group Column Name Data Type Description
Demographics Age Int Age of each employee
Demographics Gender Varchar Gender of employee
Demographics Marital Status String Marital status of employee
Demographics Education Field String Education field of employee
Job Details Job Role String Designation of employee
Job Details Department String Located Department
Job Details Job level Integer Level Of job
Job Details Distance from Home Integer Distance from home
Satisfaction Metrics Job Satisfaction Integer Job satisfaction score
Satisfaction Metrics Environment Satisfaction Integer Environment satisfaction score
Satisfaction Metrics Relationship Satisfaction Integer Relationship satisfaction score
Satisfaction Metrics Work life balance Integer Work life balance score
Tenure Information YearsAtCompany Integer Years worked for a company
Tenure Information YearsInCurrentRole Integer Working years for current role
Tenure Information YearsSinceLastPromotion Integer Years from last promotion
Tenure Information YearsWithCurrManager Integer Years of working with current manager

This rich dataset enables analysis of attrition patterns across various segments, identification of high-turnover areas, and understanding of drivers impacting employee engagement, job satisfaction, and retention. With these insights, HR professionals can develop data-driven strategies to foster a stable, satisfied, and high-performing workforce. Dataset consists of 35 columns and 1470 rows.

How Infosys Utilizes HR Analytics to Build a World-Class Workforce?

As a data analyst, it is crucial to address extensive datasets such as HR dataset. We have developed a five-step plan focused on understanding the key aspects of HR data analysis.

By leveraging HR analytics, It is possible to spot patterns in employee turnover and retention issues in different departments and demographics. This helps HR professionals customize retention efforts, improve engagement tactics, and enhance workforce stability by identifying high attrition risk areas. By using data to analyze employee departures, we can improve job satisfaction and create a more engaged and dedicated workforce in line with organizational goals.

Step 1: Identify the user or stakeholder for dashboard

The first step involves determining the intended recipients of the data, in this scenario, an individual known as an HR Manager. The HR Manager handles numerous responsibilities like overseeing recruitment and onboarding, creating pay structures, setting up employee benefits, ensuring adherence to labor laws, and promoting employee welfare, employee attrition and involvement. 

Stakeholders need in-depth data to make well-informed choices, such as details on employee demographics, turnover rates, and market trends for talented individuals. They face difficulties like attracting and keeping top talent, managing employee interactions, following employment rules, upholding a healthy company culture, Managing attrition and resolving workplace issues.

Step 2: Design Empathy Map

To truly connect with the experiences and expectations of a HR Manager, the creation of an empathy map is invaluable. This visual tool allows for a deeper understanding of the emotions, aspirations, and pain points of users. 

By empathizing with their perspectives, we can design a data story that not only meets functional requirements but also resonates with the human elements of their roles.

Step 3: Identify the Key Performance Indicators (KPI’s)

Once an empathy map is ready. As a data analyst, you need to decide on the most important things to keep an eye on, called KPIs (Key Performance Indicators).The heartbeat of any analysis lies in KPI’s and their Metrics. It's crucial to identify the KPIs that matter most to achieving the defined objectives and by focusing on the most relevant metrics, organizations can gain actionable insights into HR Analytics.

Transaction Information
KPI Formula Description
Employee Turnover Rate Number of employees left / Total number of employees Measures the rate of employee attrition, which can impact productivity and workforce stability.
Attrition Rate by Department Number of employees left in a department / Total employees in that department Helps identify departments with high attrition rates that may require intervention.
Regrettable Attrition Rate Number of high-performers or critical employees left / Total number of employees left Tracks the loss of top talent, which can significantly impact the organization.
Cost per Hire Total recruitment costs / Number of hires Measures the average cost associated with hiring new employees, helping to optimize recruitment budgets.
Training Effectiveness Performance improvement after training Evaluates the impact of training programs on employee performance and skill development.
Training Completion Rate Number of employees completing training programs / Total number of employees enrolled Measures the effectiveness of training and development programs.

Step 4: Understand the Goals & Objectives of User

Based on the Empathy Map and KPIs, it is essential to establish the goals and objectives of the Users. This will help align with the data story functionalities for effective decision making. Here are the Key Objectives & Goals:

Objective :

Analyze the organization's human capital management practices to reduce employee turnover, improve recruitment, and support a skilled workforce. Use data to understand attrition rates, recruitment costs, training impact, and program success. The aim is to find areas for improvement and introduce strategies that boost talent retention, growth, and organizational performance.

Goal :

The main goals are to reduce employee turnover rates significantly and consistently, especially in departments experiencing high attrition. It is important to avoid losing top-performing employees and key talent. The aim also to improve recruitment procedures, cut costs, and use new methods to find the right talent efficiently. Additionally, the focus is on improving training quality to boost performance and ensure high completion rates for required programs, all aimed at developing a skilled and motivated workforce for long-term success.

Step 5 : Ask Business Questions

Beyond KPI’s, organizations must engage in business-driven inquiry. This involves asking strategic questions that directly align with overarching business objectives.

1. Which departments are experiencing the highest & lowest attrition ?
  • Out of the total 237 employee departures, the R&D department has experienced the highest attrition rate at 56% (133 employees), followed by Sales (39%, 92 employees) and HR (5%, 12 employees).

Visualization :

SQL Code :
    COUNT(*) AS TotalEmployees,
    SUM(CASE WHEN Attrition = 'Yes' THEN 1 ELSE 0 END) AS AttritionCount,
    (SUM(CASE WHEN Attrition = 'Yes' THEN 1 ELSE 0 END) * 100.0 / COUNT(*))    AS AttritionRate
    AttritionRate DESC;

2. What is the breakdown of total employees, active employees, and employees who have left the company by gender?
  • Out of a total workforce of 1470 employees, 1233 remain actively employed. This means 237 employees have left the company, with a breakdown of 150 males and 87 females.

Visualization :

SQL Code :
    COUNT(*) AS TotalEmployees,
    SUM(CASE WHEN Attrition = 'No' THEN 1 ELSE 0 END) AS ActiveEmployees,
    SUM(CASE WHEN Attrition = 'Yes' THEN 1 ELSE 0 END) AS LeftEmployees

3. Is there a correlation between employee work experience and attrition?
  • A noticeable decrease in departures is observed as experience increases: 182 for 0-10 years, 39 for 11-20 years, 11 for 21-30 years, and just 5 for 31+ years. This indicates the need to focus retention efforts on employees in their initial ten years with the company.

Visualization :

SQL Code :
        WHEN TotalWorkingYears BETWEEN 0 AND 10 THEN '0-10 years'
        WHEN TotalWorkingYears BETWEEN 11 AND 20 THEN '11-20 years'
        WHEN TotalWorkingYears BETWEEN 21 AND 30 THEN '21-30 years'
        ELSE '31+ years'
    END AS ExperienceRange,
    COUNT(*) AS AttritionCount
    Attrition = 'Yes'

4. How many employees are there in each age group, and how is the gender distribution within each age group?
  • The workforce is diverse in terms of age groups. Employees under 18 are evenly split between 4 males and 4 females.
  • The 19-30 age group has the highest number of employees at 378, with 236 males and 142 females. The 31-40 age group closely follows with 619 employees, consisting of 370 males and 249 females.
  • In the 41-50 age bracket, there are 322 employees, including 193 males and 129 females. Employees over 50 total 161, with 97 males and 64 females.

Visualization :

SQL Code :
        WHEN Age < 18 THEN 'Under 18'
        WHEN Age BETWEEN 19 AND 30 THEN '19-30'
        WHEN Age BETWEEN 31 AND 40 THEN '31-40'
        WHEN Age BETWEEN 41 AND 50 THEN '41-50'
        ELSE 'Over 50'
    END AS AgeGroup,
    COUNT(*) AS TotalEmployees,
    SUM(CASE WHEN Gender = 'Male' THEN 1 ELSE 0 END) AS MaleEmployees,
    SUM(CASE WHEN Gender = 'Female' THEN 1 ELSE 0 END) AS FemaleEmployees

5. Are there specific job roles with higher attrition rates, and how does job satisfaction play a role?

Examining data based on job role and job satisfaction ratings can offer valuable insights into possible areas for enhancement.

  • For example, in the laboratory technician position, there appears to be a greater number of departures among employees with lower satisfaction ratings (20 with a rating of 1, 8 with a rating of 2).
  • Conversely, individuals with higher satisfaction ratings exhibit lower turnover rates (21 with a rating of 3, 13 with a rating of 4).
  • This pattern might be present across other job roles as well, highlighting the potential impact of job satisfaction on employee retention.

Visualization :

SQL Code :
    COUNT(*) AS TotalEmployees,
    SUM(CASE WHEN Attrition = 'Yes' THEN 1 ELSE 0 END) AS AttritionCount,
    ROUND(AVG(JobSatisfaction), 2) AS AvgJobSatisfaction
    AttritionCount DESC, JobRole;


  • Attrition by Department: The R&D department has the highest attrition rate at 56%, followed by Sales at 39% and HR at 5%. Further exploration is necessary to understand the reasons behind the high attrition in R&D and Sales.

  • Total Turnover Rate: The company's overall turnover rate is 16%. Comparing this rate with industry standards can offer valuable context.

  • Gender Discrepancy: Although more male employees left (150 compared to 87 females), a more thorough analysis is recommended to ascertain if attrition rates vary by gender when considering the total workforce size for each.

  • Experience and Attrition: There is a significant connection between experience and attrition. Employees with 0-10 years of experience have the highest departure rate at 182, underscoring the need for focused retention efforts for newer employees.

  • Workforce Composition: The employee population spans a wide age range, with the most significant concentration in the 31-40 age group consisting of 619 employees.

  • Job Satisfaction and Attrition: The instance of laboratory technicians indicates a relationship between job satisfaction scores and attrition rates. Examining this pattern across all job roles can guide strategies to enhance employee satisfaction and decrease turnover.

These discoveries establish a solid groundwork for HR to create targeted initiatives that tackle high-attrition departments, boost employee engagement at all experience levels, and ultimately contribute to a more stable and productive workforce.


The HR Analytics dashboard uncovered important information. The R&D and Sales departments experience the most employees leaving, especially among new hires with 0-10 years of experience. A connection between job satisfaction and employee turnover was noticed among laboratory technicians, indicating a need to investigate this pattern in all positions.

Taking action on these insights through focused retention programs, engagement efforts, and potentially improved recruitment strategies can help HR create a more robust and dependable workforce.

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