Data Diagnostic

Key Performance Indicators

Process Overview

Finding

Recommendations

Attrition by Dept. Employees by Age Group Job-Satisfaction Matrix

Conclusion

Next Steps

Status of Highest Count Age-Group

Dataset

Project Objective

The purpose of this HR Analytics Dashboard is to monitor and analyze workforce metrics with a strong focus on employee attrition. It aims to help HR leaders and management identify patterns in employee turnover, evaluate job satisfaction, and highlight high-risk employee segments. By visualizing key metrics such as attrition by department, education field, gender, and age group, the dashboard supports data-driven decision-making for workforce planning and retention strategies.

The broader objective is to uncover the root causes of attrition and provide actionable insights into employee satisfaction and demographics. This ensures that HR teams can implement targeted retention policies, optimize hiring strategies, and improve employee engagement initiatives.

Project Objective

The purpose of this HR Analytics Dashboard is to monitor and analyze workforce metrics with a strong focus on employee attrition.

It aims to help HR leaders and management identify patterns in employee turnover, evaluate job satisfaction, and highlight high-risk employee segments.

By visualizing key metrics such as attrition by department, education field, gender, and age group, the dashboard supports data-driven decision-making for workforce planning and retention strategies.

The broader objective is to uncover the root causes of attrition and provide actionable insights into employee satisfaction and demographics.

This ensures that HR teams can implement targeted retention policies, optimize hiring strategies, and improve employee engagement initiatives.

Dataset Overview

The dataset consisted of HR records with the following key attributes:

🗂️ Column Names

Attrition
Business Travel
CF_age band
CF_attrition label
Department
Education Field
emp no
Employee Number
Gender
Job Role
Marital Status
Over Time
Over18
Training Times Last Year
Age
CF_current Employee
Daily Rate
Distance From Home
Education

🗂️ Column Names ...

Employee Count
Environment Satisfaction
Hourly Rate
Job Involvement
Job Level
Job Satisfaction
Monthly Income
Monthly Rate
Num Companies Worked
Percent Salary Hike
Performance Rating
Relationship Satisfaction
Standard Hours
Stock Option Level
Total Working Years
Work Life Balance
Years At Company
Years In Current Role
Years Since Last Promotion
Years With Curr Manager

Key Questions

5 key questions this analysis aim to address:

What is our overall attrition rate, and how many employees are leaving the company?
(Answered by the headline metrics – Attrition #, Attrition Rate, and Active Employees.)
Which departments and education fields experience the highest attrition?
(Attrition by Department and Education Field breakdowns.)
Are certain age groups or genders more prone to attrition?
(Attrition by Age Group and Gender–Age cross-sections.)
How does job satisfaction vary across different roles, and does it link to attrition risk?
(Job Role vs. Job Satisfaction heatmap.)
What are the demographic characteristics of our workforce, and do they correlate with turnover?
(Employee distribution by Age Group, Gender, and Education Field.)

Data Diagnostic

📊 Dataset Overview

Total Records: ~1,000+ employees
Employee Status: Includes both Current Employees and Ex-Employees
Purpose: Suitable for HR analytics, attrition modeling, and workforce planning

👥 Demographics

Age Bands: Under 25, 25–34, 35–44, 45–54, Over 55
Gender: Male and Female
Marital Status: Single, Married, Divorced

🎓 Education

Levels: High School, Associates, Bachelor’s, Master’s, Doctoral
Fields: Life Sciences, Medical, Marketing, Technical Degree, Human Resources, Other

🏢 Job Information

Departments: R&D, Sales, HR
Roles: Research Scientist, Sales Executive, Manager, Laboratory Technician, etc.
Job Level: 1 to 5
Business Travel: Travel_Rarely, Travel_Frequently, Non-Travel
OverTime: Yes/No

💰 Compensation

Monthly Income: $1,200 to $19,999+
Hourly Rate: Up to 100
Stock Option Level: 0 to 3
Percent Salary Hike: Up to 100%

📈 Performance & Satisfaction

Ratings: Job Satisfaction, Performance, Environment, Relationship (Scale 1–4)
Work-Life Balance: Scale 1–4
Training Times Last Year: 0 to 9

⏳ Tenure & Experience

Years at Company: 0 to 37
Years in Current Role: Up to 33
Years Since Last Promotion: Up to 16
Years with Current Manager: Up to 15
Total Working Years: Up to 40+

🔍 Attrition Analysis

Attrition Label: Yes (Ex-Employees), No (Current Employees)
Correlated Factors: OverTime, Travel Frequency, Job Satisfaction, Income

Key Performance Indicators

Process Overview

The process involves collecting employee records from HRIS or payroll systems, cleansing the data, and categorizing employees into age bands, gender, education fields, and job roles. Calculated fields such as attrition rate, years since last promotion, and working years are derived for further analysis.
Tableau is then used to create an interactive dashboard with KPIs, charts, and filters. Visuals such as pie charts, bar graphs, and heat maps allow HR professionals to quickly identify problem areas. The dashboard also enables filtering by education level for deeper drill-downs.

Finding
Attrition is highest in the Research & Development (56%) and Sales (39%) departments, showing key risk areas. The Life Sciences education field contributes the most to attrition, followed by Medical. Males have slightly higher attrition compared to females.
From an age perspective, employees between 25–34 years old account for nearly 30% of total attrition, suggesting mid-career employees are more likely to leave. Job satisfaction scores vary, but Sales Executives and Lab Technicians show higher dissatisfaction, which aligns with their attrition contributions.

Recommendations

Retention Programs for Mid-Career Employees (25–34 yrs): Create mentorship, leadership training, and career progression initiatives to address turnover in this critical segment.
Focus on R&D and Sales Departments: Offer tailored retention strategies such as flexible work, incentive structures, and career advancement programs.
Boost Job Satisfaction in At-Risk Roles: Conduct role-specific surveys for Sales Executives and Lab Technicians and address top concerns such as workload, compensation, or career mobility.
Education-Specific Interventions: Engage employees from Life Sciences and Medical backgrounds through skill development or specialized career tracks to increase loyalty.
Gender Balance Monitoring: Though not significantly skewed, HR should ensure equitable policies and development opportunities for both genders to reduce attrition risk further.

Attrition by Dept. Employees by Age Group Job-Satisfaction Matrix

Conclusion
The dashboard provides strong visibility into workforce health, highlighting attrition hotspots by age, department, and education.
The overall attrition rate of 16.12% is a red flag, particularly given the concentration in R&D and Sales functions, which are critical to business growth.
By leveraging these insights, HR leaders can develop targeted retention strategies, improve employee engagement, and align workforce policies with business needs. Continuous monitoring of satisfaction, promotion cycles, and workload balance will ensure the organization can proactively reduce attrition and retain top talent.

Next Steps

1. Deeper Diagnostic Analysis

Conduct correlation and regression analysis to identify which factors (age, tenure, overtime, satisfaction, etc.) are most strongly linked to attrition.
Segment attrition by high performers vs. low performers to see if critical talent is leaving disproportionately.

2. Predictive & Prescriptive Modeling

Build an attrition prediction model (using logistic regression, decision trees, or ML in Python/R/Power BI).
Identify “at-risk” employees based on characteristics like overtime, years at company, job role, or satisfaction scores.
Simulate what-if scenarios (e.g., “If we improve job satisfaction by 10%, how much attrition could drop?”).

3. Actionable HR Interventions

Recommend targeted retention strategies for the most vulnerable groups (e.g., Sales, early-career employees, specific education backgrounds).
Conduct employee feedback surveys tied back to dashboard insights to validate reasons for leaving.
Design cost-benefit analysis of retention programs vs. cost of turnover.

Status of Highest Count Age-Group

Dataset

HR Data

Page updated

Google Sites

Report abuse