HR DASHBOARD

Business Problem and Context: 

HR teams face significant challenges due to gaps in workforce data quality and accessibility, which hinders effective decision-making. Disparities in diversity representation, marital status trends, and gender equity are not fully understood, leading to missed opportunities for inclusion. Additionally, a high concentration of employees in specific departments and imbalanced age dynamics highlight potential risks in workforce planning and succession management. 

Project Objective: Develop a data-driven HR analytics solution to enhance workforce planning, improve diversity representation insights, and optimize succession management strategies. 

Data and Tools: 

Dataset: Modified HR dataset from Kaggle (DataSense Analytics)
Data Cleaning & Prep: Handling missing values, duplicates, and standardizing job roles
Exploratory Data Analysis (EDA):

• Analyzing racial demographics to identify representation disparities between White and Black or African American employees.

• Examining marital status distribution across single, married, and divorced employees.

• Evaluating workforce distribution by department, with a focus on the production department's resource concentration.

• Assessing workforce age groups, emphasizing the dominance of employees aged 36-45 and the underrepresentation of older groups (66-75).

• Reviewing gender distribution to highlight the female-to-male ratio and identify potential equity gaps.

Tools Used:

• Excel (Pivot Tables, Advanced Formulas, Data Visualization, and Dashboard Creation)

Key Findings and Business Insights

Insight 1: Demographic Disparity in Racial Representation Problem: The majority of the workforce is White (187 employees), with significantly lower representation of Black or African American employees (80), highlighting diversity gaps. 

Recommendation: Implement targeted diversity and inclusion hiring initiatives to improve representation across demographics.

Insight 2: Balanced Marital Status Distribution Problem: While single (137) and married employees (124) are nearly balanced, divorced employees form a much smaller group (30), showing an uneven marital distribution. 

Recommendation: Investigate potential factors influencing job satisfaction across marital groups to ensure inclusivity.

Insight 3: Workforce Heavily Concentrated in Production Problem: The production department holds 209 employees, indicating a significant resource focus but a potential over-reliance on a single area. 

Recommendation: Assess department-specific needs to optimize workforce allocation across other divisions.

Insight 4: Age Dynamics in the Workforce Problem: Younger employees, particularly aged 36-40 (86) and 41-45 (71), dominate the workforce. Older age groups, especially 66-75, are underrepresented with only 5-6 employees. 

Recommendation: Consider succession planning and mentoring programs to balance experience levels within the workforce.

Insight 5: Gender Composition Problem: Females form the majority of the workforce (176) compared to males (135), resulting in a 1.3:1 female-to-male ratio. 

Recommendation: Analyze roles and career progression paths to ensure gender equity in opportunities.

Impact & Next Steps 

What I learned:
How to turn raw HR data into actionable workforce insights
How workforce trends, such as demographics and department dynamics, influence decision-making
How to clean, analyze, and visualize real-world-like datasets

Next Steps:
Enhance dashboard with interactive features
Test predictive modeling for attrition risk

Take a look at the presentation below!
Click the link to view it on Google Slides for more details and a better viewing experience. 👇