About the project
Scenario
Imagine a company with a diverse workforce and a wide range of products and services. The company aims to enhance its compensation strategy to ensure all employees receive fair and effective compensation. However, the company lacks updated information on potential disparities in its current compensation.
How can the company enhance its compensation strategy to ensure fair and effective compensation for all employees, given that it lacks updated information on potential disparities in its current compensation strategy?
Aim of this project
✅ To investigate how compensation differs based on various factors, such as the role an employee occupies, the department in which they work, the level of experience, and the demographics.
This may help to inform the development of a fair and effective compensation strategy.
A fair and effective compensation strategy can help attract and retain top talent, improve employee morale and motivation, and ultimately drive business success.
✅ To develop a tailor-made dashboard that allows to personalize the reporting based on specific needs of stakeholders instead of having a generic dashboard for everyone.
- This can help to focus on specific groups, characteristics, and factors and bring to attention what interested people should focus on, what matters, and what to do about it.
Research Questions
How did the last salary increase vary across departments, roles, experience levels, generations, and genders?
How is compensation distributed across different departments, roles, experience levels, generations, and genders?
How does compensation compare across different departments, roles, experience levels, generations, and genders?
Specifics
This project utilized open-source data for analysis and answering the questions.
I performed a descriptive analysis to explore and compare compensation differences across various factors.
Data collection
- Data is sourced from the book: The Fundamentals of People Analytics With Applications in R
Packages
Dashboard interface - this dashboard is built with Quarto Dashboards.
Data source - the peopleanalytics package
Visualization - the ggplot and the plotly package for the plots
Data manipulation - the tidyverse package
Tables - the DT package
For any question or feedback, you can contact me on Linkedin.
| Variable of interest | Variable Name (data set) | Description of Variable | Data type |
|---|---|---|---|
| Compensation | annual_comp | Hourly rate * 2080 | Continuous |
| Role | job_title | Job title | Categorical |
| Department | dept | Department of which an employee is a member | Categorical |
| Experience | job_lvl | Job level, where 1 = Junior and 5 = Senior | Categorical |
| Salary Increase | salary_hike_pct | The percent increase in salary for the employee’s most recent compensation adjustment (whether due to a standard merit increase, off-cycle adjustment, or promotion) | Continuous |
| Age | age | Employee age in years | Continuous |
| Gender | gender | Gender self-identification | Categorical |
