For this project, I utilized a dataset from Kaggle to explore the relationships between employee performance, satisfaction, and retention. Using Power Query for data transformation and Power BI for data visualization, I generated actionable insights to support informed workforce decision-making.
The dataset contains 100,000 records that capture key aspects of employee performance, productivity, and demographics. It includes information regarding employee roles, departments, education levels, and tenure, along with work-related factors such as hours worked, projects handled, and overtime. The dataset also measures performance and engagement through metrics such as performance scores, training hours, promotions, and employee satisfaction. Compensation data, including monthly salary, is provided and correlated with job role and performance. Additionally, it supports workforce analysis by including a resignation indicator, enabling insights into employee retention and overall organizational effectiveness.
The overall objective of this analysis is to identify key factors that influence employee performance, satisfaction, and retention. It also aims to uncover patterns that can inform effective workforce strategies. By examining relationships between workload, compensation, training, and employee engagement, this project aims to highlight actionable opportunities for improving organizational performance and reducing turnover. Key metrics such as retention rate, average performance score, and employee satisfaction were analyzed to provide a comprehensive view of workforce dynamics. The insights derived from this analysis are intended to support data-informed decision-making and contribute to more strategic, employee-focused business practices.
I initiated the data transformation process by importing the raw dataset into Power Query and addressing missing values by removing incomplete records to ensure data quality and consistency. I also removed all duplicate rows. I proceeded to make sure that each column had the correct data type. I specifically converted Hire_Date to Date format by modifying the column’s data type from Date/Time to Date. I also transformed the Remote_Work_Frequency values by dividing them by 100 to convert them from whole numbers into decimal format. This allowed the values to be converted from decimal form into percentage format for accurate analysis and visualization. Lastly, Monthly_Salary was converted into currency format to ensure consistent and accurate financial representation. The dataset was now ready for analysis and visualization, although more extensive preprocessing and data quality adjustments would have been required if the dataset had been less structured.
This dashboard presents a comprehensive analysis of workforce dynamics by examining the interconnected relationships between employee performance, satisfaction, and retention across multiple organizational dimensions. At an aggregate level, the organization demonstrates a strong overall retention rate, indicating a relatively stable workforce. However, when broken down by department, attrition appears fairly consistent, suggesting that turnover is not isolated to a single functional area but may instead be driven by broader organizational factors such as workload, engagement, or career development opportunities. Additionally, performance levels remain relatively stable across departments and roles, indicating a balanced distribution of productivity throughout the organization. By incorporating key metrics such as average performance score, satisfaction levels, and compensation, the dashboard provides a holistic view of workforce health and establishes a strong foundation for deeper analysis.
A more detailed exploration of performance drivers reveals that training hours, while an important investment in employee development, do not show a strong direct correlation with higher performance scores. This suggests that training alone may not be sufficient to significantly enhance employee productivity and that other variables, such as job role complexity, prior experience, or intrinsic motivation, may play a more substantial role. Similarly, analysis of workload indicators, including projects handled and overtime hours, highlights nuanced patterns in employee outcomes. While higher project involvement may align with experienced or high-performing employees, increased overtime appears to be associated with slightly lower satisfaction levels, indicating a potential trade-off between productivity and employee well-being. These findings underscore the importance of balancing performance expectations with sustainable workload management to maintain both efficiency and morale.
Retention trends further reinforce the importance of a multi-dimensional approach to workforce strategy. Analysis by tenure shows that attrition is not limited to early-stage employees but also occurs among more tenured individuals, suggesting potential challenges in long-term engagement, career progression, or organizational alignment. Additionally, the relationship between satisfaction and attrition highlights that employees with lower satisfaction scores are more likely to leave, emphasizing the critical role of employee experience in retention efforts. Compensation analysis, while generally aligned with performance and role, may also contribute to retention outcomes when viewed alongside satisfaction and promotion opportunities. Overall, this dashboard demonstrates how integrating performance, engagement, and retention data can uncover meaningful patterns that inform strategic decision-making, enabling organizations to proactively address workforce challenges, enhance employee experience, and drive long-term organizational effectiveness.