- 📊 Dashboard Preview
- 📖 Project Overview
- 🎯 Business Problem
- ❓ Business Questions
- 🗂️ Dataset Information
- 🛠️ Tools & Technologies
- 🧹 Data Cleaning & Preparation
- ⚙️ Data Analysis Process
- 📋 Interactive Dashboard Visualization
- 💡 Key Findings
- 📣 Recommendations
- 🏁 Conclusion
- 👨💻 Author Information
This data analytics project examines workforce performance, employee engagement, and training effectiveness within a multinational company. A raw workforce dataset was cleaned, analyzed, and visualized through interactive dashboards built in Microsoft Excel. The project aimed to assess the impact of employee development initiatives and uncover insights to support data-driven workforce management decisions.
The company faced difficulties in effectively tracking workforce performance, employee engagement, job satisfaction, and training outcomes. As the organization expanded, the lack of a centralized and data-driven monitoring system limited management’s ability to accurately evaluate workforce productivity and employee development initiatives.
These operational gaps could increase the risk of creating an ineffective reward structure in which high-performing employees could be overlooked, potentially reducing morale, productivity, and long-term employee retention.
In addition, the company’s investment in employee training and development programs lacked a structured analytical framework to identify underperforming teams or employees who would benefit most from such initiatives. As a result, training resources and development investments could be allocated inefficiently, reducing their overall impact.
To address these challenges, key data analytics techniques including data cleaning, data analysis, and dashboard reporting were applied to generate actionable insights, conclusions, and business recommendations that support informed decision-making and better performance.
Based on the company's business challenges, the following key questions were developed to guide the analysis and uncover actionable insights:
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How is the workforce distributed across departments, business units, age groups, and gender demographics?
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Which departments exhibit the highest and lowest levels of employee performance?
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How do employee engagement, job satisfaction, and work-life balance vary across the organization?
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How are training resources and development investments allocated across departments, and do they align with workforce needs?
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Which training programs achieve the highest participation levels, and where should future training investments be prioritized to maximize impact?
The dataset contains a wide range of employee-related records and attributes, comprising 28 columns and 2,845 rows of data. The data include key information such as;
- Employee ID
- Division
- Performance Score
- Current Employee Rating
- Engagement Score
- Satisfaction Score
- Work-Life Balance Score
- Training Outcome
- Training Cost
- Age
- 📊 Microsoft Excel: For data cleaning and reporting.
- 📑 Pivot Tables: – For data aggregation and summarization.
- 📈 Pivot Charts: For data visualization and reporting.
- 📋 Dashboard Design: For interactive dashboards.
- 🎯 KPI Cards: For tracking key performance metrics.
- 🎛️ Slicers: For interactive dashboard filtering.
- 💻 Visual Studio Code: For project documentation, README development, and Git integration.
- 🗂️ Git & GitHub: For version control and portfolio presentation.
- Removed duplicate records to ensure data accuracy and consistency.
- Converted four date fields to a standardized short-date format.
- Checked for and verified missing values across the dataset.
- Standardized text values to maintain consistency in data entries.
- Validated the Age column by cross-checking employee ages against their dates of birth.
Six calculated columns were added to the existing columns to provide deeper insights into the workforce and training performance of the company. A step-by-step explanation of how these calculations were carried out is presented below.
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Age Bracket
This is the first of six calculated columns created to improve the analysis. The IFS function was used to categorize employees into six age groups based on their age values.- 13 - 19 (Teenager)
- 20 - 29 (Younger Adult)
- 30 - 39 (Adult)
- 40 - 49 (Middle Age)
- 50 - 59 (Older Adult)
- 60 + (Senior)
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Tenure
To evaluate employee experience levels, this column calculates each employee’s length of service with the organization based on the employment start date. The DATEDIF function was used to extract both the number of years and the remaining months in decimal form, which was then formatted in years. -
Engagement Level
This column was created to group employees into three levels based on their engagement scores. Using the IF function, employees with a score of 5 or higher were classified as "High", those with scores between 3 and 4 were classified as "Medium", while the remaining employees were grouped as "Low". -
Satisfaction Level
Similarly, the satisfaction level column was created using satisfaction scores to group employees into three categories. With the IF function, employees with a score of 5 or higher were classified as "Satisfied", those with scores between 3 and 4 were classified as "Neutral", while the remaining employees were classified as "Unsatisfied". -
Performance Category
The performance category column followed the same approach, with values calculated based on employee performance scores. Using the IF function, employees who scored 5 or higher were classified as "High Performer", those with scores between 3 and 4 were classified as "Average Performer", while the remaning employees were grouped as "Low Performer". -
Training Efficiency
Following management’s investment in employee training, it was important to evaluate the effectiveness of these capacity development programs. To achieve this, the training outcome and employee current rating columns were combined using a Nested IF function to calculate this column. Employees who "Passed" the training and received a rating of 4 or higher were classified as "Highly Effective", those who "Completed" the training with ratings of at least 3 were classified as "Effective", while the remaining employees were categorized as "Needs Improvement".
The analysis of the dataset was primarily conducted using Pivot Tables in Microsoft Excel. This tool played a key role in aggregating and summarizing important workforce KPIs, including employee distribution by gender, age group, department, and business unit. Pivot Tables also enabled efficient analysis of employee performance, engagement, satisfaction levels, and training outcomes.
Furthermore, the tool made it easier to identify trends, compare performance across different groups, and generate further insights. Its flexibility and interactive features allowed for quick filtering, sorting, and summarization of large volumes of employee data.
As mentioned earlier, Pivot Tables were central to the insights generated in this project. They enabled the data to be analyzed and summarized efficiently before the results were used for dashboard development, as they can be seen in the screenshots below.
To visualize the analysis, three professional dashboards were built entirely in Excel, with each dashboard serving a distinct purpose related to the project’s business questions.
- Workforce Overview Dashboard
- Employee Performance & Engagement Dashboard
- Training & HR Investment Dashboard
This dashboard focuses on workforce distribution and employee demographics across the organization. It helps management understand employee structure, gender diversity, department size, and workforce composition.
- KPI Cards
- Total Employees
- Average Employee Age
- Number of Male and Female Employees
- Average Tenure
- Charts
- Employee Count by Department
- Gender Distribution
- Employee Count by Business Unit
- Workforce Distribution by Age Group
- Slicer
- Department
This dashboard focuses on employee performance, engagement, satisfaction, and work-life balance. It helps management identify high-performing departments, employee morale levels, and areas requiring HR attention.
- KPI Cards
- Average Engagement Score
- Average Satisfaction Score
- Average Work-Life Balance Score
- Average Employee Rating
- High Performer Count
- Charts
- Performance Score by Department
- Engagement Level Distribution
- Satisfaction Score by Division
- Top Performing Departments
- Slicer
- Business Unit
This dashboard analyzes employee training activities and HR investment. It helps management evaluate training participation, training costs, and the effectiveness of workforce development programs.
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KPI Cards
- Total Training Cost
- Total Training Programs
- Training Completion Rate
- Average Training Duration
- Successful Training Count
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Charts
- Training Program Attendance
- Training Cost by Department
- Training Outcome Analysis
- Training Type Performance
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Slicer
- Training Program Name
The analysis revealed that the company employs 2,845 people across six departments and ten business units, with females representing the majority of the workforce. The Production department dominates the workforce, accounting for 67% of all employees, with staff relatively evenly distributed across the business units. The workforce is also notably mature, with an average employee age of 49 years and 34% of employees aged 60 or older. Combined with an average tenure of just five years, this raises potential concerns around succession planning, workforce sustainability, and the retention of institutional knowledge.
Employee performance was highest within the Administration Offices department, with Production and IT/IS also demonstrating strong performance relative to other departments. While 42% of employees were classified as having low engagement levels, most employees across all departments still met performance expectations. This suggests that employee engagement may be an area of opportunity for management, as improving engagement levels could increase the proportion of employees who exceed performance expectations, which currently stands at just 12%.
The company invested approximately $1.58 million in five training programs to support employee development. Participation was fairly balanced across the programs, although Communication Skills training attracted the highest attendance at 22%, closely followed by Project Management training at 21%. The remaining three programs each accounted for roughly 19% of total training participation, indicating broad interest across multiple areas of professional development.
Based on these findings, it is recommended that the company place greater emphasis on succession planning to ensure the transfer of institutional knowledge and maintain its long-term competitive advantage. Given the aging workforce, developing future leaders and implementing structured knowledge-sharing programs should be a strategic priority.
The company should also invest in employee engagement initiatives to address the high proportion of disengaged employees. Regular employee feedback surveys, recognition programs, career development opportunities, and improved communication channels can help identify and resolve issues that may be affecting morale and productivity.
Finally, management should evaluate the effectiveness of its training programs by measuring their impact on employee performance and engagement. This would help ensure that training investments are aligned with business objectives and delivering optimal returns.
This project has revealed the importance of Microsoft Excel and how the tool can be used to transform raw workforce data into meaningful business insights through data cleaning, calculated columns, Pivot Tables, and interactive dashboards. The analysis provided a comprehensive view of the company's workforce composition, employee performance, engagement levels, and training activities.
Key findings revealed an aging workforce, low employee engagement among a significant portion of staff, and varying levels of participation across training programs. While overall performance levels remained satisfactory, the results highlighted opportunities to strengthen succession planning, improve employee engagement, and maximize the return on training investments.
Segun Olakoyenikan
Data Analyst | Storyteller