🎯 What This Dashboard Represents:
This Employee Data Analysis Dashboard offers a visual overview of employee performance metrics like total hours worked, punctuality, departmental distribution, and location-wise contribution. It’s created using Power BI after performing key data transformation tasks with the Power Query Editor (PQE).
It helps managers and HR professionals quickly answer:
- Which employee worked the most?
- Which department logged the highest hours?
- How punctual are employees by department?
- What are the location-wise contributions?
Get the Dataset Here: https://github.com/slidescope/5-features-of-power-query-editor-on-EMployees-data/
🧰 Power Query Editor – 4 Data Transformation Features Used
Here’s what users will learn and apply before building this dashboard:
1. Column Split (Split Column by Delimiter):
Used to separate Name and EmployeeID (e.g., “Diana-E004” → “Diana” & “E004”).
Purpose: Improves clarity and usability in visualizations by creating individual fields for better filtering and sorting.
2. Change Data Types:
Ensures that HoursWorked is a decimal, Date is date type, and IDs are text.
Purpose: Prevents data type mismatch errors and improves aggregation accuracy.
3. Merge Queries:
Merged multiple tables like Employees, Departments, and Locations using EmployeeID or DepartmentID.
Purpose: Brings in all required fields from different sources into one unified dataset.
4. Pivot/Unpivot Columns:
Used to convert data formats (e.g., date-wise hours worked per employee to a single consolidated column).
Purpose: Makes the data model dashboard-ready and easier to visualize.
📊 Dashboard Components Explained:
| 📌 Section | 🔍 Explanation |
|---|---|
| Total Hours Worked by NameDep | A bar chart showing individual employees (Name + EmployeeID) and their total hours logged. |
| Total Hours by Department | Summarizes and ranks departments based on the cumulative working hours of all employees in them. |
| Total Hours by Location | Donut chart showing hours worked by employees based on geographic location (e.g., Chicago, Austin). |
| Employee Table | A sorted table with individual EmployeeIDs, names, and HoursWorked, with a visual mini bar chart. |
| Count by Punctuality | Pie chart showing count and % of employees who were on time vs late. |
| Department-wise Punctuality | Stacked bar chart to show punctuality status within each department. Helps spot culture trends. |
| Filters | Slicers for filtering data by Department, Location, and Date Range (01-06-2024 to 05-06-2024). |
📘 What You Will Learn by Building This Dashboard:
- How to clean and transform raw data using Power Query Editor
- How to create visualizations using Power BI
- How to combine multiple sources and shape data for analysis
- How to use charts for effective storytelling in employee performance
- Best practices for using filters and slicers for dynamic dashboards
🏁 Final Takeaway:
This project demonstrates how Power Query Editor (PQE) is crucial for preparing raw HR data and converting it into powerful insights. By mastering these 4 transformations, any beginner or intermediate user can create insightful dashboards for real-world business use cases.