1 hidden cell
**Report on Data Cleaning and Power BI Dashboard Development
Prepared by: Favour EgberikeDate: 13/03/25**
1. Introduction
This report outlines the data cleaning process and the development of an interactive Power BI dashboard for a call center dataset. The goal of this project is to create a visually appealing and insightful dashboard that highlights key performance indicators (KPIs) and business insights, thereby attracting high-paying clients and potential employers.
2. Data Cleaning Process
The raw dataset contained inconsistencies that required preprocessing before analysis. The following steps were taken to clean and prepare the data:
Date and Time Formatting:
Converted the 'Date' column to a standard Date format. Converted the 'Time' column to a Time format and combined it with 'Date' to create a new 'Datetime' column for better analysis.
Data Type Corrections:
Ensured numerical columns such as 'Speed of answer in seconds' and 'Satisfaction rating' were correctly formatted as numerical values.
Handling Missing Values:
Filled missing values in 'Speed of answer in seconds' and 'Satisfaction rating' using the median to maintain data integrity.
Column Optimization:
Removed redundant columns such as 'AvgTalkDuration' and 'Column1', which were not contributing to meaningful insights.
3. Power BI Dashboard Development
To create an interactive and insightful dashboard, the following elements were implemented:
3.1 Key Performance Indicators (KPIs)Displayed in a card format at the top of the dashboard:
Total Calls Handled Average Speed of Answer Customer Satisfaction Score Call Abandonment Rate
3.2 Trend AnalysisCalls Over Time: A line chart showing daily, weekly, and monthly call trends to identify peak periods.
Agent Performance: A bar chart visualizing calls handled per agent and their satisfaction ratings. Call Duration vs Satisfaction: A scatter plot analyzing the impact of call length on customer satisfaction.
3.3 Customer Experience & Operational InsightsCall Volume Heatmap: A heatmap showing the busiest times of the day.
Customer Satisfaction Breakdown: A pie chart segmenting happy, neutral, and unhappy customers. Call Resolution Time vs Satisfaction: A table with conditional formatting highlighting key metrics.
4. Impact and Business ValueThis dashboard is designed to:
Provide real-time insights into call center operations. Identify peak call times and optimize agent allocation. Improve customer satisfaction by analyzing key metrics. Serve as a professional portfolio piece to attract potential employers and high-paying clients in business intelligence and data analytics roles.
5. Conclusion & Next StepsThe cleaned dataset and interactive Power BI dashboard provide valuable insights into call center performance. Moving forward:
Additional enhancements such as predictive modeling for call volume forecasting can be implemented. The dashboard can be integrated with live data sources for real-time monitoring. Advanced DAX measures can be used to refine metrics and calculations further. This project demonstrates a strong grasp of data cleaning, transformation, and visualization techniques in Power BI, making it a powerful addition to a professional portfolio.