Professional Power BI Dashboard

Jun 22, 2024

Lecture Summary: Professional Power BI Dashboard

1. Project Overview

  • Problem Statement: Credit Card Financial Dashboard
  • Deliverables: Creating a professional Power BI dashboard in five steps
    • Project Overview
    • Data Extraction
    • Data Processing and DAX Queries
    • Dashboard and Insights
    • Export and Share

2. Data Extraction

  • Connect Power BI to SQL Database
  • Steps to Extract Data: SQL Database to Power BI
    • Prepare CSV files
    • Create tables in SQL database
    • Import data from CSV to SQL
    • Connect Power BI to SQL database and import tables

3. Data Processing and DAX Queries

  • Data Cleaning: Ensuring no null values or duplicates
  • Creating Calculated Columns and Measures: Extracting additional insights
    • Examples: Age Group, Income Group, Revenue calculations
  • **Examples of DAX Queries: **
    • Calculate Revenue: Annual Fees + Total Transaction Amount + Interest Earned
    • Age Group: Using SWITCH function to categorize age
    • Income Group: Categorizing income into Low, Medium, High
  • Week-on-Week Comparison: Calculating current and previous week's revenue for trend analysis

4. Dashboard and Insights

  • Design Two Dashboards:
    • Credit Card Transaction Report: Revenue by quarter, transaction types, education level, job type, customer acquisition cost, card type
    • Credit Card Customer Report: Split by gender, income group, age group, job category, education level, state
  • Charts and Tables: Various visualizations for insights
  • Formatting: Uniform color scheme, clear indication of insights, and intuitive navigation

5. Export and Share

  • Exporting Dashboard: Save as PDF
  • Upload to GitHub: For sharing with potential employers
  • Creating a LinkedIn post: With detailed project overview and insights
  • Updating Resume: Adding project details with GitHub link

Important Metrics Highlighted:

  • Revenue Generation Insights: Overall, by card type, by quarter
  • Customer Demographics: By age, income, job, education
  • Transaction Analysis: Count, amount, interest earned
  • Comparative Metrics: Weekly trends, customer satisfaction, delinquency rates

Technical Aspects Covered:

  • DAX Queries: For data processing and creating insight-generating calculated columns
  • Data Visualization: Using Power BI's features for effective representation
  • Data Connectivity: Importing and connecting various data sources to Power BI
  • Report Creation: Formatting and ensuring readability and usability