25+ Business Intelligence Exercises to Master Power BI & Tableau (2025)

Why Hands-On Practice is Essential for BI Success

Landing a job as a Business Intelligence Analyst or Data Visualization Specialist in 2025 requires more than just watching tutorials. Employers want proof that you can transform raw data into actionable insights. That’s where practical exercises come in.

This comprehensive guide walks you through 25+ real-world business intelligence exercises designed to build your skills systematically. Whether you’re just starting with Power BI and Tableau or looking to add advanced techniques to your portfolio, these projects will give you the experience employers are searching for.

What You’ll Gain:

• A strong portfolio demonstrating problem-solving abilities
• Practical experience with industry-standard BI tools
• Confidence to tackle real business scenarios
• Skills that directly translate to workplace success

Understanding the BI Workflow

Every business intelligence project follows a similar pattern. Understanding these stages will help you approach any data challenge systematically:

Beginner-Level Exercises

Perfect for building foundational skills in Power BI Desktop, Tableau Public, and basic SQL.

Exercise 1: Clean Messy Sales Data

Your Mission: Learn to identify and fix common data quality problems.

Tools Needed: Power Query in Power BI or Tableau Prep

The Challenge:

1. Download a sales dataset from Kaggle
2. Find five data issues (missing values, wrong date formats, spelling errors)
3. Document each problem you discover
4. Clean the data using your chosen tool
5. Save your transformation steps

What You’ll Learn: Data rarely arrives perfect. This exercise teaches you to spot problems before they ruin your analysis.

Exercise 2: Build Your First Sales Dashboard

Your Mission: Create an interactive dashboard tracking sales performance.

Tools Needed: Power BI or Tableau

The Challenge:

1. Connect to your cleaned sales data
2. Create two key metrics: Total Revenue and Number of Orders
3. Build a line chart showing sales trends over time
4. Add a map or bar chart displaying sales by location
5. Include a filter so users can view specific years

What You’ll Learn: How to transform data into visual insights that tell a story.

Exercise 3: Identify Your Best Customers

Your Mission: Use a scatter plot to segment customers by value.

Tools Needed: Tableau or Excel

The Challenge:

1. Calculate each customer’s total spending
2. Count how many orders each customer placed
3. Create a scatter plot with spending on one axis and order count on the other
4. Color-code or label your highest-value customers
5. Draw conclusions about customer behavior patterns

What You’ll Learn: Visualization techniques for discovering patterns in customer data.

Exercise 4: Write SQL to Find Top Customers

Your Mission: Practice essential SQL skills for data extraction.

Tools Needed: MySQL, PostgreSQL, or any SQL database

The Challenge: Write a query that returns:

• Customer names
• Their total purchase amounts
• Only the top 10 customers
• Sorted from highest to lowest spending

What You’ll Learn: SQL fundamentals that every BI professional needs, as covered in Microsoft’s SQL documentation.

Exercise 5: Budget vs Actual Comparison in Excel

Your Mission: Calculate financial variances with conditional formatting.

Tools Needed: Microsoft Excel

The Challenge:

1. Create a table with budgeted amounts for five departments
2. Add actual spending for each department
3. Calculate the difference (Variance)
4. Calculate the percentage difference
5. Apply red formatting for over-budget, green for under-budget

What You’ll Learn: Basic financial reporting and Excel formatting skills.

Intermediate-Level Exercises

These projects introduce data modeling, advanced calculations, and storytelling techniques.

Exercise 6: Build a Star Schema Data Model

Your Mission: Organize data for maximum performance and clarity.

Tools Needed: Power BI Model View or Tableau Data Source

The Challenge:

1. Start with a single flat table containing all your data
2. Separate it into a central Facts table (sales transactions)
3. Create Dimension tables (dates, products, customers)
4. Establish relationships between tables
5. Verify that all relationships follow proper one-to-many patterns

What You’ll Learn: Star schema design dramatically improves report performance and is fundamental to professional BI work.

Exercise 7: Master Time-Based Calculations

Your Mission: Create measures that compare performance across time periods.

Tools Needed: Power BI (DAX) or Tableau (Calculated Fields)

The Challenge: Build a report showing monthly sales with these three additional metrics:

1. Year-over-year growth percentage
2. Month-over-month change
3. Three-month rolling average

What You’ll Learn: Time intelligence is crucial for business reporting and helps stakeholders understand trends.

Exercise 8: Handle Changing Data Over Time

Your Mission: Manage historical changes in your data.

Tools Needed: Power Query or SQL

The Challenge: Imagine a product changes categories. Implement two approaches:

• Type 1: Simply update the category (loses history)
• Type 2: Keep both old and new records with date stamps (preserves history)

What You’ll Learn: How to maintain data accuracy when business definitions change.

Exercise 9: Analyze Customer Churn

Your Mission: Discover why customers stop buying.

Tools Needed: Power BI or Tableau

The Challenge:

1. Calculate your churn rate (percentage of customers who left)
2. Create visualizations showing churn trends over time
3. Analyze which customer groups have the highest churn
4. Identify factors correlated with customers leaving
5. Present findings across three connected dashboard pages

What You’ll Learn: How to investigate business problems using data and tell a complete analytical story.

Exercise 10: Create a Profit & Loss Report

Your Mission: Structure financial data into standard reporting format.

Tools Needed: Power BI with Matrix visual and DAX

The Challenge:

1. Organize data containing revenue, costs, and expenses
2. Calculate Gross Profit (Revenue minus Cost of Goods Sold)
3. Calculate Net Income (Gross Profit minus Operating Expenses)
4. Display results in a professional P&L format
5. Use DAX to ensure calculations follow accounting rules

What You’ll Learn: Financial reporting fundamentals that apply across industries.

Advanced-Level Exercises

These portfolio projects showcase enterprise-level skills including security, forecasting, and integration.

Exercise 11: Forecast Future Sales

Your Mission: Predict next quarter’s sales using historical patterns.

Tools Needed: Python (with Scikit-learn or Prophet) plus Power BI or Tableau

The Challenge:

1. Build a time-series forecasting model in Python
2. Generate predictions for the next 90 days
3. Import predictions into your BI tool
4. Create a visualization showing actual vs predicted sales
5. Display confidence intervals around your predictions

What You’ll Learn: How to integrate machine learning with BI tools for predictive analytics.

Exercise 12: Build Interactive Geographic Analysis

Your Mission: Identify high and low-performing regions visually.

Tools Needed: Tableau (Map Layers) or Power BI (ArcGIS)

The Challenge:

1. Plot customer locations on a map
2. Create a heat map showing sales density
3. Add drill-through functionality (click a state to see city details)
4. Identify geographic patterns in your data
5. Make recommendations based on location insights

What You’ll Learn: Advanced mapping techniques for spatial analysis.

Exercise 13: Implement Row-Level Security

Your Mission: Control who sees what data in shared reports.

Tools Needed: Power BI Service or Tableau Server

The Challenge:

1. Create two manager roles (North Region and South Region)
2. Set up security rules so each manager sees only their region’s data
3. Test by logging in as different users
4. Verify that data restrictions work correctly
5. Document your security implementation

What You’ll Learn: Data governance techniques essential for enterprise BI deployments, as explained in Tableau’s security documentation.

Exercise 14: Optimize Supply Chain Performance

Your Mission: Track efficiency and spot operational bottlenecks.

Tools Needed: Power BI or Tableau

The Challenge:

1. Analyze inventory or logistics data
2. Calculate Days of Inventory Outstanding
3. Measure Average Lead Time
4. Create visualizations showing product movement
5. Use Gantt or Waterfall charts to display timelines

What You’ll Learn: Operations analytics for manufacturing and distribution businesses.

Exercise 15: Advanced SQL Window Functions

Your Mission: Perform complex data analysis using SQL.

Tools Needed: PostgreSQL or SQL Server

The Challenge: Write a query that:

1. Uses window functions (LAG, RANK, etc.)
2. Calculates time between each customer’s first and second purchase
3. Ranks customers by the shortest time gap
4. Identifies patterns in repeat purchase behavior

What You’ll Learn: Advanced SQL techniques that separate junior from senior analysts.

Quick Tool-Specific Exercises

These focused tasks help you master specific features in each platform.

Making Your Portfolio Stand Out

Building a Complete Project Showcase

As you complete these exercises, document your work professionally:

1. Screenshot Your Process: Capture before/after data transformations
2. Explain Your Decisions: Write brief descriptions of why you chose specific approaches
3. Show Business Impact: Frame results in terms of business value
4. Create a GitHub Repository: Store your work where employers can find it
5. Write Case Studies: Turn 2-3 exercises into detailed portfolio pieces

Understanding Analytical Approaches

Frequently Asked Questions

What’s the most critical BI skill to develop?

Data modeling stands out as the most important skill. A well-designed data model makes everything else—calculations, visualizations, performance—work better. Poor data structure leads to slow reports and inaccurate metrics regardless of how advanced your other skills are.

Should I learn Power BI or Tableau first?

For complete beginners, Power BI offers a gentler learning curve because it integrates seamlessly with Excel and has massive community support. The DAX language is also more structured for business metrics. However, learning both tools makes you more marketable. Start with whichever is more common in your target job market.

What makes these exercises effective for learning?

These exercises work because they simulate real business scenarios. Instead of learning features in isolation, you practice combining multiple skills to solve complete problems—exactly what you’ll do in actual BI roles. This approach builds both technical skills and business judgment.

How long should I spend on each exercise?

Beginners should spend 2-4 hours per exercise, taking time to understand each step. Intermediate exercises might take 4-8 hours. Advanced projects could require 8-16 hours. Don’t rush—deep learning is more valuable than quick completion.

What is Row-Level Security and when do I need it?

Row-Level Security (RLS) restricts which data rows specific users can access in shared reports. For example, a regional sales manager sees only their region’s data even though the complete dataset powers the report. RLS becomes essential when deploying reports across organizations where different users need different data access levels, as detailed in Power BI’s security documentation.

Your Next Steps

Start with Exercise 1 and work through each level systematically. Don’t skip the beginner exercises even if you have some experience—they build essential habits. Focus on completing 2-3 projects each week rather than rushing through everything.

Remember: employers value portfolios showing real problem-solving ability over certifications alone. Each exercise you complete adds concrete proof of your capabilities. Take your time, document your work, and you’ll build the skills that separate good candidates from great ones.

Good luck with your business intelligence journey!