BI Exercises with Public Data: Government, Ecommerce & SaaS Examples

In the era of data-driven decision-making, hands-on experience with real-world datasets is a crucial part of learning business intelligence (BI). Whether you’re a student, an aspiring data analyst, or a business professional sharpening your analytics skills, practicing BI exercises using publicly available data presents an accessible and effective learning opportunity. Government agencies, ecommerce platforms, and SaaS (Software as a Service) companies all offer rich datasets that learners can use to simulate business scenarios, apply data visualization techniques, and generate actionable insights.

Why Use Public Data for BI Exercises?

Public data has several advantages for those looking to hone their BI skills:

Accessibility: Most public datasets are free and easy to access with no restrictions.
Realism: These datasets often come from real-world sources, reflecting the complexities professionals face daily.
Diversity: From demographics to product sales, the variety enables wide-ranging analysis applications across industries.
Tool Compatibility: Public data can be used in BI tools like Power BI, Tableau, Google Data Studio, and Excel.

Where to Find Public Datasets

Before diving into exercises, it’s important to know where to locate quality, up-to-date data. Some popular sources include:

Data.gov – A hub for U.S. government datasets spanning sectors including health, environment, and education.
Data.world – A community for sharing and discovering datasets with a focus on collaborative analysis.
Kaggle – More than a competition platform, Kaggle offers a vast library of structured datasets with accompanying metadata.
Google Public Data Explorer – Easy to visualize datasets from organizations like the World Bank and UN.

BI Exercise Ideas by Domain

1. Government: Analyze Public Services & Demographics

Government datasets are especially useful for learning data blending, geographic mapping, and time-series analysis. For example, use city-level crime reports or census data to uncover trends or regional disparities.

Exercise idea: Develop a dashboard analyzing population growth and public school availability by state. Include metrics such as population per school, funding per student, and teacher-to-student ratios. Consider layering in interactive elements that let users filter by age group, state, or year.

Potential data sources include:

U.S. Census Bureau
OpenStreetMap
Federal Bureau of Investigation (FBI) crime statistics

2. Ecommerce: Track Sales & Customer Behavior

Ecommerce analysis is a prime use case for BI. Key business metrics include total revenue, returning customers, cart abandonment rates, and popular products. These can all be derived from mock ecommerce datasets often found on Kaggle or Github.

Exercise idea: Take an ecommerce order dataset and build a comprehensive BI report that covers:

Monthly and daily sales trends
Top performing products
Inventory aging and stock-out alerts
Geographic trends in customer orders

Add drill-down capabilities to explore the performance of items by category and vendor. Try applying forecasting models using sales history to predict future demand.

Popular datasets for ecommerce BI exercises include:

“Ecommerce Purchases” CSV dataset from the UCI Machine Learning Repository
The “Online Retail” dataset, available through Kaggle
Amazon product reviews and sales history from open sources

3. SaaS: Measure Customer Retention & Product Usage

SaaS businesses rely heavily on metrics such as Monthly Recurring Revenue (MRR), churn rate, Customer Lifetime Value (CLV), and user engagement. Building a BI dashboard for a SaaS product can involve multi-table joins, cohort analysis, and time-based filtering.

Exercise idea: Use a dummy SaaS dataset with information on subscriptions, user logins, and feature usage. Create an executive dashboard that includes:

New vs. returning customers over time
Churn rate by plan tier
User activation rate by sign-up channel
Revenue breakdown across pricing models

Dig deeper with segmentation by customer size or industry. This simulates real-world business questions stakeholders might ask.

Helpful datasets might include:

Mock SaaS revenue reports from Reforge or ChartMogul data exports
CSV or JSON samples showing user login logs, subscription metadata, and NPS ratings

Tips for Maximizing BI Learning from Public Data

When developing BI projects, consider the following strategies to improve your learning outcomes:

Start with a business question: Know what decision or problem the data will inform. This provides direction and adds context.
Clean and shape your data: Data rarely comes clean. Learning to spot and correct issues is part of the process.
Use calculated measures: Create KPIs like ROI or customer tenure using formulas and DAX language (in Power BI).
Embrace interactivity: Design dashboards that allow users to filter by time range, customer type, or geography.
Interpret, don’t just present: Add takeaway messages beside charts—what is this chart telling a business leader?

Combining Multiple Data Sources

In advanced BI scenarios, it’s common to merge different data sources. For example, census demographic data could be combined with ecommerce customer databases to test whether income levels correlate with purchase frequency in a specific region.

This exercise often involves:

Matching keys across sources (e.g., zip code fields)
Normalizing differing formats (e.g., date formats, numeric encodings)
Dealing with missing or mismatched data

These skills mimic real-world BI challenges and enhance understanding of data architecture and modeling techniques.

Tools You’ll Need

Some popular BI tools for working with public datasets include:

Power BI: Ideal for creating interactive dashboards and performing DAX calculations.
Tableau: Known for strong data visualization capabilities and map-based analysis.
Google Data Studio: Easy to connect to Google Sheets and BigQuery for rapid prototyping.
Excel: Still the go-to for many analysts due to its familiarity and flexible formulas.

Conclusion

Practicing BI exercises using public data encourages not only technical learning but also strategic thinking. By exploring datasets across government, ecommerce, and SaaS domains, learners gain a multifaceted understanding of how to turn raw data into compelling business insights. It’s a risk-free environment to iterate, explore, and build a portfolio that demonstrates analytical and storytelling abilities to future employers or stakeholders.

Frequently Asked Questions (FAQ)

What public datasets are best for beginners?: Datasets with clean formatting and well-documented columns, such as the UCI “Iris” or “Online Retail” datasets, are great starting points due to their simplicity and business relevance.
Can I use public datasets in my portfolio?: Yes, public datasets are ideal for building dashboards and case studies in portfolios since you can share them freely without licensing concerns.
What BI skills will I develop through these exercises?: You’ll learn data preparation, data modeling, dashboard design, KPI calculation, and storytelling through visualization tools.
How do I choose a BI tool to start with?: Choose based on your goals. Power BI is great for enterprise environments, Tableau is best for design-first users, and Google Data Studio is ideal for those embedded in the Google ecosystem.
Where can I share my BI projects?: Platforms like GitHub, LinkedIn, Tableau Public, and Kaggle allow you to showcase your work and receive feedback from the community.