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# Sample files

> Browse and load built-in sample datasets across business, technology, finance, and healthcare to try Mammoth without uploading your own data.

Sample files are built-in datasets you can load without uploading your own data. Use them to evaluate Mammoth, train team members, or build demos.

![New Dataset dialog with Sample Files selected and category tabs above cards such as Multi-Store Retail Sales](https://docs.mammoth.io/api/v1/images/20260324_104502_NewDatasetModalshowingSampleFiles.jpg)

## Why use sample files?

- **No data preparation**: Start exploring right away.
- **Business scenarios**: Each dataset represents a common business problem.
- **No sensitive data**: Test features without using company data.
- **Shared training data**: Teams can practice on identical datasets.
- **Demo preparation**: Build presentations with familiar data patterns.

## Available sample datasets

Each sample is a CSV file of 25 to 63 rows, so it loads quickly and is easy to inspect. Samples are grouped into four categories.

### Business

| Dataset | Description |
| --- | --- |
| Multi-Store Retail Sales | 5 clothing stores format standardization |
| E-commerce Multi-Channel | Shopify, Amazon, eBay product matching |
| Global Franchise Performance | Multi-country restaurant data with currency |
| Hotel Occupancy Analytics | Multi-platform booking data consolidation |
| Social Media Campaign Performance | Facebook, Instagram, LinkedIn consolidation |

### Technology

| Dataset | Description |
| --- | --- |
| SaaS Business Metrics | Customer acquisition, churn, expansion analysis |
| Competitive Intelligence | Competitor pricing and feature analysis |
| Manufacturing Quality Control | Production line and IoT sensor metrics |
| Supply Chain Optimization | Global vendor performance and delivery |
| Media & Entertainment ROI | Cross-platform content performance |

### Finance & Real Estate

| Dataset | Description |
| --- | --- |
| Financial P&L Consolidation | Multi-subsidiary revenue with currency conversion |
| Credit Risk Assessment | Multi-bureau loan data risk scoring |
| Real Estate Portfolio Analytics | Multi-market property performance and occupancy |
| Construction Project Tracking | Multi-site timelines, costs, resources |

### Healthcare & Entertainment

| Dataset | Description |
| --- | --- |
| Patient Outcome Tracking | Clinical data ID standardization and metrics |
| Educational Institution Analytics | Student performance, enrollment, resource data |
| Non-Profit Fundraising Analytics | Multi-platform donor and campaign data |
| Sports Team Performance | Player stats, financials, fan engagement |
| Event Management Analytics | Multi-venue ticket sales and attendance |

![Sample Files dialog on the Business tab with Multi-Store Retail Sales selected and Create new dataset enabled](https://docs.mammoth.io/api/v1/images/20260324_104557_Categoryselectioninterface.jpg)

## How to access sample files

### Create a dataset from sample data

1. **Start Dataset Creation**

   - Click **+ New Dataset** in your Project
   - Select **Sample Files** from the data source options

2. **Browse by Category**

   - Choose the **All samples** tab, or one of the Business, Technology, Finance & Real Estate, or Healthcare & Entertainment tabs
   - Review dataset descriptions
   - Consider your learning objectives or demo requirements

3. **Select**

   - Click the card of your chosen dataset. The card is highlighted; there is no data preview
   - Confirm the description matches your intended use case

4. **Create Dataset**

   - Click **Create new dataset** to load the sample data
   - Your new dataset appears in the Project with sample data ready for exploration

[Video: Watch on YouTube](https://www.youtube.com/watch?v=2ONAIO0mk70)

> **Tip:** Choose sample data that matches your team's domain, for example a manufacturing quality dataset for an operations team.

## Choosing the right sample dataset

### For first-time exploration

**Recommended**: Multi-Store Retail Sales

- Familiar business concepts (sales, products, regions)
- Suits several transformation types
- Has patterns for data quality assessment
- Fits aggregation and filtering scenarios

### For advanced feature testing

**Recommended**: E-commerce Multi-Channel or Supply Chain Optimization

- Complex multi-source scenarios
- Data standardization challenges
- Advanced calculation opportunities

### For team training

**Recommended**: Match your industry (Manufacturing → Quality Control, SaaS → Business Metrics)

- Domain-relevant scenarios keep exercises practical
- Team members recognize the patterns and problems

### For client demonstrations

**Recommended**: Industry-specific datasets

- Stakeholders recognize familiar data
- Use cases map to real business problems

![Before and after of a sample dataset: Remove Duplicate Rows and Bulk Replace steps reduce institution_type to one value](https://docs.mammoth.io/api/v1/images/20260410_061905_Side-by-sidecomparison.jpg)

## Get the most from sample data

### Exploration best practices

#### Start with a data quality assessment

1. Use Explore Cards to understand data distributions
2. Check for missing values, outliers, inconsistencies
3. Identify transformation opportunities before building pipelines

#### Try several transformation types

1. **Cleaning**: Standardize company names, fix formatting issues
2. **Filtering**: Remove test data, focus on specific regions or time periods
3. **Aggregating**: Calculate totals, averages, growth rates
4. **Joining**: Combine multiple datasets to create richer analysis

#### Build example pipelines

- Create monthly sales reports (Multi-Store Retail)
- Calculate customer lifetime value (SaaS Business Metrics)
- Analyze campaign ROI (Social Media Performance)
- Track quality improvements (Manufacturing Quality Control)

### Move to your own data

When you are ready for real data:

1. **Document Your Learning**: Note which transformations worked well with sample data
2. **Identify Patterns**: Which data quality issues did you encounter and solve?
3. **Plan Your Implementation**: How will your real data differ from the samples?
4. **Start Small**: Begin with a subset of your production data

## Example sample file use cases

### New user onboarding

**Timeline**: about 15 minutes

**Path**:

1. Select Multi-Store Retail Sales sample (2 minutes)
2. Explore data using Explore Cards (5 minutes)
3. Build simple filter pipeline (5 minutes)
4. Generate first dashboard showing sales by region (3 minutes)

**Outcome**: The user has hands-on experience with the platform.

### Consultant client demo

**Timeline**: 30 minutes preparation, 15 minutes presentation

**Path**:

1. Choose industry-relevant sample dataset
2. Build demo pipeline solving common client problem
3. Create polished dashboard for presentation
4. Use familiar data patterns to explain capabilities

**Outcome**: A demo that uses relevant, familiar data.

### Team training standardization

**Timeline**: 1 hour training session

**Path**:

1. All team members use identical sample dataset
2. Follow structured exercises with predictable outcomes
3. Compare results and discuss best practices
4. Progress to individual exploration with other samples

**Outcome**: The team shares a common baseline.

> **Tip:** Build your demo pipeline with sample data before the client meeting. You can then repeat the same transformations with their actual data.

## Sample file limitations

**What sample files are not**:

- Your actual business data (they're learning tools)
- Production-ready datasets (they're designed for education)
- Comprehensive industry solutions (they're starting points)

**What to expect**:

- Datasets are smaller than production data (optimized for learning speed)
- Some edge cases may not be represented
- Data is clean enough to focus on learning Mammoth, not data cleaning

**When to move beyond samples**:

- You're comfortable with basic transformations
- You need to solve specific business problems
- You're ready to integrate with your actual systems
- You want to build production dashboards

## Next steps

After exploring sample files:

1. **Try File Upload**: [Import your own CSV or Excel files](https://docs.mammoth.io/learn/data-sources/file-upload)
2. **Build your first real pipeline**: Apply learned techniques to your business data
3. **Explore connectors**: [Connect](https://docs.mammoth.io/learn/connectors/) to your databases and business systems
4. **Create dashboards**: Visualize insights from your transformed data
5. **Set up Automation**: Schedule regular data processing workflows

![Five-step path: Sample Files, File Upload, Live Connections, Production Dashboards, then Automated Workflows](https://docs.mammoth.io/api/v1/images/20260324_104701_Successpathwaydiagram.jpg)

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Source: https://docs.mammoth.io/learn/data-sources/sample-files.md · Updated: 2026-10-13