Brighthive Mock Data Generator

This repository contains scripts to generate synthetic datasets that support Brighthive demos and storytelling for various use cases. The generated data is designed to be realistic and representative of real-world scenarios while maintaining privacy and security.

Purpose

The mock data generator serves several key purposes:

• Create realistic datasets for demonstration purposes
• Support storytelling and use case presentations
• Enable testing and development without using real production data
• Provide consistent, reproducible data for demos and training

Getting Started

Prerequisites

1. Python 3.7 or higher
2. Required Python packages (install using pip):

   pip install -r requirements.txt

Directory Structure

brighthive-mock-data/
├── documentation/     # Documentation files
├── output/           # Generated data files
├── scripts/          # Data generation scripts
└── requirements.txt  # Python dependencies

Generating Datasets

Step-by-Step Example: Generating CRM Data

1. Create a virtual environment:

   python -m venv venv

2. Activate the virtual environment:

   source venv/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the data generation script. Here is an example for CRM data:

   python scripts/generate_crm_data.py

5. The script will:

   • Create an output directory if it doesn't exist
   • Generate synthetic CRM data with realistic fields
   • Save the data as a CSV file in the output directory with the current date
   • The output file will be named crm_data_MM-DD.csv

6. Verify the generated data:

   • Check the output directory for the new CSV file

Available Datasets

The repository includes generators for various types of data:

• CRM data
• Healthcare data
• Financial data
• Student data
• Web analytics data
• Health devices data

Each script generates data specific to its domain while maintaining realistic relationships and patterns.

Contributing

When adding new data generators:

1. Follow the existing code structure and patterns
2. Include appropriate documentation
3. Use realistic data ranges and distributions
4. Ensure data privacy and security
5. Add your script to this README's "Available Datasets" section