This documentation is currently a work in progress. Some sections may be incomplete or subject to change.

Multi Variable Data

Create complex, interrelated data sets with a single setup, perfect for populating user profiles, product catalogs, or any design that requires diverse but coherent information.

Overview

One of Datapaw's most powerful features is its ability to generate multi-variable data. This allows you to create complex, interrelated data sets with a single setup, perfect for populating user profiles, product catalogs, or any design that requires diverse but coherent information in a single click.

How to Set Up Multi-variable Data

  1. Open Datapaw

    Select Multi Variable Data menu in homepage.

  2. Add Variables

    Think variable is a target layer name you will populate the data of. At the end, we will use the variable as layer name.

    • Click the "+" button to add a new variable.
    • You can add as many variables as you need for your design.

  3. Configure Each Variable:

    • Choose the data type (String, Number, or Percentage).
    • Set specific options for each variable (e.g., range for numbers, order type, decimal places).

  4. Generate Data:

    • Click the "Generate" button to create your multi-variable data set.

Tips for Effective Multi-variable Data

  1. Think About Relationships: Consider how your variables might relate to each other in the real world.

  2. Use Consistent Formats: Ensure number formats, date styles, etc., are consistent across your data set.

  3. Balance Randomness and Realism: While randomness adds variety, some constraints ensure your data remains realistic.

  4. Test and Iterate: Generate a few sets and review them. Adjust your setup if needed to get the desired results.

  5. Save Complex Setups: If you create a particularly useful multi-variable setup, save it for future use.

Troubleshooting

If you encounter issues with your multi-variable data:

  • Check that all variables are properly configured.
  • Ensure any relationships between variables are logically sound.
  • Verify that your ranges and constraints aren't too restrictive.
  • Try adjusting the seed value for a different set of random results.

Remember, the key to great multi-variable data is creating a setup that generates diverse yet realistic and coherent information. Experiment with different combinations to find what works best for your specific design needs!