Quick start: Generate synthetic tabular data

You need a project to to store the assets.

Watch a video to see how to create a sandbox project and associate a service. Then follow the steps to verify that you have an existing project or create a sandbox project.

1. From the watsonx home screen, scroll to the Projects section. If you see any projects listed, then skip to Task 2. If you don't see any projects, then follow these steps to create a project.

2. Click Create a sandbox project. When the project is created, you will see the sandbox project in the Projects section.

For more information or to watch a video, see Creating a project.

To preview this task, watch the video beginning at 00:24.

The data set used in this tutorial contains typical information that a company gathers about their customers, and is available in the Resource hub. Follow these steps to find the data set in the Resource hub and add it to your project:

1. Access the Customers data set in the Resource hub.

2. Click Add to project.

3. Select your project from the list, and click Add.

4. After the data set is added, click View Project.

For more information on adding data assets from the Resource hub to your project, see Loading and accessing data in a notebook.

To preview this task, watch the video beginning at 00:43.

Use the Synthetic Data Generator to create a data flow that generates synthetic tabular data based on production data or a custom data schema using visual flows and modeling algorithms. Follow these steps to create a synthetic data flow asset in your project:

1. From the Assets tab in your project, click New asset > Generate synthetic tabular data.
2. For the name, type Bank customers.
3. Click Create.
4. On the Welcome to Synthetic Data Generator screen, click First time user, and click Continue. This option provides a guided experience for you to build the data flow.
5. Review the two use cases:
   • Leverage your existing data: Generate a structured synthetic data set based on your production data. You can connect to a database, import or upload a file, mask, and generate your output before exporting.
   • Create from custom data: Generate a structured synthetic data set based on meta data. You can define the data within each table column, their distributions, and any correlations.
6. Select the Leverage your existing data use case, and click Next to import existing data.
7. Click Select data from project to use the customers data asset that you added from the Resource hub.
   1. Select Data asset > customers.csv.
   2. Click Select.
   3. Click Next.
8. In the list of columns, search for creditcard_number.
   1. In the Anonymize column for CREDITCARD_NUMBER, select Yes to mask customers' credit card numbers.
   2. Click Next.
9. Accept the default settings on the Mimic options page. These options generate synthetic data, based on your production data, using a set of candidate statistical distributions to modify each column in your data. Click Next.
10. For the File name, type bank_customers.csv, and click Next.
11. Review the settings, and click Save and run. The Synthetic Data Generator tool displays with the data flow. Wait for the run to complete.

To preview this task, watch the video beginning at 01:48.

When the run completes, you can explore the data flow. Follow these steps to review the synthetic data flow and the results:

1. Click the Palette icon to close the node panel.

2. Double-click the Import node to see the settings.

   1. Review the Data properties. The tool read the data set from the project and filled in the appropriate data properties.
   2. Expand the Types section. The tool read the values and columns in the data set.
   3. Click Cancel.

3. Double-click the Anonymize node to see the settings.

   1. Verify that the CREDITCARD_NUMBER column is set to be anonymized.
   2. Expand the Anonymize values section. Here you can customize how the values are anonymized.
   3. Click Cancel.

4. Double-click the Mimic node to see the settings.

   1. Review the default settings to mimic the data in the source customers data set.
   2. Click Cancel.

5. Double-click the Generate node to see the settings.

   1. Review the list of Synthesized columns.
   2. Optional: Review the Correlations and Advanced Options.
   3. Click Cancel.

6. Double-click the Export node to see the settings.

   1. Optional: By default the exported data is stored in the project. Click Change path to store the exported data in a connection, such as Db2 Warehouse.
   2. Click Cancel.

7. Click your project name to return to the Assets tab.

   

8. Click bank_customers.csv to see a preview of the generated synthetic tabular data.