How to Check the Quality of Synthetic Data (Checklist)

Synthetic data is only useful if it behaves like the real thing and does not leak the real rows. Four quick checks tell you both.

Open the Synthetic Data Generator

Compare the numbers. For each number column, check the minimum, maximum, average and spread against the real file. Small differences are normal. Large gaps usually mean the real file had too few rows to learn from. Under about 100 real rows, expect only a rough copy.

Compare the shares. For category columns, the share of each value (for example, how many orders are Paid, Pending or Refunded) should be close to the real file. If a category is far off, try Statistical mode, or Random sampling if you only want values that already exist.

Check the relationships. Columns often move together: salary rises with age, order total with quantity. Good synthetic data keeps this. Plot or sort one column against another in both files and see if the pattern is similar. Random fake data fails this test, which is the main reason to use synthetic data.

Check privacy. No new row should be an exact copy of a real row, and rare values (here, fewer than 5 rows) should be hidden as Other, since a unique value can point to one person. Names, emails and phones should be new, and ids renumbered. Synthetic data lowers risk but is not a guarantee, so review it before sharing it outside your team.

Open the Synthetic Data Generator

Related tool pages

  • Generate Test Data from Production Data: Create realistic test data from a production export without copying real rows. Names and emails are replaced, patterns are kept. Runs in your browser.
  • Anonymize CSV Data for Testing: Make an anonymous version of a CSV for testing: fake names and emails, new ids, same distributions. Free and in your browser.
  • GDPR-Safe Test Data Generator: Generate test data with no real people, based on the patterns of your own file. Everything runs on your device. Not legal advice; review before sharing.
  • Generate Synthetic Data from a CSV: Upload a CSV and generate synthetic rows that match its distributions and number relationships. Names and emails are replaced. Free, no upload.
  • Privacy-Safe Test Data from Real Data: Create test data that looks like production without copying production rows. Names and emails are replaced and rare values are hidden. Free, no upload.
  • Synthetic Dataset from an Excel File: Turn an Excel file into a synthetic dataset with the same patterns and no real records. Choose how many rows and download CSV, Excel or Parquet. Free.

How to use the Synthetic Data Generator: step by step

  1. Generate your synthetic data from the real file.
  2. Read the real versus synthetic table for each column.
  3. Check that the tool reports no exact copies.
  4. Download it only when the numbers, shares and privacy checks look right.

When to use the Synthetic Data Generator

  • Check a dataset before sharing it
  • Decide if you need more real rows
  • Pick between Statistical and Random sampling
  • Review test data before loading it into staging

Example

Input: real customers.csv

Output: new rows that behave like it

Frequently asked questions

What is synthetic data?

Data made by a program that follows the patterns of real data, such as ranges, shares and how columns move together, without containing the real records.

Is the synthetic data generator free?

Yes. There is no signup and no limit on how often you use it. It runs in your browser, so your file is never uploaded.

Which files can I use?

Load a CSV, TSV, Excel (.xlsx), JSON or Parquet file, then download the synthetic result in the format you choose.

How many rows can I generate?

Up to 500,000 new rows in one run. The default is the size of your file, between 100 and 50,000 rows.

Tip: If the numbers differ a lot, add more real rows rather than changing settings. More real data gives better results.