Synthetic Data vs Random Fake Data: What Is the Difference?
Both avoid real people. Only one of them looks like your real data.
Random fake data. A generator picks values for each column on its own: a name here, an email there, a number between 1 and 100. The columns do not know about each other, so age and salary are unrelated, every plan is equally likely and dates fall anywhere. It is quick and good for filling a screen or a demo. The Data Generator in Data Utility does this.
Synthetic data. A synthetic generator first looks at your real file and learns its patterns: the range and spread of each number column, the share of each category, the period of the dates and how number columns move together, such as salary rising with age. It then makes new rows that follow those patterns. The Synthetic Data Generator in Data Utility does this in your browser.
What stays private. In our tool, names, emails, phones and similar fields get new fake values, ids are renumbered, free text keeps only its shape, rare values are hidden as Other, and the tool checks that no new row is an exact copy of a real row. A table then shows real and synthetic numbers side by side, so you can see how close they are.
When to use which. Use random fake data for demos, screenshots and filling a form. Use synthetic data to test reports, dashboards, models and queries that depend on realistic patterns. Remember that synthetic data lowers risk but is not a guarantee, so review it before sharing it publicly.
Open the Synthetic Data Generator
Related tool pages
- 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
- Open the Synthetic Data Generator.
- Load your real file.
- Check how each column will be made.
- Generate, compare with the real data and download.
When to use the Synthetic Data Generator
- Test a dashboard with realistic numbers
- Share a dataset without the real records
- Train or try a model before real data is ready
- Fill a staging database that behaves like production
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.
How is it different from the Data Generator?
The Data Generator makes random rows from column types you choose. The Synthetic Data Generator learns from your own file and makes rows that match its patterns, so the results look like your real data.
Is synthetic data fully anonymous?
It lowers the risk, and no real row is copied, but it is not a guarantee. Review it before you share it publicly.
How is the Synthetic Data Generator different from the Data Generator?
The Data Generator makes random rows from column types you choose, such as names, emails and dates, so the columns do not relate to each other. The Synthetic Data Generator learns from your own file and makes rows that match its ranges, shares and the way number columns move together, so the result looks like your real data. Use the Data Generator for quick fake data, and the Synthetic Data Generator when the data should behave like production.
Tip: Look at the real versus synthetic table before you use the data. Large gaps mean you need more real rows.