First off, why do we need fake data?

The answer is simple: to respect and protect the privacy of real data. Sure, there’s a lot of real data out there, but we shouldn’t be using it however we please.

Where do we need fake data?

There are many use cases. Here are the ones we focus on at
Software development, testing, and QA
ML model training
Data analysis
Sales demos

So, what do we mean by fake data?

Here are some terms you may have heard:
Test data
Anonymized data
De-identified data
Masked data
Encrypted data
Generated data
Synthesized data
Mimicked data

Lots of words with lots of meanings, but at a high level:

It’s all fake.

The difference lies in the approach. Not all fake data is created equally.

How should you fake it?

You should fake your data according to:

Your use case
The degree of privacy you need to acheive
The degree of utlity you want to preserve

Fact is, not all real data is created equally. Data exists on a spectrum. Your approach to faking it should too.

Faking data with

Our platform offers all the fake data solutions you need in one place. Mix and match advanced de-identification,  synthesis, and subsetting techniques to service all of your data’s use cases as they each need to be handled.

Fake your data to achieve the right degree of privacy and utility for each data point in your database.
How are you looking to fake it? Whatever your answer is, we can make it happen.

Fake your world a better place

Enable your developers, unblock your data scientists, and respect data privacy as a human right.