Synthetic Data Generation - What it Solves, Where it Fits, & Whether it Can Deliver Data Teams Trust

Synthetic data is everywhere in AI conversations!!!! But what does it actually solve? I had an amazing conversation with Michael Eckhoff on The Ravit Show at Gartner he brought this down to reality. We spoke about when synthetic data makes more sense than masking or subsetting production data.

It shines when:

• Compliance makes moving production data into lower environments a bottleneck

• Teams need data that simply does not exist

• Rare edge cases are missing from real datasets

Synthetic data lets teams generate fit-for-purpose datasets on demand without copying real customer records across environments.

We also tackled the big concern. Is synthetic realistic enough?

Realistic does not mean copied. It means the relationships hold. The distributions look right. The system behaves the same way.

And you prove it.

You compare statistical properties.

You validate patterns.

You ensure no record is traceable to a real individual.

Finally, where does synthetic fit in AI and GenAI?

It removes the compliance friction.

It helps balance datasets.

It enables experimentation without exposing sensitive information.

For AI teams trying to move fast and stay compliant, this is a serious lever.

#data #ai #gartner #k2view #theravitshow