Synthetic Data
In my data and analytics work on a large project, I’ve found our data team in a unique position. We’ve collaborated extensively with end users to define the desired analytical outcomes, but we’re far ahead of the source data systems coming online.
The absence of data could have been a significant blocker for developing our reporting and analytics outcomes. However, by creating synthetic data, we’ve been able to make progress—building dimensional data models and reports that can be reviewed and refined with end users well before the source systems go live.
What’s truly remarkable is how quickly high-quality synthetic data can be generated using generative AI. With well-developed and refined prompts that capture the transactional logic of the underlying source systems, it’s possible for a skilled practitioner to produce synthetic data within just a couple of days.
This approach not only enables data teams to stay ahead of project timelines but also fosters collaboration with business users. By introducing them to the new data landscape early, we become agents of change, helping them adapt and engage with the evolving project requirements.
It’s a game changer. If you’re curious about how synthetic data could accelerate your project or benefit your organization, feel free to reach out—I’d be happy to discuss it with you.