Synthetic data is data generated by algorithms to simulate real-world data without using actual personal or proprietary information. It’s widely used to train AI models when real data is too sensitive, scarce, or expensive.
Synthetic data is computer-generated data that mirrors the statistical patterns of real-world data—minus the messy legal baggage and privacy headaches. It’s created by algorithms (often using techniques from generative AI) to produce datasets that look, feel, and function like the real thing.
Think of it as a stand-in actor for your actual data. It behaves like your customer data, financial transactions, or product logs—just without exposing anyone’s private info or breaking any compliance rules. Synthetic data isn’t “fake” in the sense that it’s useless; it’s deliberately structured to preserve the same distributions, relationships, and quirks that make real data useful for training AI models or testing systems.
This matters because AI needs fuel—and in many businesses, that fuel is regulated, limited, or siloed. Synthetic data lets teams build, test, or scale responsibly without waiting for a data privacy miracle.
Let’s say you’re a SaaS company training an AI feature that flags tickets likely to become churn risks. You’ve got customer support logs, but they contain sensitive data. You can’t risk privacy violations, but you also can’t train a useful model on scraps. Enter synthetic data: you generate a training set based on patterns in your real data, scrub out personally-identifiable details, and still get a performant model.
Businesses are leaning into this approach in a big way. According to a 2024 study by Synthesis AI and Vanson Bourne, 89% of tech decision-makers say synthetic data is key to their AI strategy—reporting boosts in privacy compliance, lower costs, and faster model development. And they’re not just using it in IT: synthetic data now shows up in marketing (for ad testing), sales forecasting, operations, even legal doc analysis.
Take sales, for example. Synthetic customer personas or purchase sequences let you test pricing models or decision-tree automations before unleashing them on real leads. For legal teams, it’s useful for stress-testing AI contract review tools without feeding them confidential NDAs.
Here’s a common scenario we see with fast-growing marketing teams:
The team wants to build an AI model to optimize copy and creative for A/B testing across email, landing pages, and ads. The problem? They don’t have enough campaign data volume—and what they do have is scattered across tools, locked behind client permissions, and tangled with PII like email addresses.
What often goes wrong:
Here’s what a fix could look like:
The result? Faster campaign iteration cycles, less friction with legal/compliance, and more robust models that aren’t limited by volume—or by red tape.
At Timebender, we help you stop spinning your wheels figuring out where AI fits—and start building systems that actually work. We teach teams how to prompt synthetic data responsibly, test AI tools without risking compliance, and architect workflows that cut manual rework in half.
For example, we’ll show your sales or marketing ops team how to use prompt engineering to generate synthetic contact logs, support interactions, or lead journeys to test follow-up sequences or proposal-bot logic without exposing your CRM. Clean, safe, test-ready data at a fraction of the compliance hassle.
Want to see how synthetic data can save your team time (and legal headaches)? Book a Workflow Optimization Session and we’ll walk through where synthetic data fits in your AI stack.
ResearchAndMarkets 2023: Synthetic Data Market Report
