When people are demoing new products that require data, they usually do so by using either mock data or empty analytics boards. This can be a really fast way to lose a deal during a live demo because your product might be good, but the demo data looks off. The whole demo and the whole product look a bit off.
Everybody in sales and in demos knows the feeling: you're 20 minutes into a demo. The product is performing beautifully, the workflow is smooth, the UI is polished. Then the prospect starts looking at the data that's shown on screen, and he sees that something does not look good.
"John Doe"
"James Smith"
"Test User 1"
"Test User 2"
Transaction amounts are static and always alternate between two numbers, like $10 and $20. Every customer has exactly three orders. Addresses are generic. Dates are clustered into the same week. That's the exact moment the prospect often stops evaluating the product and starts evaluating the data. They begin by asking themselves:
Would this work on my data?
Does this team understand the complexity of my data?
Can their product handle the relationships, edge cases, and business logic that exists in my enterprise environment?
Even if your product performs perfectly in production, unrealistic demo and unrealistic demo data makes it difficult for prospects to see themselves using that product. Demo data creates a first impression that can be surprisingly difficult to overcome. In the worst cases, prospects disengage because they cannot connect the demonstration to the reality. In the best case, you need to have additional meetings, stakeholder reviews, and the proof of concept becomes necessary to bridge that gap.
Most teams still usually manually create demo data, or use generic data from the internet, or even demo the product without any data within it. This is the wrong approach. To avoid this, a few teams are starting to adopt synthetic data. Rather than manually creating demo records, they create small representative datasets from previous deployments, pilot projects, or public sources. They train a synthesizer on the representative dataset. The synthesizer then learns the distributions, correlations, and patterns that are present in the real data, and they then generate the demo dataset of any size that reflects realistic statistical patterns and relationships that an enterprise dataset would have.
The objective is not simply to make the data look realistic—it'st's to make the demonstration relatable. When prospects see familiar-feeling customers, transactions, and variability in your demo data, the conversation changes. Instead of questioning whether the product can handle their environment, they begin discussing their actual workflows, pain points, and use cases.
That's how you know you've crossed the demo data barrier.
Prospects use demo data to check whether you understand their business. Synthetic data helps you cross the demo data barrier.