Case Study: Using Synthetic Data to Validate Onboarding Flows for a Predictive Analytics Startup

AI · 6 min read

Predictly, a pre-revenue predictive analytics startup serving enterprise customers, could not use real client data to prototype onboarding due to strict data governance. The team built a synthetic data generator that produced realistic but non-identifiable datasets reflecting various data quality issues: missing columns, skewed distributions, and inconsistent timestamps.

Designers used the synthetic datasets to stress-test import flows, validation messaging, and error handling. They discovered scenarios where default validation masks obscured root causes (for example, silent column renaming), and added explicit remediation steps and sample previews. The approach surfaced edge cases that would have been costly to find in production.

Because the synthetic generator mirrored client schemas, the team confidently shipped a robust onboarding wizard that reduced setup time by 30% in pilot customers. The case demonstrates that synthetic data, paired with careful UX design, can accelerate product decisions in regulated contexts without compromising privacy.