Introduction
When I reflect on customer conversations over the past year, the change is clear. We used to spend most of our time explaining what synthetic data is — curiosity, skepticism, confusion were common. Today, most teams know what synthetic data is.
But now the real challenge has emerged: knowing how to use synthetic data effectively.
What’s Changed Since Last Year
- Awareness is up. Synthetic data is now on the radar of QA teams, data scientists, and privacy/legal teams.
- Interest is genuine. Teams dealing with sensitive data or coverage gaps are asking how synthetic data can unblock them.
- Regulation is a catalyst. Privacy mandates and AI oversight are triggering demand for data-safe alternatives.
But Here’s Where Teams Still Struggle
1. “When should we use synthetic data?”
Most still think: real or synthetic. The value lies in the mix:
- When real data is too sensitive to share
- To simulate rare or missing scenarios
- To preserve privacy while collaborating
- To build realistic product demos without using production data
2. “We can’t share real data — what then?”
Enter privacy-first synthetic data:
- Removes identifiers (PII/PHI)
- Maintains statistical utility
- Enables safe distribution to QA, partners, vendors
3. “Is synthetic data useful?”
Concerns about fidelity and ROI are common — but focusing on measurable outcomes (QA bugs reduced, faster iterations, safe demos) proves its value.
How to Actually Get Started
1. Start with why
Define your goal: privacy, edge-case testing, compliance-safe collaboration, Realistic product demos?
2. Think in scenarios, not rows
Ask: “What behavior or issue do I need to simulate?” or “Can I recreate a meaningful story?” rather than “Can I mimic my dataset?”
3.. Measure the impact
Track results such as:
- Reduced QA cycle time
- Increase in edge-case coverage
- Speed of data-driven model iteration
- Higher-quality product demos
Common Misconceptions — Still Alive
“Synthetic data is only for ML.”
False. It’s used heavily in QA, demos, compliance-safe sharing.
“It’s only useful when real data is unavailable.”
False. It’s strategically valuable — even alongside real data — for augmenting and securing workflows.
The Road Ahead
- 2023: Synthetic data was a curiosity.
- 2024: It became a toolkit.
- 2025 and beyond: It will be standard practice in QA, data compliance, ML, and product workflows.
At Rockfish, we’re actively building support for scenario-driven generation (schema + intent) — coming soon.
Join the Conversation
I’m eager to hear how your team is approaching synthetic data or where you’re encountering friction. Let’s connect and share insights.
Realistic data shouldn’t block innovation.
Synthetic data should be accessible, applicable — and trusted.
Authored by Deepti Mande, Director of Product at Rockfish. Connect on LinkedIn.