Understanding Synthetic Data: Applications, Opportunities and Risks for Researchers

Synthetic data has rapidly become one of the most discussed topics in market research. From data augmentation and imputation to AI-generated personas, digital twins and synthetic panels, researchers are being asked to evaluate new approaches that promise faster, cheaper and more scalable insight generation.

But what exactly is synthetic data? Where did it come from? What problems is it trying to solve? And perhaps most importantly, what can it genuinely do well—and where should researchers remain cautious?

This course provides a practical, researcher-focused introduction to the rapidly evolving synthetic data landscape. We'll explore the history and evolution of synthetic data, differentiate between the major approaches currently being used, and examine real-world applications emerging across the research industry.

Throughout the day we'll compare synthetic approaches with familiar research methods and discuss where they complement, extend, or potentially replace traditional data collection techniques. We'll also examine the limitations, validation challenges, ethical considerations, governance requirements, and emerging regulatory issues that researchers need to understand before adopting these approaches.

By the end of the course participants will have a practical framework for evaluating synthetic data claims, asking the right questions of vendors and stakeholders, and determining where synthetic data may—or may not—be appropriate within their own organisations.

Learning outcomes / learning objectives

We'll make sure you'll come away being able to:

• Understand the major categories of synthetic data and how they differ.
• Explain the historical development of synthetic data and the technologies driving recent advances.
• Identify where synthetic data can effectively complement traditional market research approaches.
• Distinguish between data completion, augmentation, simulation and replacement use cases.
• Evaluate vendor claims and understand common validation approaches.
• Recognise limitations, risks and potential sources of bias in synthetic data applications.
• Understand emerging ethical, governance and regulatory considerations.
• Ask informed questions when assessing synthetic data solutions and research proposals.
• Develop a practical framework for deciding when synthetic data is—and is not—appropriate.

Who will benefit?

This course is designed for researchers, insight professionals, consultants, research managers and client-side practitioners who want a practical understanding of synthetic data and its implications for the future of research.

No technical or data science background is required.

Learning method

The session will combine seminar-style teaching, industry examples, case studies, facilitated discussion and practical evaluation exercises. Participants will be encouraged to critically assess synthetic data applications against established research principles and methods.

Course delivery mode

Online

Trainer Biography

Scott Worthge brings more than four decades of hands-on experience designing, managing, and delivering market and social research for complex, decision-critical projects across public- and private-sector organizations. His work spans consumer goods, technology, healthcare, financial services, government agencies, and nonprofit organizations, with engagements ranging from localized program evaluations to large-scale, multi-market domestic and international studies.

In parallel with his professional practice, Scott has taught market research and applied analytics at the university level for more than 20 years, including appointments with UC Berkeley Extension’s Marketing program and Michigan State University’s Master of Science in Market Research program. His teaching reflects the same applied orientation as his consulting work—bridging methodological rigor with the realities of implementation, procurement constraints, and stakeholder decision-making.

Scott is also a Board member of the Insights Career Network, a nonprofit organization dedicated to peer-to-peer support for research and insights professionals, where he contributes governance oversight and program guidance on a volunteer basis.

In 2025, Scott formed mktresearchguy llc to support project-based research engagements requiring senior-level design leadership, independent oversight, and practical execution. His consulting work includes advising on RFP-driven studies, leading primary research programs, and supporting organizations adopting emerging methodologies. Recent original research has focused on synthetic data modeling, voice and video data collection effectiveness, multicultural research design, and evolving definitions of representativeness in U.S. populations—areas increasingly relevant to public-sector, regulatory, and large-enterprise research initiatives.

A frequent speaker at professional conferences, webinars, and association forums, Scott regularly presents for organizations such as ESOMAR, IIEX, the Insights Association, and the Market Research Society. He has delivered subscribed webinars with record-setting attendance, reflecting sustained demand for practical guidance grounded in real-world research delivery rather than abstract theory.

Across all engagements, Scott’s work is characterized by a pragmatic, collaborative approach—focused on delivering research that is defensible, transparent, and actionable for organizations operating under formal procurement, compliance, and accountability requirements.