Unlocking Insights: The Power of Synthetic Data in Market Research and Beyond

Dr Sp Mishra
6 days ago
4 min read

ICC Blog # 138

AI generated using Chat GPT — A Creative Presentation

Published: January 22, 2026 Based on a conversation with Kiran HN, Head of Data Science and Innovation at Kantar, and insights from reliable sources.

In a recent episode of the India Career Centre podcast, I had the pleasure of reconnecting with my old friend Kiran HN, a seasoned expert in market research at Kantar. Our discussion delved into the fascinating world of synthetic data—a cutting-edge tool that's revolutionizing how we gather and analyze insights. Drawing from that conversation and supplemented by credible sources, this blog post explores key aspects of synthetic data: what it is, why it's needed, how it addresses challenges in market research through boosting and augmentation, and the exciting career paths in data science. Whether you're a student in Hyderabad exploring tech careers or a professional navigating AI trend, these insights highlight why synthetic data is a gamechanger.

What is Synthetic Data?

Synthetic data is artificially generated information that mimics the characteristics, patterns, and statistical properties of real-world data without containing any actual real data points. It's created using algorithms, statistical models, or advanced AI techniques like generative AI and machine learning. As Kiran explained in our chat, think of it as a "stunt double" in movies—it looks, moves, and acts like the real hero but isn't the actual person. This analogy captures how synthetic data replicates the essence of human or survey data (often called "human data") while being entirely fabricated.

Unlike real data collected from surveys, observations, or transactions, synthetic data is produced programmatically. For instance, it can be generated from a real dataset by training an AI model to learn its distributions and correlations, then outputting new, anonymized records that preserve those traits. This makes it invaluable in fields where real data is sensitive, scarce, or expensive to obtain. Types include tabular data (like spreadsheets), time-series, images, or even text, and it's increasingly used in AI training, simulations, and analytics.

What Was the Need for Synthetic Data?

The demand for synthetic data stems from the limitations of real-world data in an era of big data, privacy regulations, and rapid innovation. As Kiran noted, market research has long faced the "imputation problem"—gaps in datasets from incomplete surveys, merged sources, or time constraints with respondents. Traditional methods like econometric modeling addressed this, but emerging technologies like generative AI (e.g., ChatGPT) have expanded its potential.

Key drivers include:

Data Scarcity and Cost: Real data is often hard to collect, especially for niche groups like luxury car buyers or patients with rare illnesses. Synthetic data fills these voids affordably, allowing for larger, more diverse datasets without exhaustive fieldwork.
Privacy and Compliance: Regulations like GDPR and concerns over data breaches make sharing real data risky. Synthetic data eliminates personally identifiable information (PII), enabling safe use in sensitive sectors like healthcare and finance.
AI and ML Training Needs: With AI models requiring vast amounts of high-quality data, synthetic alternatives provide unlimited, on-demand generation to train systems without ethical dilemmas or biases from real datasets. Gartner predicts that by 2026, 75% of businesses will use generative AI for synthetic customer data.
Speed and Efficiency: Clients demand faster insights on tighter budgets. Synthetic data accelerates processes by simulating scenarios or augmenting incomplete data, as Kiran highlighted in experiments showing LLMs' limitations (e.g., positivity bias) but strengths when grounded in real data.

In essence, synthetic data isn't new—it's evolved from statistical imputation—but its need has exploded with AI's rise, addressing the paradox of abundant data yet persistent insight hunger.

How Synthetic Data is Helping Market Researchers Address Boosting and Augmentation

In market research, synthetic data shines in solving practical challenges like boosting and augmentation, as Kiran detailed. These techniques enhance datasets without compromising quality, enabling better decision-making for brands.

Boosting: This involves expanding a dataset by adding synthetic "rows" (e.g., respondent profiles) that mimic the statistical properties of real data. For hard-to-reach audiences, like young luxury buyers in Indian metros, researchers might collect responses from 30 real people and boost to 200 synthetic ones, reducing sampling errors and increasing confidence in insights. Kiran described it as amplifying underrepresented subgroups in brand tracking studies, cutting costs and time. Companies like Kantar use this for brand performance measurement, with launches expected in the next 12-18 months.
Augmentation: Here, synthetic data fills gaps in individual responses, like a jigsaw puzzle. If a survey is limited to 10 minutes but ideally needs 20, key questions are prioritized, and the rest predicted synthetically. This shortens questionnaires, improves respondent experience, and supports segmentation studies—dividing consumers into groups like "value-conscious" or "adventurous explorers." Kiran mentioned creating AI avatars for these segments, allowing clients to query virtual personas for deeper insights.

Overall, synthetic data coexists with human data, not replacing it. It boosts agility in product testing, fraud detection, and trend forecasting across industries like pharma and automotive. At Kantar, rigorous validations ensure synthetic outputs match real patterns, with transparency to clients being non-negotiable.

Careers in Data Science and Related Educational Paths

Our conversation also touched on careers, especially for youngsters eyeing market research or data analytics. Data science offers diverse paths, blending tech, business, and domain expertise.

Common Career Progressions: Start as a Data Analyst (focusing on data cleaning and visualization), advance to Data Scientist (building models and predictions), then Senior/Lead roles overseeing teams. Specialized tracks include Machine Learning Engineer (AI systems) or Data Engineer (infrastructure). In market research, roles like those in Kantar's Data Science and Innovation Team involve piloting AI solutions.
Educational Paths: A bachelor's in statistics, math, computer science, or engineering is foundational. For quantitative research, add marketing basics; for qualitative, psychology or anthropology. Pursue a master's in data science or certifications like IBM Data Science Professional Certificate for skills in Python, SQL, ML, and stats. Kiran emphasized three pillars: math/algorithms, technology (e.g., cloud), and business acumen. Gain experience via internships, open-source projects, or online courses.

Data science is booming, with roles in healthcare, finance, and beyond. As Kiran said, it's a "privileged position" with a ringside view of AI trends.

Conclusion: The Future is Synthetic, But Grounded in Reality

Synthetic data is transforming market research by bridging data gaps and fostering innovation, as evident from my discussion with Kiran and industry sources. Yet, it thrives alongside human data, with transparency and validation key.

For aspiring data scientists, the field promises rewarding paths—start building your foundation today. Follow me on X @spmishrais for more on careers and tech. What are your thoughts on synthetic data?

Share in the comments!

Watch the full conversation here.