The Analytics Power Hour: Episode #274 – Real Talk About Synthetic Data with Winston Lee

Release Date: June 24, 2025

Introduction to Synthetic Data

In Episode #274 of The Analytics Power Hour, hosts Michael Helbling, Val Kroll, and Julie Hoyer delve into the burgeoning field of synthetic data with esteemed guest Winston Lee. Synthetic data, a term that might evoke images from a sci-fi narrative, is increasingly integral to today's data-driven landscape. The conversation sets the stage by highlighting synthetic data's role in machine learning, privacy protection, and enhancing dashboards when real data poses challenges like missing values or outliers.

Michael Helbling opens the discussion by emphasizing the practical applications of synthetic data:

"Whether you're using it for machine learning, protecting privacy, or just giving your dashboard something to chew on when the real data won't play nice, it's definitely having a moment in our industry." ([00:14])

Understanding Synthetic Data

Winston Lee provides a foundational understanding of synthetic data, distinguishing it from anonymized or merely sampled data. Unlike data collected from real-world events, synthetic data is generated algorithmically to mimic real data patterns without directly replicating actual records.

Winston Lee clarifies:

"Synthetic data, to put it simple, it's data sets that are generated by an algorithm as opposed to being collected from some sort of real event." ([02:20])

He further demystifies synthetic data by asserting its authenticity and utility:

"We're not making up data. The algorithms that we use to generate synthetic data are indeed trained on real data... it is based on learnings of patterns from real data." ([04:00])

Common Use Cases of Synthetic Data

The discussion transitions to practical applications, where Winston elucidates that synthetic data primarily serves as an alternative to real data in scenarios constrained by privacy laws or procurement challenges. It's not about introducing entirely new capabilities but about enabling existing processes in a privacy-compliant manner.

Winston Lee explains:

"People consider synthetic data more as a way to, let's say, be able to do things that, you know, privacy laws don't otherwise allow them to do." ([05:51])

Val Kroll probes deeper into specific use cases, prompting Winston to discuss how synthetic data can augment low-resolution datasets, enhancing their granularity without compromising individual identities.

Challenges and Pitfalls in Using Synthetic Data

A critical aspect of the conversation revolves around the limitations and potential misuses of synthetic data. Winston warns against the misconception that synthetic data can "magically" generate information without a real-world basis.

Winston Lee cautions:

"One is where they think synthetic data could just miraculously invent some stuff for them." ([34:35])

He also emphasizes that synthetic data is only as reliable as the statistical properties it preserves:

"It's only statistically meaningful, it's only statistically equivalent... you have to look at it across a group of them." ([34:42])

Julie Hoyer raises concerns about biases, especially when synthetic data is used to model missing data, highlighting the importance of ensuring that the synthetic data accurately represents the underlying population.

Synthetic Data vs. Other Privacy Methods

The conversation contrasts synthetic data with other data privacy techniques like differential privacy, which involves adding noise to datasets to obscure individual identities. Winston delineates the distinct approaches:

Winston Lee states:

"For us, synthetic data is much more about recreating the data set in such a way that statistical properties are preserved, but the actual sort of cells are different." ([20:28])

This distinction underscores synthetic data's focus on maintaining utility while ensuring privacy, as opposed to merely obfuscating existing data points.

Applications in Market Research and Analytics

Winston shares insightful examples of synthetic data in action within market research. He describes a scenario where synthetic data enables nuanced audience segmentation, facilitating more accurate estimations of consumer behavior without exposing individual identities.

Winston Lee elaborates:

"So, so, let me give you a simple scenario... synthetic data is like that too. Not to say exactly... but at least if you were to build models or do analysis on the synthetic data set, you can expect it to work just as well as if you were to use the real, real, real low resolution, high resolution data set." ([29:27])

Additionally, Winston touches upon the integration of Large Language Models (LLMs) with synthetic data to enhance predictive analytics, demonstrating innovative intersections between different data technologies.

Future Directions and Industry Impact

The episode explores the future trajectory of synthetic data, highlighting its potential to revolutionize data privacy and analytics. Winston underscores the importance of continuous model maintenance and the need for thoughtful application to avoid over-extrapolation.

Winston Lee advises:

"There is a little bit of a judgment call as to what is appropriate. And there's definitely no sort of fixed formula to say, well, let's just plug these numbers in and here comes the synthetic data and then we're done." ([12:22])

Conclusion and Final Thoughts

As the episode wraps up, the hosts and Winston reflect on the significance of synthetic data in the evolving analytics landscape. They reiterate the necessity of understanding its capabilities and limitations to harness its full potential responsibly.

Michael Helbling concludes:

"Synthetic data. It's sort of going to be. I think it's. It's got a big future. So it's really cool to kind of break into this topic for the first time on the show." ([56:10])

Winston Lee urges listeners to stay engaged and continue the conversation through various platforms, reinforcing the collaborative spirit essential for advancing the field.

Key Takeaways

• Synthetic Data Defined: Algorithmically generated data that mimics real data patterns without using actual records.
• Primary Uses: Enhancing privacy, enabling data processes restricted by regulations, and augmenting low-resolution datasets.
• Challenges: Avoiding misconceptions about its capabilities, ensuring statistical equivalence, and mitigating biases.
• Comparative Advantage: Maintains data utility while ensuring privacy better than traditional anonymization or noise addition methods.
• Future Potential: Integration with AI technologies like LLMs for advanced predictive analytics and market research.

Notable Quotes:

• "We're not making up data. The algorithms that we use to generate synthetic data are indeed trained on real data." – Winston Lee ([04:00])
• "Synthetic data is much more about recreating the data set in such a way that statistical properties are preserved, but the actual sort of cells are different." – Winston Lee ([20:28])
• "It's only statistically meaningful, it's only statistically equivalent... you have to look at it across a group of them." – Winston Lee ([34:42])

For those interested in exploring synthetic data further or engaging with the community, consider joining discussions on the Measure Slack chat group, LinkedIn, or reaching out via contact@analyticshour.io.

This summary captures the essence of Episode #274, providing a comprehensive overview of the discussions on synthetic data. Whether you're a seasoned data scientist or simply curious about data analytics, this episode offers valuable insights into the current and future state of synthetic data.