Originally published on LinkedIn by Yabble Chief Growth Officer, Doug Guion.
Synthetic data has long suffered from a bad reputation in some corners of the business world, largely due to an outdated and frankly misguided narrative: that it exists to replace survey respondents or fill the gaps in poorly executed quantitative research. It’s an association that rightly raises eyebrows—using AI to conjure up “fake people” in the context of data collection runs afoul of good research principles and risks undermining validity.
But let’s step back and reconsider. What if synthetic data is not a shortcut to patch incomplete surveys but the long-awaited solution to a much larger challenge? Let’s reframe the narrative. Synthetic data isn’t about manufacturing "fake people." It’s about unlocking the enormous, untapped potential of the data we already have.
A Legacy of Unfulfilled Promises: The Big Data Disconnect
Remember when Big Data was supposed to revolutionize the way businesses operated? The idea was thrilling: organizations would harness their sprawling repositories of information to allow real-time, data-driven decision-making. Yet for most companies, that vision never fully materialized. Instead, the reality is bleak:
- Vast volumes of historical data lie dormant. Across enterprises, valuable data sits inert in SharePoint directories, isolated in silos, or locked away in outdated systems.
- Connecting data is prohibitively complex. Integrating these fragmented data sources is expensive, time-consuming, and requires specialized expertise that many organizations lack.
- Insights are locked behind inefficiencies. Even when data is connected, extracting meaningful insights at scale remains elusive.
This is where synthetic data enters the stage—not as a replacement for real-world respondents but as the key to realizing the dreams of Big Data.
Redefining Synthetic Data: A Synthesis of the Trusted and the Timely
Let’s define synthetic data as it should be understood: not as a stand-in for human input, but as the intelligent synthesis of your existing data assets. Synthetic data can combine insights from previously disconnected datasets to reveal patterns, trends, and opportunities that would otherwise remain hidden. Think of it as a modern "translator" that lets all the data your organization has already paid for finally "talk to each other"—at scale and in ways that are both accessible and actionable.
Here’s what sets synthetic data apart in this context:
- It leverages trusted, existing sources. Unlike the dubious premise of creating “fake survey completes,” synthetic data draws from real, historical data that your organization has already vetted and relied upon.
- It overcomes data silos. By connecting and synthesizing disparate datasets, synthetic data allows businesses to harness the full spectrum of their knowledge base.
- It combines the local with the global. Synthetic data can integrate your proprietary datasets with broader, external data sources, giving you both depth and diversity in your insights.
- It is explainable. Unlike the “black box” stigma that often surrounds AI, modern conversational AI techniques make it possible to trace synthetic data’s outputs back to their original sources, ensuring transparency and trustworthiness.
Conversational AI: The Missing Link in Big Data
So why now? Why can synthetic data achieve what Big Data failed to deliver? The answer lies in conversational AI. Modern AI systems excel at understanding and synthesizing unstructured data—emails, documents, presentations, logs, surveys, and more—into cohesive, interpretable outputs. Unlike traditional analytics tools, which require painstaking preparation and integration, conversational AI enables dynamic, on-demand access to insights.
Imagine asking a single question—“What do our customers really care about in our product roadmap?”—and getting an answer derived from a synthesis of thousands of customer support logs, historical survey responses, sales reports, and market trends. No need to manually connect these sources or “mine” the data: conversational AI unlocks the value of your existing datasets without the usual barriers.
Is It Still "Impossible"?
Here’s the challenge I’ll leave you with: If synthetic data is no longer about “fake people” but instead about unlocking the latent potential of the data you’ve already paid for—if it can answer your business questions by synthesizing insights in a transparent, verifiable way—does it still feel impossible?
Synthetic data, powered by conversational AI, represents the payoff that Big Data promised but never delivered. It’s time to stop dismissing it as an invalid shortcut and start embracing it as the solution to the challenges of scale, complexity, and inertia in enterprise data. Let’s break it out of the box we’ve put it in and start thinking about it as the catalyst for a more connected, more actionable data future.
What are your thoughts? How could your organization benefit from a more integrated approach to its existing data? Let’s discuss.
About Doug Guion
Doug Guion is passionate about leveraging technology to unlock the untapped potential of data. As a leader at Yabble, a pioneer in AI-powered insights, Doug focuses on driving innovation that transforms how businesses connect and synthesize their data for smarter decision-making.
Connect with Doug to discuss the future of synthetic data, conversational AI, or how Yabble’s solutions can help your organization turn dormant data into actionable intelligence.
