Today, there is a lot of buzz surrounding synthetic data, certainly as much, if not more than all the talk around AI. However, to best answer this question, we must first understand what synthetic data is and what it is not.
What is Synthetic Data?
Simply, synthetic data is artificial data, not stemming from or produced by real world events. It is data that is created by algorithms and can be used to train machine learning models or validate mathematical models. In the world of market research, synthetic data can mimic real world data’s statistical properties, offering scalable insights. It preserves privacy while reducing costs and is sometimes considered transformative in the work we do.
Synthetic Data is not…
It is not, however, real data generated by actual events. While it mimics real data’s properties, it can be misleading, due to a lack of variability. As it is often created using a computer algorithm, it can occasionally produce inaccurate or misleading results. Synthetic data requires strong analysis and discipline to be properly utilized, to help ensure clients receive data that is actionable and trustworthy.
Safety and accuracy.
Is it safe? Accurate? As synthetic data is artificially generated, it can offer insights, speed and scalability at times or in applications where real data is scarce. However, real data tends to provide greater accuracy and higher fidelity for modeling. Synthetic data can be safe when used with transparency and added rigor in data review/analysis.
How is it used?
Synthetic data can be used to create data that simulates conditions, not yet encountered. In cases where real data does not yet exist, synthetic data may well be the only solution. It tends to eliminate problems like nonresponse and skip patterns and responses can be logically consistent across all survey questions/items. It is both cost-effective and can be used to enrich and even train AI/ML models.
How does Dynata embrace Synthetic Data?
Here at Dynata, we combine insights from historical data with rich datasets that help ensure our clients receive actionable data that allows them to innovate confidently and, with purpose. The resulting data provides better clarity overall.
Just like AI, synthetic data is evolving constantly, and strong business rigor is essential to ensure its accuracy and usability. Relying solely upon synthetic data may not provide the full picture overall, yet combined with accurate, substantive data from real world studies and events will continue to offer the fuller picture for our clients.