As businesses strive to improve their strategic models, the use of synthetic data—artificially generated data that mimics real-world information—is emerging as a powerful tool. The World Economic Forum recently noted that synthetic data could transform industries by 2030, particularly in fields where privacy concerns or data scarcity limit access to real-world datasets.
Synthetic Data and Future Modeling
One of the most intriguing applications of synthetic data is in scenario analysis, where it can be used to model situations that haven’t yet occurred, or where historical data is limited. For example, in climate modeling or pandemic preparedness, synthetic data allows analysts to simulate a variety of future scenarios without the constraints of past events. Gartner projects that by 2027, synthetic data will be used in 60% of AI development projects, up from just 1% in 2021.
For example, in climate modeling, NOAA collaborates with health researchers to integrate climate data for infectious disease forecasts. By incorporating environmental data into synthetic disease models, scientists can predict disease spread patterns influenced by climate factors. This approach has been used to study diseases like dengue and COVID-19, leveraging NOAA’s environmental insights for more robust predictions.
Similarly, pandemic preparedness efforts during COVID-19 utilized synthetic data to model disease spread under various environmental conditions. Juli Trtanj, NOAA’s One Health Lead, emphasized, “We need to think in new ways…or we’ll continue to be surprised by pandemics.” The collaboration between NOAA and health organizations exemplifies how environmental data can bolster pandemic responses and public health .
How It Enhances the “Plan” Phase
In our Plan phase, using synthetic data allows for greater flexibility in future modeling. By feeding synthetic data into models, businesses can experiment with multiple future scenarios without facing the risks associated with real-world testing. This enhances not only risk management but also innovation cycles by allowing faster iteration and optimization. We propose the following model to SHAPE your synthetic data to fit your planning needs;
SHAPE: A Model for Leveraging Synthetic Data
Specify - Define the purpose and scope of synthetic data within your project. Identify precise needs: Are you enhancing privacy, creating rare scenarios, or filling data gaps? Clarity here lays a solid foundation for all subsequent steps.
Harmonize - Ensure alignment between synthetic and real-world datasets, confirming consistency, quality, and relevance for your use case. Harmonize the synthetic data characteristics to fit the intended model requirements, especially around ethical and legal constraints.
Adapt - Generate the synthetic data models tailored to your needs. Adapt them by layering complex, nuanced patterns, and validate these to confirm they meet quality and realism benchmarks, adjusting for biases as necessary.
Polish - This is the iterative refinement phase. Monitor the synthetic data in use, evaluating for accuracy and realism. Reiterate adjustments based on real-world changes to ensure the synthetic data evolves with new insights and conditions.
Enhance - Integrate and expand synthetic data into production workflows, continuously enhancing its application through rigorous monitoring. Long-term adjustments ensure the synthetic data maintains utility as new objectives and conditions emerge.
By using SHAPE alongside our VALID model we can cover both the planning and validation phases of using synthetic data, creating a cohesive end-to-end framework that aligns with best practices for AI data integrity.
The Ethical Implications of Synthetic Data
While synthetic data has its benefits, the ethical implications are still a gray area. If synthetic datasets are poorly constructed, they can lead to biased models, which in turn generate flawed strategies. Ensuring ethical oversight and transparency in how synthetic data is created will be critical to preventing unintended consequences. As more businesses explore this tool, it will be important to develop standards that ensure synthetic data reflects real-world diversity and variability accurately.
Comentários