Using generative AI for synthetic data
FEB 2024
Embedded Files
Generative AI has revolutionized the way we create synthetic data, providing a powerful tool for research and analytics. Synthetic data, artificially generated using algorithms or simulations, is designed to reflect the complexity and characteristics of real-world data.
Generative AI, particularly Large Language Models (LLMs), has made the generation of synthetic data more accessible and efficient.
This technology allows researchers, analysts, and data enthusiasts to augment existing datasets with generated data, supporting robust data analysis and decision-making.
However, the generation of synthetic data requires a clear understanding of data modeling and the real data environment.
For a comprehensive understanding of how generative AI can be used for synthetic data, read our full guide here.
Try MindPort for better insights, faster ⬇︎
MindPort empowers researchers, data teams, and agencies to effortlessly generate synthetic data with unmatched efficiency. Whether you want us to run an end-to-end research project, or just generate the data for your in-house team, we use our proprietary data-driven platform to scope, synthesise, collect, analyze and share data with your team.
Using GPT can only get you so far. Inside MindPort, our data team can organize your projects, scope your requirements, generate a synthetic sample and track individual synthetic participants. We're able to generate validated data in a fully bespoke workflow. This means your teams get the data and insights they need, in one centralized location.
Take your insights and analytics a step further with MindPort's synthetic data and insights:
Even before generating synthetic data, we can tailor over 180 unique categories of variable, totalling over 700 individual parameters per participant, based on your data requirements.
We can scale up from one to one thousand participants, enabling us to generate entirely unique three-dimensional personas that reflect the complexities, nuances, and fidelity of your real-world data.
We take this further by using 3rd party verification and our review framework to benchmark generated data against input data, carefully checking for biases, errors, and miscategorisations.
We're then able to package your synthesized data and insights within a team workspace, incorporating quotes for a visually compelling presentation.
Notably transforms your data analysis process, making synthetic data synthesis a seamless and insightful journey from start to finish.
Interested? Contact us and we'll send you a synthetic sample for free.
Report abuse