What Is Synthetic Data?
Synthetic data is information that has been artificially generated to resemble real-world data without including any real-world data. Generative AI is particularly suited to this task as it can analyze any dataset and create synthetic data that closely matches it. This allows businesses to train AI algorithms, perform tests, and simulations without exposing private or sensitive information.
Applications of Synthetic Data
Synthetic data is used in various industries for different purposes. In finance, it’s used to train fraud detection algorithms to spot falsified transactions. In healthcare, it helps avoid using sensitive patient data. In retail and marketing, synthetic data is used to create simulated customers and analyze their buying behavior.
According to Gartner research, synthetic data is often used due to difficulties with accessibility, complexity, and availability of real-world data. Partially synthetic datasets, which combine real-world and synthetic data, are more commonly used. Synthetic data allows companies to plug gaps in existing records, create new datasets, reduce costs, speed up training of machine learning models, and make better decisions.
Snowflake: Data-as-a-Service and Synthetic Data
Snowflake, a leading data-as-a-service company, offers access to synthetic datasets created by generative AI algorithms through its data marketplace. For example, Synthesis AI’s synthetic human face dataset addresses biases in facial recognition algorithms by creating diverse representations. Snowflake also offers synthetic financial data from Clearbox AI, which includes simulated mortgage applications.
Generative AI at Snowflake goes beyond synthetic data. The company has created tools based on generative AI to enhance data analysis and extraction of value. With the acquisition of Neeva, Snowflake enables natural language querying of datasets, allowing non-technical users to extract insights. Additionally, Snowflake’s partnership with Nvidia allows users to build generative AI applications that can access Snowflake data, such as chatbots and search engines. Snowflake’s Document AI tool, developed through the acquisition of Applica, enables users to query documents and extract meaning from legal contracts and invoices.
The Future of Data Generation
Snowflake sees generative AI and synthetic data playing an important role in its business going forward. As generative models become more sophisticated, they will create synthetic data that increasingly reflects the real world, leading to cheaper and more efficient insights for businesses.
Generative AI and synthetic data offer exciting possibilities for data generation, analysis, and decision-making. Snowflake’s commitment to integrating these technologies into its offerings reflects the growing importance of leveraging artificial intelligence and advanced analytics in the data-driven era.
