The success of data analytics is dependent on the availability of quality data, which can be difficult or expensive to acquire. Supplementing authentic data with synthetically generated data can improve the effectiveness of Analytics, Business Intelligence, and Product Development.
Traditional analytics are limited to highly structured data. However, the 21st century enterprise possesses data lakes – mountains of structured and unstructured data, within loosely organized collections from disparate sources.
The potential of data lakes can now be tapped thanks to advances in artificial intelligence and machine learning. Applying synthetic data to data lakes allows engineers to get the most out of advanced data lake analytics.
Current synthetic data generation services are restricted to limited data types and schema. FlexGAN’s proprietary framework provides an autonomous solution for the generation of data lakes of arbitrary schema and type. FlexGAN adapts to the unique structures of user provided data, mimicking the relationships and distributions found between collections of data within the entirety of the data lake.