The Gen AI Frontier

Helping enterprises solve data-related problems with Generative AI

Data drives decision-making in business processes, services, and products that move the global economy forward. However, despite the volume of real-world data available, at the enterprise level, the datasets may need to be more extensive or more openly available for use to derive relevant insights. The business case for synthetic data Enterprises need augmented data to solve analytical use cases where real data is insufficient or unavailable. Existing data is copied or sampled until the data set is large enough to produce results. This method creates oversights that can lead to inaccuracy and data compliance issues. Synthetic data addresses many of these problems. Accuracy Instead of just adding repeated records to a data set, using synthetic data to augment the scarce real data leads to more accuracy in replicating the underlying joint data distributions. This is because synthetic data creates fake/synthetic entities or customers based on the behavior and parameters of real-world data, leading to the generation of more representative data overall. Data privacy Many countries have introduced data protection and privacy legislation. While this does not necessarily prevent a business from using customer data, there are tight controls to ensure it is used only for the reason it was collected. Synthetic data negates this issue: it imitates the properties of the real personally identifiable information (PII) data to ensure accurate analytics, but as the data is not linked to a real-world entity, it can effortlessly be created, shared, and disposed of without compromising data subjects or contravening privacy laws. This makes access to relevant data faster and easier. Cost The acquisition, processing, and analysis of external real-world data are expensive for most enterprises. Once a generative model is in place, the cost of generating new data drops, making it more affordable.

Development cycle Prototypes and innovative ideas require rigorous testing to ensure market relevance. Synthetic data enables the opportunity to test and demo products in various iterations, shorten the development cycle and increase market speed and product adoption/success rates.

Made with FlippingBook - PDF hosting