Leveraging Analytics and Business Intelligence

GenAI is the shiny new object that has executives and techies salivating. Companies have launched thousands of GenAI prototypes, of which only a small fraction have made it into production. The reason? Data deadweight.

A GenAI model is only as good as its data. Garbage in, garbage out. But given how eloquent GenAI can string together words and/or images, many unsuspecting users may not recognize errors, distortions, and downright hallucinations.

This session will outline the data risks of GenAI and provide guidelines for how to address them. It will address data bias, data freshness, data access, and data accuracy--among other things--and describe governance techniques, architectural patterns, and tooling that can bolster data inputs and minimize the risks of GenAI to business organizations.