As 2023 rolled along, and the generative AI announcements began to accumulate, the wind picked up and drove vendors toward the necessary discussion about the data on which the models were trained, how the models were trained, what biases may have been introduced, and how those inputs (among others) might affect the outputs of those LLMs – toxicity, bias, hallucinations, factually wrong responses, etc.
According to Omdia’s AI and intelligent automation research director Natalia Modjeska, one major challenge companies face is data. “Specifically, high-quality, scalable, reliable, and trusted enterprise data to fine-tune and ground foundational models to make them usable. We know that many organizations still struggle with that because of the chronic underinvestment in data quality, management, and governance.
