P2P

71 I L T A N E T . O R G FEATURES • Data Scale and Integration: Datasets are growing exponentially and becoming more complex, thanks partly to duplicate, unstructured, and multi-modal data (e.g., video, images, audio). Training or grounding Gen AI models becomes more challenging, costly, and time-consuming on the back end without proper data preparation on the front end (e.g., tagging, metadata, de-duping, homogenizing formats). Data governance facilitates this by establishing universal data definitions and formats, developing standardized processes, and guiding data integration tool selection and use. Such processes help ensure the seamless integration of data across different sources, enabling organizations to effectively utilize large volumes of data from diverse sources for AI applications without extensive data cleansing or transformation processes. • Transparency and Explainability: Aside from hallucinations, one of the biggest concerns about Gen AI is the "black box" obscurity of how large language models (LLMs) source the information they use to generate their results. The mistrust and doubt generated in response to the sourcing ambiguity around many LLMs is a frequent hindrance to Gen AI adoption, leading to decreased efficiency and productivity, the inability to effectively capitalize on institutional or industry knowledge, reduced competitiveness and customer service, and wasted Gen AI investments.

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