AI, ML, Data Science, and LLMs – are as good as the data used to train them.Data readiness is not just about having vast amounts of data, but it also encompasses data quality, accessibility, and ethical considerations. Data sets need to be robust, clean, and up-to-date to unleash the full potential of these technologies.In this whitepaper, we explore the key components of data readiness and discuss strategies to prepare data for the age of AI. From data cleansing and augmentation to implementing robust data governance frameworks, we delve into the essential steps needed to make data future-ready and unlock the true potential of artificial intelligence and its successors.
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