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Data is the lifeblood of the digital transformation engines powered by artificial intelligence and machine learning. GPUs require enormous amounts of data moving at scale to run optimally and efficiently, and data quality and the ability to effectively train AI and ML models are becoming competitive differentiators. However, many organizations are struggling to fuel their GPUs with enough quality data, due to legacy data architectures and data management approaches that were conceived and constructed before the emergence of the cloud and AI era. A recent survey found that data management is the most frequently cited technical inhibitor to AI initiatives, and data platforms have emerged as an effective solution.