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Metadata management performs a critical role within the modern data management stack. It helps blur data silos, and empowers data and analytics teams to better understand the context and quality of data. This, in turn, builds trust in data and the decision-making to follow. However, as data volumes continue to grow, manual approaches to metadata management are sub-optimal and can result in missed opportunities. Suppose that a new data asset becomes available but remains hidden from your data consumers because of improper or inadequate tagging. How do you keep pace with growing data volumes and increased demand from data consumers and deliver real-time data governance for trusted outcomes?