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Rise of self-service analytics
Organizations are under growing pressure to democratize access to data and empower business users for faster decision making. The traditional model of relying solely on centralized IT and analytics teams to run reports and develop business intelligence solutions is proving woefully inadequate in today's dynamic markets. As analytics becomes more pervasive, there is a demand for self-service provision where users can discover, understand, and leverage disparate internal and external datasets on their own with limited support.
A platform that provides metadata-driven data discovery, recommendation engines, and drag-and-drop tools allows business stakeholders, data scientists, and other persona to find, prepare, and analyze data independently based on their unique needs. This spurts innovation by letting diverse user groups rapidly develop and test hypotheses without constraints. It also ensures analytics is decentralized and embedded at the point-of-use wherever insights are consumed. By surfacing comprehensive metadata through an interactive catalog, organizations can scale self-service analytics across their extended networks in a compliant and governed manner.
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