Most organizations are pursuing data transformation but face productivity loss due to tool sprawl. On average, data teams use 8–10 tools per data lifecycle step, slowing machine learning and AI progress. A unified, end-to-end data lakehouse can streamline workflows, reduce complexity, and boost insight generation. While data teams are becoming decentralized, consolidating data management on fewer platforms is key to improving efficiency, saving resources, and accelerating customer value.

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