{"id":2238,"date":"2025-11-12T07:12:34","date_gmt":"2025-11-12T07:12:34","guid":{"rendered":"https:\/\/www.newevol.io\/resources\/?p=2238"},"modified":"2025-11-12T07:12:36","modified_gmt":"2025-11-12T07:12:36","slug":"build-ai-ready-data-lake","status":"publish","type":"post","link":"https:\/\/www.newevol.io\/resources\/blog\/build-ai-ready-data-lake\/","title":{"rendered":"Building a Data Lake for GenAI and ML"},"content":{"rendered":"

The next wave of digital transformation is being powered by Generative AI (GenAI) and Machine Learning (ML) — technologies that rely on massive volumes of clean, contextual, and connected data. But most enterprises still struggle with fragmented data silos, inconsistent governance, and legacy architectures that can’t support the scale or speed required by modern AI models.<\/p>\n

Cloud-native data lakes cut total data management costs by 35%<\/a>, proving efficiency and scalability can coexist.<\/p>\n

This is why data lakes have become the cornerstone of AI-ready infrastructure. They provide a unified environment to store, organize, and process raw data at scale<\/a>, enabling seamless access for analytics, training, and experimentation.<\/p>\n

For Malaysian enterprises accelerating their AI journeys — across sectors like banking, manufacturing, telecom, and government — building a future-proof data lake is not just a technical initiative. It’s a strategic investment in intelligence.<\/p>\n

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