Modern Data Architecture for AI

As AI adoption continues to accelerate, many organizations now face the challenge of extending their existing data infrastructure to support the unique demands of AI applications. This means revisiting architecture principles for collecting, storing, processing, and governing data.

The shift to cloud platforms that offer scalable data storage and broad data processing capabilities is a good starting point. Some additional components and skills need to be considered. To enable semantic search, RAG, and generative AI, the integration of LLMs and the use of NLP, knowledge graphs, and vector embeddings will be required. An open semantic layer will be essential to bridge the gap between raw data and active metadata and provide a consistent view of data across various tools and applications. Finally, there is the ever-growing need for real-time data alongside fine-tuned privacy and security.

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