Prerequisite: None
Artificial intelligence is fundamentally transforming enterprise data architecture, challenging data architects to extend existing infrastructures for advanced AI capabilities. As businesses adapt to AI-driven operations, modern data architectures are evolving to accommodate diverse data types and embracing flexible storage solutions like data lakes and lakehouses.
The implementation of semantic search enhances data discovery, while retrieval-augmented generation (RAG) improves AI accuracy in enterprise contexts. Integrating generative AI capabilities into enterprise systems opens new possibilities for data-driven innovation.
At the heart of this transformation lies the open semantic layer, bridging the gap between raw data and AI applications. These technologies are revolutionizing data management, analytics, and decision-making processes. Forward-thinking executives must understand these trends to effectively prepare their organizations for the AI-driven future of enterprise data architecture, ensuring competitive advantage in an increasingly data-centric business landscape.