Databases are becoming the core infrastructure of AI-based projects, providing the foundation for use cases that require efficiency and accuracy. It’s now a necessity to use cutting-edge tools such as vector and RAG for processing AI data, and organizations are seeking databases designed to get the most out of their data.
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Summary
Databases are becoming the core infrastructure of AI-based projects, providing the foundation for use cases that require efficiency and accuracy.
The growing use of GenAI is changing how businesses manage operations and make decisions.
Databases are becoming the core infrastructure of AI-based projects, providing the foundation for use cases that require efficiency and accuracy. It’s now a necessity to use cutting-edge tools such as vector and RAG for processing AI data, and organizations are seeking databases designed to get the most out of their data. However, organizations innately proceed into this realm with different internal capabilities and maturity levels for their database strategies, which prompts variation in priorities and processes.
To gain further insight into these trends, TechTarget’s Enterprise Strategy Group surveyed 358 IT professionals at organizations in North America (US and Canada) involved with or responsible for the database technologies, processes, and programs used to manage their organization’s data.
Read Research Report: Rethinking Database Requirements in the Age of AI
Appendix
Further reading
Author
Stephen Catanzano, Senior Analyst, Data Management and Analytics
