If your AI feels slow, expensive or risky, the problem isn’t the models — it’s the data, and cognitive data architecture is ...

AI initiatives don’t stall because models aren’t good enough, but because data architecture lags the requirements of agentic systems.

There’s always a worry that “agentic AI” means people step aside. The reality is sort of the opposite. Agents take on the minute-by-minute decision loops, but humans define the goals, priorities, ...

Time flies in the world of data analytics and artificial intelligence (AI). Seemingly every day, new technologies rise, new use cases emerge, and new frontiers unfurl themselves before a world that ...

Incubated for DoD & Intelligence use cases, startup announces commercial availability of enterprise AI platform, delivering ...

Data center architectures are undergoing a significant change, fueled by more data and much greater usage from remote locations. Part of this shift involves the need to move some processing closer to ...

In the first quarter of 2025, nearly 60% of DBTA subscribers told us they were actively researching GenAI with LLMs, including RAG and knowledge graphs. On top of this, when asked which technologies ...

The future of the field isn't less engineering but better engineering, where people focus on design, integrity and impact ...

Over the past decade, the data boom has created exciting strategic opportunities for adaptive companies and enabled the development of entirely new enterprises. This wave was the result of the ...

A guide to the 10 most common data modeling mistakes Your email has been sent Data modeling is the process through which we represent information system objects or entities and the connections between ...