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

Data modeling, at its core, is the process of transforming raw data into meaningful insights. It involves creating representations of a database’s structure and organization. These models are often ...

Forbes contributors publish independent expert analyses and insights. I write about the economics of AI. When Snowflake announced its $250 million acquisition of Crunchy Data two weeks ago at its ...

Amazon Web Services's AI Shanghai Lablet division has created a new predictive model -- an open-source benchmarking tool called 4DBInfer used to graph predictive modeling on RDBs, a relational ...

Data modeling tools play an important role in business, representing how data flows through an organization. It’s important for businesses to understand what the best data modeling tools are across ...

Vector databases and search aren’t new, but vectorization is essential for generative AI and working with LLMs. Here's what you need to know. One of my first projects as a software developer was ...

The addition of Transformational Modeling, Tx, allows data teams to simplify, automate, and collaborate on their end-to-end data modeling workflows. SAN FRANCISCO--(BUSINESS WIRE)--SqlDBM, a leading ...

At a time when every enterprise looks to leverage generative artificial intelligence, data sites are turning their attention to graph databases and knowledge graphs. The global graph database market ...

This article was written by Bloomberg Intelligence senior industry analyst Mandeep Singh and associate analyst Robert Biggar. It appeared first on the Bloomberg Terminal. AI’s shift to inference at ...

SAN FRANCISCO--(BUSINESS WIRE)--Cyber risk analytics leader CyberCube has launched the world’s first set of detailed Exposure Databases to enable (re)insurers and brokers to perform a wide array of ...