As agentic and RAG systems move into production, retrieval quality is emerging as a quiet failure point — one that can ...

Tabular foundation models are the next major unlock for AI adoption, especially in industries sitting on massive databases of ...

Most enterprise data lives outside databases. Here's why that's holding AI back — and how connecting context can change it.

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...

Learn how this new standard connects AI to your data, enhances Web3 decision-making, and enables modular AI systems.

For financial institutions, threat modeling must shift away from diagrams focused purely on code to a life cycle view ...

Training artificial intelligence models is costly. Researchers estimate that training costs for the largest frontier models ...

At its heart, data modeling is about understanding how data flows through a system. Just as a map can help us understand a city’s layout, data modeling can help us understand the complexities of a ...

Artificial Intelligence is expanding rapidly, but it faces a significant barrier: data privacy. Companies hold massive ...

Count data modelling comprises a suite of statistical techniques dedicated to analysing non-negative integer-valued observations. Such data often arise in a variety of contexts including epidemiology, ...

Occasionally one may hear that a data model is “over-normalized,” but just what does that mean? Normalization is intended to analyze the functional dependencies across a set of data. The goal is to ...