The redesign of data pipelines, models, and governance frameworks is integral in facilitating the adoption of automation across asset servicing. Through re-engineering — which usually involves ...

Data scientists today face a perfect storm: an explosion of inconsistent, unstructured, multimodal data scattered across silos – and mounting pressure to turn it into accessible, AI-ready insights.

Real-time insights and self-service analytics have become critical for survival in a rapidly evolving data landscape.

If your AI feels slow, expensive or risky, the problem isn’t the models — it’s the data, and cognitive data architecture is the fix. Let’s be honest: our data systems are struggling to keep up with AI ...

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 ...

Large language models like ChatGPT and Llama-2 are notorious for their extensive memory and computational demands, making them costly to run. Trimming even a small fraction of their size can lead to ...