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 ...

Change is driven by the huge amount of data growth, with organizations now running workflows typically seen in High Performance Computing (HPC). Compute Systems, whether at the core, the cloud, or the ...

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 ...

Data modeling is the procedure of crafting a visual representation of an entire information system or portions of it in order to convey connections between data points and structures. The objective is ...

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 ...

In an era where data is a strategic asset, organizations often falter not because they lack data—but because their architecture doesn’t scale with their needs. Leaders must design data ecosystems that ...

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 ...

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 headless data architecture means no longer having to coordinate multiple copies of data and being free to use whatever processing or query engine is most suitable for the job. Here’s how it works.