As organizations scale their AI and analytics efforts, a data architecture that is 'up to snuff' is even more critical to business success. Data teams are being asked to deliver faster insights, ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

The headless data architecture is the formalization of a data access layer at the center of your organization. Encompassing both streams and tables, it provides consistent data access for both ...

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.