Data architecture, however, spans the organization and takes a high-level, holistic view, whereas data modeling focuses on specific systems or business cases. In any case, the architecture or ...

The goal of data architecture is to translate business needs into data and system requirements, and to manage data and its flow through the enterprise. ... Data architecture vs. data modeling.

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 ...

Who needs rewrites? This metadata-powered architecture fuses AI and ETL so smoothly, it turns pipelines into self-evolving ...

A novel approach from the Allen Institute for AI enables data to be removed from an artificial intelligence model even after it has already been used for training.

Certainly, an effective data architecture needs to map the flow of information through the organisation. ... But this still needs to tie into the overall data model the business is working towards.

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.

CHICAGO--(BUSINESS WIRE)--Today, DataForge, a leading data architecture solution provider, introduces the market’s first scalable, automated, and prepackaged data architecture platform. Tailor ...

Data architecture, guidelines + industry standards: Health systems’ recipe for generating value from information assets Healthcare organizations are awash in data.