A novel approach from the Allen Institute for AI enables data to be removed from an artificial intelligence model even after ...

Learn how to create a scalable data model that can handle growth and complexity without compromising quality, efficiency, or usability in the context of data architecture.

Data architecture translates business needs into data and system requirements, and seeks to manage data and its flow through the enterprise.

Learn what data model governance is, why it matters, and how to implement it effectively. Discover some common data modeling techniques and how to choose the right one.

Data architects visualize and design an organization's enterprise data management framework, aligned with enterprise strategy and business architecture.

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.

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.

An open data architecture eliminates silos and allows data teams to collaborate on the same data, deliver data reliability and easily manage data. This kind of architecture is beneficial for ...

According to DAMA International’s Guide to the Data Management Body of Knowledge, enterprise data architecture typically consists of three major sets of design components. First, an enterprise data ...

What is data modeling, and why does it break down? Fundamentally, data modeling involves arranging data in a structured way to improve accessibility and use for a range of applications and analyses.