Most projects benefit from having a data model. Read this article for an overview of the most common types.

Alternatively, if a solution is being built internally, an existing canonical data model can be a valuable jump-start to the process of building a physical database design covering one or more areas ...

Increasingly, customers are demanding that their databases take on multiple chores. Emergence of multi-model databases providing developers multiple paths through APIs is the latest evidence of ...

One of the most important steps in desiging a database is establishing the data model. Part one of a two-part article describes how to create a logical model.

This approach features a centralized database linked to other data stores with a common data model that carries information from one point to another, without the need to rewrite code.

In a graph database, every element contains a direct pointer to its adjacent element and no index lookups are necessary. Before looking into a graph database provider, make sure your intended use ...

Data models are used to represent real-world entities, but often have limitations. Avoid common data modeling mistakes for data integrity.

Better data annotation—more accurate, detailed or contextually rich—can drastically improve an AI system’s performance, adaptability and fairness.