Discover four foundational elements of AI architecture that will endure as models continue to advance: data quality, context ...

Every data modernization effort starts with a blueprint. The architecture looks clean. The data flows are defined. The platform choice is justified. Whether it is a data warehouse, a data lake or a ...

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 ...

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 evolution of data architecture is accelerating. In 2025, 85% of DBTA subscribers reported plans to modernize their data platforms—driven largely by the explosive rise of GenAI and large language ...

LFM2.5-230M proves that while 3-billion-parameter models like VibeThinker are solving advanced calculus, a ...

Industry discussions about what’s holding back AI often focus on security, graphics processing unit availability and other ...

Data models are used to represent real-world entities, but they often have limitations. Avoid these common data modeling mistakes to keep data integrity. Data modeling is the process through which we ...

AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented ...

Part of the SD Times 100 2026 series. See the full SD Times 100 2026 list for every category and honoree. Every conversation ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.