To govern AI safely and keep its speed advantage, enterprises must move from static, rule-based control systems to adaptive, ...

Traditional data architectures are rigid and siloed, limiting agility, experimentation and the cross-domain insights required ...

Unified metadata models are emerging as a cornerstone in the changing landscape of data management, highlighting the critical need for integrated frameworks that ensure data accessibility, security ...

Healthcare AI is growing up: instead of one massive model, 2026 favors teams of smaller, specialized models that collaborate, ...

Krishnam Narsepalle argues traditional credit systems must evolve into event-driven architectures for real-time risk ...

Health systems are entering an era of intelligent data management where real-time validation, data quality scoring and robust ...

Anil Lokesh Gadi, a distinguished expert in the fields of advanced data engineering, data analytics, and data warehousing, has recently published a research paper providing valuable insights into ...

Industry leaders say decentralised ownership plus central oversight, not invented soundbites, are what will scale AI responsibly. As companies embed AI deeper into core operations, decisions about ...

"Our goal is to continually improve management of state government information and knowledge assets." -- Oregon CIO Dugan Petty, co-chair of the NASCIO Enterprise Architecture and Governance Committee ...

Data governance is an umbrella term encompassing several different disciplines and practices, and the priorities often depend on who is driving the effort. Chief data officers, privacy officers, ...