Data is the foundation of AI. It acts as the experiential element, providing AI systems with the necessary information to understand patterns, make decisions, and predict outcomes. From simple algorithms to complex neural networks, the quality, quantity, and variety of data directly influence the effectiveness of AI solutions. To support organizations in navigating through new challenges and a rapidly evolving Big Data ecosystem, Big Data Quarterly presents 2026's "Data and AI 75," a list of companies driving innovation and expanding what is possible in terms of collecting, storing, and extracting value from data.
Read More
Today's enterprises have adopted AI with one promise in mind: efficiency. In the mindset of most IT leaders, efficiency equals simpler workflows, easier data access, faster results, and lighter burdens for IT teams. Unfortunately, according to a recent IT trends study, 71% of IT professionals report AI is actually making their role more demanding. Although AI has proven its ability to fast-track individual tasks, attaching AI technology to legacy processes has revealed a trap hidden under that promised simplicity.
Read More
Today, Information Today, Inc. released the "AI-Readiness in Enterprise Data Architecture" survey. The research shows stronger results depend on a three-part "readiness stack": the data foundation, operating architecture, and governance discipline.
Read More
AI started by helping developers write code faster. Now it's beginning to participate across the software delivery lifecycle (SDLC). As the SDLC becomes increasingly agentic, AI can create changes, test and validate them, initiate deployments, observe what happens in production, and increasingly help remediate issues. The architecture of the SDLC doesn't disappear but the actors moving through it change, and the speed and volume of change increase dramatically.
Read More