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.
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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.
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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.
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Enterprise companies persist in making significant investments in data warehouses, cloud solutions, and business intelligence tools, but numerous executives still doubt the figures generated by those systems. A well-structured Data Trust Framework fills a void that conventional data validation approaches miss: It makes the distinction between a pipeline that operates effectively and a report that executives genuinely trust.
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