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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Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized, and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are re-evaluating whether those decisions still hold up under current conditions.
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A few months ago, I was called in to discuss an IoT business case with the leadership of a prospect that had been waiting—patiently, they told me, but impatiently, I could tell—for their home build connected asset program to deliver on its promise. The use cases were well-defined. The ROI model looked compelling on paper. And yet, somehow, the data still wasn't flowing the way it needed to. Their engineers were buried.
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Data mesh and data fabric caught the attention of data leaders in the early 2020s as a compelling way to retain and deliver information across highly disparate enterprises. Then AI came into the picture in a big way, overshadowing the time and attention devoted to mesh and fabric. Lately, because data-hungry AI systems demand a seamless flow of information, there has been a resurgence of interest in the flexibility that mesh and fabric provide. To meet this need, mesh and fabric are being layered together, rather than being categorized as differing approaches to data decentralization.
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