Innovation in the big data and analytics space continues to blossom, with more companies than ever before riding the high of generative AI. Along with the myriad possibilities of this new sphere of artificial intelligence comes some questions and concerns. Startups are emerging to address challenges and leverage opportunities this new area may bring. Here are 30 startups DBTA thinks are worth watching in 2024.
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The market is saturated with thousands of purpose-built database systems. This creates too much noise for analysts and scientists dealing with vital data problems across numerous important application domains: genomics, geospatial, and more. To solve this problem, Stavros Papadopoulos, founder and CEO at TileDB, Inc., is introducing the concept of a universal database, which had heretofore been a missed opportunity in the database community.
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AI talent is in high demand; AI reveals brain oscillations for memory and disease; AI may allow us to talk to whales; AI can help plan meals and other tasks; an AI platform for dentistry just raised $53 million. That was the tip of the AI news iceberg—in one day. Has technology ever garnered so much interest, so widely, so fast? Amid the hype lies the incalculable promise of AI—and the fear of getting left behind by competitors that figure it out first. It's no wonder that organizations are rushing to implement AI, ready or not. The trouble is, many are not ready.
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AI—both operational and generative—is knocking on enterprises' doors, forcing data managers to make new architectural choices on what it will take to support these data-hungry initiatives. Data mesh and data fabric are increasingly favored to help organizations get better control of their data, but which is the best option?
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