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Data is at the core of every modern business decision, but without visibility into where it comes from, how it changes, and where it’s used, organizations risk making decisions based on incomplete or inaccurate information. Data lineage provides the critical transparency needed to track the end-to-end journey of data—from raw ingestion to transformation, reporting, and beyond. With a clear view of data movement and dependencies, businesses can drive better governance, improve data quality, and accelerate data-driven innovation.
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Key takeaways
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The world of data management is rapidly changing, pushing organizations to rethink how they extract value from their data. A key driver of this transformation (along with AI) is the rise of data products. According to the Gartner Chief Data and Analytics Officer (CDAO) Agenda Survey 2024, 50% of organizations have already deployed data products, and another 29% are actively considering them.
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AI agents are autonomous systems designed to perform tasks and make decisions by processing data and learning from their environments.
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In today’s data-driven world, data scientists are rock stars as they shape the future and command top salaries. But even these heavy hitters have a common obstacle: finding and trusting the right data to do their jobs.
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Why is data quality important for businesses today?
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As the data and analytics landscape evolves, leaders face mounting pressures to deliver value, harness AI, and navigate complex data ecosystems. From March 3-5, 2025, at the Gaylord Palms Resort & Convention Center, the Gartner Data & Analytics Summit will unite data experts and business leaders to explore cutting-edge strategies to address these challenges. This year, Alation is bringing innovation to the forefront with an agenda packed with a range of opportunities to learn, engage, and connect—which we’ll preview in this blog.
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In today's data-driven enterprises, the quest for critical knowledge often feels like searching for a needle in a haystack. Employees routinely spend countless hours hunting for relevant data, resulting in a significant productivity drain. Modern organizations can ill afford this inefficiency.
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We define a data catalog as “a repository of data assets with information about that data.” It is, literally, a catalog that users, analysts, data scientists, and others can browse to find, understand, evaluate fit for purpose, and access trusted data.
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