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Modern organizations face a persistent challenge: balancing rapid insights with the need for trust, security, and compliance. Business leaders need fast access to data, but centralized data teams often become bottlenecks, delaying decision-making. Conversely, decentralized teams promote agility but can introduce inconsistencies in quality and governance. This tension, known as the "Speed vs. Trust Conflict," prevents organizations from fully harnessing their data.
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Today, quality data can often spell the difference between business success and failure. In fact, Gartner projects that poor data quality costs the average business about $12.9 million each year. Small wonder, as poor data quality leads to flawed AI models, operational errors, and costly decisions – creating distrust between data producers and consumers. This lack of trust can severely hinder an organization's ability to make informed decisions and achieve desired outcomes.
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Data duplication is a divisive topic—some see it as essential for flexibility and performance, while others view it as a source of confusion and inefficiency. The reality is that duplication itself is neither inherently good nor bad; its impact depends on the reasons behind it and how it is managed.
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Alation was thrilled to participate in the Gartner Data & Analytics Summit 2025, held in sunny Orlando, Florida. As the creator of the modern data catalog, we have continually evolved the platform to meet the changing needs of data teams — first by integrating data governance, and now by leading the market in the reinvention of the data catalog as an agentic data platform.
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Tom Davenport is a distinguished thought leader in data, analytics, and artificial intelligence, shaping modern business thinking through more than 20 influential books and numerous articles.
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Data protection and privacy have become increasingly critical as digital transformation accelerates and regulatory landscapes evolve. A Data Protection Impact Assessment (DPIA) is an essential tool for organizations to manage privacy risks associated with processing personal data.
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The pressure to deliver AI-driven innovation has never been higher, whether you are using off-the-shelf solutions or creating custom tools to meet specific business needs. While many AI teams are in the experimentation stage, piloting new AI projects, leading companies are already capturing substantial returns on investment. According to a recent Deloitte survey, 74% of respondents reported advanced Generative AI initiatives are meeting or exceeding ROI expectations, and 78% of respondents expect to increase their AI spending next year.
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AI is reshaping industries and dominating conversations across organizations. Yet, with innovations emerging rapidly, many struggle to understand the nuances between popular AI technologies—and even fewer fully grasp how crucial data quality is for achieving AI success. This blog post defines key AI terms and explains why high-quality data, supported by robust data governance, is foundational for realizing AI’s true potential.
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Large Language Models (LLMs) are revolutionizing business operations, transforming how organizations approach automation, decision-making, and efficiency. In a conversation on Data Radicals, Raza Habib, co-founder and CEO of Humanloop, shared his insights on the evolving landscape of AI, including prompt engineering, fine-tuning, AI agents, and the role of data product managers.
Blog
Today, quality data can often spell the difference between business success and failure. In fact, Gartner projects that poor data quality costs the average business about $12.9 million each year. Small wonder, as poor data quality leads to flawed AI models, operational errors, and costly decisions – creating distrust between data producers and consumers. This lack of trust can severely hinder an organization's ability to make informed decisions and achieve desired outcomes.
Blog
Modern organizations face a persistent challenge: balancing rapid insights with the need for trust, security, and compliance. Business leaders need fast access to data, but centralized data teams often become bottlenecks, delaying decision-making. Conversely, decentralized teams promote agility but can introduce inconsistencies in quality and governance. This tension, known as the "Speed vs. Trust Conflict," prevents organizations from fully harnessing their data.
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