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.
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.
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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.
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When we started Alation in 2012, our mission was clear: to help organizations navigate the complexity of data. At the time, data was exploding in volume, yet there was no easy way for businesses to find, trust, and use it effectively. So, we pioneered the modern data catalog, introducing a solution that empowered data teams and made self-service analytics possible. Since then, a lot has changed.
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.
Blog
If you work in data, you've likely encountered the term "data products." Whether you're just starting to explore the concept or already deep into implementation, understanding both the benefits and challenges of data products is essential. When done right, they enhance data accessibility, drive business value, and foster innovation. However, common pitfalls can slow progress and create skepticism about the effectiveness of data products.
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The way we understand and use data is on the brink of transformation. In fact, AI agents are poised to redefine data – and data intelligence – as we know it.
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The rapid adoption of cloud environments has been fueled by the promise of cost savings, scalability, and the ability to share data globally. As organizations embrace the cloud, effective data governance has emerged as a critical component to ensure that data remains high-quality, secure, and compliant.
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The explosion of data has reshaped how organizations operate. With data now a critical strategic asset, companies are moving away from traditional data management approaches and embracing data products—structured, reusable, and high-quality data assets designed for specific business purposes.
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Organizations are awash in data, but not all data is equally important. Understanding and managing your most vital bits of information is critical to success.
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