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What is AI-powered Data Quality?

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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Blog

Reshaping Business Processes with AI: Insights from Raza Habib, CEO of Humanloop

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.

Alation blog: Introducing the Alation Data Products Marketplace: Trusted, High-Impact Data at Speed

Blog

Introducing the Alation Data Products Marketplace: Trusted, High-Impact Data at Speed

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.

Alation blog: Announcing Alation Data Quality: Restoring Trust in Your Data

Blog

Announcing Alation Data Quality: Restoring Trust in Your Data

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.

Reinventing the Role of Data Catalogs and Governance in the Era of Agents

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Reinventing the Role of Data Catalogs and Governance in the Era of Agents

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.

Data product factory

Blog

Data Products vs Data as a Product: What’s the Difference?

Data’s value is unarguably immense. But many organizations struggle to effectively utilize their vast data resources because traditional approaches to data access and usage can’t keep up with exploding data volumes and variety of sources. This post explores two key concepts—data products and data as a product—to uncover how organizations can unlock data’s true potential.

mistake!

Blog

Five Common Mistakes When Implementing Data Products (and How to Avoid Them)

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.

AI agent chessboard

Blog

Are AI Agents the Future of Data Intelligence?

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. 

cloud migration

Blog

Why Cloud Data Governance is Critical in 2025

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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