Data Digest
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.
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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.
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Data Ethics Isn’t Just a Checkbox—It’s a Competitive Advantage
In a world increasingly shaped by data-driven decisions, trust is quickly becoming the currency of business. Consumers, partners, and regulators alike are scrutinizing how organizations collect, use, and protect data. Amid rising expectations and mounting consequences for ethical lapses, forward-thinking companies are realizing that data ethics is no longer a compliance exercise—it’s a business imperative.
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Federated data governance explained: A framework for scalable control
In today’s complex and data-saturated enterprise landscape, organizations are searching for governance practices that provide both control and flexibility. As the number of data sources, platforms, and users multiplies, traditional, one-size-fits-all governance models no longer suffice. That’s where federated data governance comes in—a scalable and adaptive framework that merges centralized oversight with decentralized governance capabilities.
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Why Data Products are the Hidden Key to Unlocking AI's True Potential
As organizations rush to embrace AI and machine learning, many are finding their efforts unraveling due to a silent saboteur lurking in their data. This isn't just a hypothetical scenario—it's the reality I see playing out every day in my role as Senior Director of Product Management at Alation.
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What Is Data Mapping? A Comprehensive Guide
Organizations today face the challenge of managing ever-increasing volumes and varieties of data. Data mapping has become a critical component of modern data governance, serving as the foundation for effective data management and compliance.
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Deploying AI Agents in Regulated Industries: What to Know
AI agents are transforming how organizations operate, make decisions, and deliver value. For regulated industries like banking, healthcare, and insurance, these AI-powered assistants promise unprecedented efficiency and innovation—but they also present unique compliance and governance challenges that data leaders must address with urgency and strategic foresight.
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How AI Agents Are Redefining Patient Care: 5 Healthcare Use Cases That Deliver
AI is rapidly entering healthcare processes, with AI agents delivering a fundamental transformation in patient care, research, and administration. But to move beyond simple automations and bring reasoning and decision-making to tackle complex processes, healthcare organizations require a foundation of data governance.
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Data Products, AI Agents, and the Future of Work: Insights from Sanjeev Mohan
In a fast-evolving digital landscape, data is no longer just a byproduct of business operations—it’s becoming the engine of innovation.
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Building Smarter AI: The Role of Metadata in Reliable Results
As companies race to harness the power of AI, many are unknowingly sabotaging their own success by overlooking a crucial component: metadata.
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The Hidden Language Crisis: Why Semantics Is Your Organization's Most Overlooked Data Asset
In my years working with organizations on their data journeys, I've observed a persistent pattern: companies pour millions into data lakes, AI initiatives, and analytics platforms, yet many still struggle to realize the promised returns on these investments. The reason isn't technical—it's linguistic. Beneath most failed data initiatives lies a fundamental language problem that's rarely addressed: semantic inconsistency.
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