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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Kenvue’s Best Practices for Building Data Products That Deliver Value
If you’re in the business of making data usable—and valuable—you know the struggle: how do you move fast, deliver impact, and maintain trust all at the same time?
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From Trust to Transformation: Elevating Data Quality with AI and Catalogs
In an era where data drives nearly every strategic decision, poor data quality isn’t just a nuisance—it’s a risk. For modern enterprises, the consequences of unreliable data can range from operational inefficiencies to financial loss and reputational damage. As the Senior Director of Product Management at Alation, I’ve had the chance to develop and lead our data quality strategy and have seen firsthand how critical data health is to organizational success.
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What Is Cloud Data Security?
Data is the lifeblood of organizations across all industries in today's digital age. As businesses increasingly migrate their information to cloud-based platforms, ensuring the security of this data has become a top priority. Cloud data security involves implementing robust measures to protect sensitive information stored in cloud environments from unauthorized access, data breaches, and other cyber threats.
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What Is Data Annotation? A Guide to Improving AI Model Performance
What is data annotation?
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What Is Data Cleansing? Best Practices for AI Success
Organizations across various industries rely heavily on accurate and reliable data to make informed decisions and drive business success. However, data collected from multiple sources often contains errors, inconsistencies, and duplicates that can lead to inaccurate insights and poor decision-making. Data cleansing addresses this challenge.
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Why CIOs Need a Discovery Platform for Data Products
In an increasingly data-driven world, Chief Information Officers (CIOs) face the pressing challenge of effectively managing and leveraging vast amounts of organizational data. Implementing a discovery platform for data products provides a strategic solution, enhancing data governance, accessibility, and ultimately driving business value. This blog explores why adopting such a platform is essential, outlines key implementation steps, and provides actionable resources and best practices to facilitate success.
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What Is a Data Loss Prevention Policy?
In today’s digital-first economy, data is among the most valuable—and vulnerable—assets a business can possess. From sensitive customer information to proprietary algorithms, data powers operations, innovation, and decision-making. But as data volumes grow and cloud-based collaboration becomes the norm, the risk of accidental exposure or malicious exfiltration rises dramatically.
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Why Data Quality Matters to Different Users Across the Enterprise
Few would question the value of data quality in the enterprise today. A survey by Wakefield Research of data management professionals found that over half of the respondents indicated that 25% or more of their revenue was affected by data quality issues, underscoring the direct impact of data quality on a company's bottom line. That same survey found an increase in data downtime, partially explained by a 166% increase in the average time to resolve data quality issues, rising to an average of 15 hours per incident, highlighting the significant operational inefficiencies poor data quality can cause.
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Data Quality Metrics for Enterprise AI Models
Data quality is a critical facet of AI through every phase of its development. It encompasses dimensions like accuracy, completeness, consistency, and timeliness.
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