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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

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Octopai Acquisition Enhances Metadata Management to Trust Data Across Entire Data Estate

Cloudera

By adding the Octopai platform, Cloudera customers will benefit from: Enhanced Data Discovery: Octopai’s automated data discovery enables instantaneous search and location of desired data across multiple systems. This guarantees data quality and automates the laborious, manual processes required to maintain data reliability.

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It’s 2025. Are your data strategies strong enough to de-risk AI adoption?

CIO Business Intelligence

If youre not keeping up the fundamentals of data and data management, your ability to adopt AIat whatever stage you are at in your AI journeywill be impacted, Kulkarni points out. This in turn stimulates a more agile and adaptable approach to AI which can accelerate its uptake and the returns that the organisation can expect.

Risk 111
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Best Practices for Metadata Management

Alation

What Is Metadata? Metadata is information about data. A clothing catalog or dictionary are both examples of metadata repositories. Indeed, a popular online catalog, like Amazon, offers rich metadata around products to guide shoppers: ratings, reviews, and product details are all examples of metadata.

Metadata 105
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2024 Gartner Market Guide To DataOps

DataKitchen

2024 Gartner Market Guide To DataOps We at DataKitchen are thrilled to see the publication of the Gartner Market Guide to DataOps, a milestone in the evolution of this critical software category. At DataKitchen, we think of this is a ‘meta-orchestration’ of the code and tools acting upon the data. Contact us to learn more!

Marketing 124
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How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

AWS Big Data

Domain ownership recognizes that the teams generating the data have the deepest understanding of it and are therefore best suited to manage, govern, and share it effectively. This principle makes sure data accountability remains close to the source, fostering higher data quality and relevance.

Metadata 105
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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

With improved access and collaboration, you’ll be able to create and securely share analytics and AI artifacts and bring data and AI products to market faster. Data teams struggle to find a unified approach that enables effortless discovery, understanding, and assurance of data quality and security across various sources.