Remove Data Quality Remove Metadata Remove Risk
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It’s 2025. Are your data strategies strong enough to de-risk AI adoption?

CIO Business Intelligence

Lack of oversight establishes a different kind of risk, with shadow IT posing significant security threats to organisations. Strong data strategies de-risk AI adoption, removing barriers to performance. AI thrives on clean, contextualised, and accessible data.

Risk 111
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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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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

Yet, despite growing investments in advanced analytics and AI, organizations continue to grapple with a persistent and often underestimated challenge: poor data quality. Fragmented systems, inconsistent definitions, legacy infrastructure and manual workarounds introduce critical risks.

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7 Benefits of Metadata Management

erwin

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. What Is Metadata? Harvest data.

Metadata 110
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Data Governance and Metadata Management: You Can’t Have One Without the Other

erwin

When an organization’s data governance and metadata management programs work in harmony, then everything is easier. Data governance is a complex but critical practice. There’s always more data to handle, much of it unstructured; more data sources, like IoT, more points of integration, and more regulatory compliance requirements.

Metadata 135
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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

Globally, financial institutions have been experiencing similar issues, prompting a widespread reassessment of traditional data management approaches. 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.

Metadata 105