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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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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Knowledge Graphs: Redefining Data Management for the Modern Enterprise

Ontotext

In the current data management landscape, enterprises have to deal with diverse and dispersed data at unimaginable volumes. Among this complexity of siloed data and content, valuable business insights and opportunities get lost. This is a core component of most data fabric based implementations.

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What is a Data Mesh?

DataKitchen

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. DataOps helps the data mesh deliver greater business agility by enabling decentralized domains to work in concert. . But first, let’s define the data mesh design pattern.

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3 Surprising Data Catalog Use Cases for Enterprises

Octopai

Establishing a single, enterprise-wide source of truth? Increasing data quality and accuracy? Why are data catalog use cases so downright… predictable? Here are three ways enterprises can leverage their data catalogs that don’t make the standard lists. The data catalog as an HR tool? Yeah, yeah.

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12 Cloud Computing Risks & Challenges Businesses Are Facing In These Days

datapine

3) Cloud Computing Benefits. It provides better data storage, data security, flexibility, improved organizational visibility, smoother processes, extra data intelligence, increased collaboration between employees, and changes the workflow of small businesses and large enterprises to help them make better decisions while decreasing costs.

Risk 237
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Third, any commitment to a disruptive technology (including data-intensive and AI implementations) must start with a business strategy. 3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)?

Strategy 290