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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. 10) Data Quality Solutions: Key Attributes.

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How Metadata Makes Data Meaningful

erwin

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But are these rampant and often uncontrolled projects to collect metadata properly motivated? What Is Metadata? Where is it now?

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How Metadata Makes Data Meaningful

erwin

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But are these rampant and often uncontrolled projects to collect metadata properly motivated? What Is Metadata? Where is it now?

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Do I Need a Data Catalog?

erwin

If you’re serious about a data-driven strategy , you’re going to need a data catalog. Organizations need a data catalog because it enables them to create a seamless way for employees to access and consume data and business assets in an organized manner. Three Types of Metadata in a Data Catalog.

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Accenture’s Smart Data Transition Toolkit Now Available for Cloudera Data Platform

Cloudera

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

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Integrating Data Governance and Enterprise Architecture

erwin

Why should you integrate data governance (DG) and enterprise architecture (EA)? Data governance provides time-sensitive, current-state architecture information with a high level of quality. Data governance provides time-sensitive, current-state architecture information with a high level of quality.

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Case study: Policy Enforcement Automation With Semantics

Ontotext

Data leaders today are faced with an almost impossible challenge. They are expected to understand the entire data landscape and generate business-moving insights while facing the voracious needs of different teams and the constraints of technology architecture and compliance.