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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 Quality Is In the Eye of the Beholder

TDAN

1 In this article, I will apply it to the topic of data quality. I will do so by comparing two butterflies, each that represent a common use of data quality: firstly and most commonly in situ for existing systems, and secondly for use […]. We know the phrase, “Beauty is in the eye of the beholder.”1

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Don’t Fear Artificial Intelligence; Embrace it Through Data Governance

CIO Business Intelligence

Preparing for an artificial intelligence (AI)-fueled future, one where we can enjoy the clear benefits the technology brings while also the mitigating risks, requires more than one article. This first article emphasizes data as the ‘foundation-stone’ of AI-based initiatives. Establishing a Data Foundation.

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5 Ways Data Engineers Can Support Data Governance

Alation

These data requirements could be satisfied with a strong data governance strategy. Governance can — and should — be the responsibility of every data user, though how that’s achieved will depend on the role within the organization. Low quality In many scenarios, there is no one responsible for data administration.

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Data Professional Introspective: Accelerating Enterprise Data Quality

TDAN

My recent columns have focused on actionable initiatives that can both deliver business value, providing a tangible achievement, and raise the profile of the data management organization data management organization (DMO).(For In that light, let’s […]

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All in the Data: Data Governance Made Easy

TDAN

Organizations that have implemented Data Governance programs, or Information Governance, Data/Information Management or Records Management programs will be the first to tell you that these data disciplines are not easy to operationalize. Data Management requires that the organization care for data as an asset.

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Data Governance Roles and Responsibilities

TDAN

Roles and responsibilities are the backbone of a successful information or data governance program. To communicate effectively […].