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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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As insurers look to be more agile, data mesh strategies take centerstage

CIO Business Intelligence

As data volumes continue to increase alongside a correlating number of business requests, modern insurance data leaders face a nuanced set of challenges. Accelerated demand in AI-enabled innovations has recently compounded these issues, prioritizing the need for new capabilities that require even more robust data foundations.

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Why Your Data Governance Strategy is Failing

Alation

These divergences of focus can lead to consumers feeling bogged down by overly complicated processes or leadership teams being unable to see initiative investments reap the desired rewards of their predictive business success metrics. (1). If managers rely on incomplete or unproductive metrics, this can obstruct strategy.

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Checklist to Building a Successful Data Governance Program

CDW Research Hub

Start meeting, strategizing, and creating the justification for data governance. Agree on a definition of data governance for organization. Identify where your company currently stands in data governance, while keeping in mind your master data management plan, data quality goals, data stewardship, data lineage, and metadata.

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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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Automating Model Risk Compliance: Model Development

DataRobot Blog

With this definition of model risk, how do we ensure the models we build are technically correct? The first step would be to make sure that the data used at the beginning of the model development process is thoroughly vetted, so that it is appropriate for the use case at hand. To reference SR 11-7: .

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Data Lakes: What Are They and Who Needs Them?

Jet Global

Ungoverned or uncatalogued data can leave businesses vulnerable both in terms of data quality (and organizational trust in that data), as well as in terms of security, regulatory, and compliance risks.