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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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What is data governance? Best practices for managing data assets

CIO Business Intelligence

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

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Why Your Data Governance Strategy is Failing

Alation

What is data governance and how do you measure success? Data governance is a system for answering core questions about data. It begins with establishing key parameters: What is data, who can use it, how can they use it, and why? Why is your data governance strategy failing?

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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. How can data engineers address these challenges directly?

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How AWS helped Altron Group accelerate their vision for optimized customer engagement

AWS Big Data

Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices. Goals – Lay the foundation for a data platform that can be used in the future by internal and external stakeholders.

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Financial Dashboard: Definition, Examples, and How-tos

FineReport

Contemporary dashboards surpass basic visualization and reporting by utilizing financial analytics to amalgamate diverse financial and accounting data, empowering analysts to delve further into the data and uncover valuable insights that can optimize cost-efficiency and enhance profitability.

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The DataOps Vendor Landscape, 2021

DataKitchen

Because it is such a new category, both overly narrow and overly broad definitions of DataOps abound. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. .

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