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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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How to Deliver Data Quality with Data Governance: Ryan Doupe, CDO of American Fidelity, 9-Step Process

Alation

Several weeks ago (prior to the Omicron wave), I got to attend my first conference in roughly two years: Dataversity’s Data Quality and Information Quality Conference. Ryan Doupe, Chief Data Officer of American Fidelity, held a thought-provoking session that resonated with me. Step 2: Data Definitions.

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Set up advanced rules to validate quality of multiple datasets with AWS Glue Data Quality

AWS Big Data

Poor-quality data can lead to incorrect insights, bad decisions, and lost opportunities. AWS Glue Data Quality measures and monitors the quality of your dataset. It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines.

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Top 10 Data Lineage Podcasts, Blogs, and Magazines

Octopai

Our list of Top 10 Data Lineage Podcasts, Blogs, and Websites To Follow in 2021. Data Engineering Podcast. This podcast centers around data management and investigates a different aspect of this field each week. The host is Tobias Macey, an engineer with many years of experience. Agile Data.

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Webinar Summary: Agile, DataOps, and Data Team Excellence

DataKitchen

The hosted by Christopher Bergh with Gil Benghiat from DataKitchen covered a comprehensive range of topics centered around improving the performance and efficiency of data teams through Agile and DataOps methodologies. The goal is to reduce errors and operational overhead, allowing data teams to focus on delivering value.

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The DataOps Vendor Landscape, 2021

DataKitchen

Read the complete blog below for a more detailed description of the vendors and their capabilities. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. QuerySurge – Continuously detect data issues in your delivery pipelines. Data breaks.

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Empowering data mesh: The tools to deliver BI excellence

erwin

The data mesh approach distributes data ownership and decentralizes data architecture, paving the way for enhanced agility and scalability. With distributed ownership there is a need for effective governance to ensure the success of any data initiative. Business Glossaries – what is the business meaning of our data?