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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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Data transformation takes flight at Atlanta’s Hartsfield-Jackson airport

CIO Business Intelligence

At Atlanta’s Hartsfield-Jackson International Airport, an IT pilot has led to a wholesale data journey destined to transform operations at the world’s busiest airport, fueled by machine learning and generative AI. That enables the analytics team using Power BI to create a single visualization for the GM.”

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What is business analytics? Using data to improve business outcomes

CIO Business Intelligence

While quantitative analysis, operational analysis, and data visualizations are key components of business analytics, the goal is to use the insights gained to shape business decisions. What is the difference between business analytics and data analytics? Business analytics is a subset of data analytics.

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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

Upload your data, click through a workflow, walk away. If you’re a professional data scientist, you already have the knowledge and skills to test these models. That takes us to a conspicuous omission from that list of roles: the data scientists who focused on building basic models. Get your results in a few hours.

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Use AWS Glue DataBrew recipes in your AWS Glue Studio visual ETL jobs

AWS Big Data

AWS Glue Studio is a graphical interface that makes it easy to create, run, and monitor extract, transform, and load (ETL) jobs in AWS Glue. DataBrew is a visual data preparation tool that enables you to clean and normalize data without writing any code. Choose Visual with a blank canvas and create the visual job.

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Data Engineers Are Using AI to Verify Data Transformations

Wayne Yaddow

AI is transforming how senior data engineers and data scientists validate data transformations and conversions. Artificial intelligence-based verification approaches aid in the detection of anomalies, the enforcement of data integrity, and the optimization of pipelines for improved efficiency.

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What is a DataOps Engineer?

DataKitchen

The data organization wants to run the Value Pipeline as robustly as a six sigma factory, and it must be able to implement and deploy process improvements as rapidly as a Silicon Valley start-up. The data engineer builds data transformations. Their product is the data. Create tests. Run the factory.

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