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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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7 enterprise data strategy trends

CIO Business Intelligence

Every enterprise needs a data strategy that clearly defines the technologies, processes, people, and rules needed to safely and securely manage its information assets and practices. Here’s a quick rundown of seven major trends that will likely reshape your organization’s current data strategy in the days and months ahead.

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Visualize data quality scores and metrics generated by AWS Glue Data Quality

AWS Big Data

AWS Glue Data Quality allows you to measure and monitor the quality of data in your data repositories. It’s important for business users to be able to see quality scores and metrics to make confident business decisions and debug data quality issues. An AWS Glue crawler crawls the results.

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The change management Informatica needed to overhaul its business model

CIO Business Intelligence

The business transformation we’ve led for the last several years has many parts; all our products have been re-built as cloud products and we continue our heritage of being neutral about data, supporting cloud, on premise, and hybrid workloads. Look at changing metrics and KPIs as a gift. Take sales territories for example.

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UK Power Networks gets smart about cloud transformation

CIO Business Intelligence

Webb’s approach contrasts to that of many enterprises that went all-in quickly on the cloud — only to now be rethinking those strategies in light of unanticipated cost overruns. At UK Power Networks, reliability remains the ultimate metric of concern. “We “I think we made the wise decision to walk instead of running to the cloud.”

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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

This stack creates the following resources and necessary permissions to integrate the services: Data stream – With Amazon Kinesis Data Streams , you can send data from your streaming source to a data stream to ingest the data into a Redshift data warehouse. version cluster. version cluster.

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Manage your workloads better using Amazon Redshift Workload Management

AWS Big Data

With Amazon Redshift , you can run a complex mix of workloads on your data warehouse, such as frequent data loads running alongside business-critical dashboard queries and complex transformation jobs. We also see more and more data science and machine learning (ML) workloads. ExampleCorp has multiple Redshift clusters.