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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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The DataOps Vendor Landscape, 2021

DataKitchen

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Data breaks.

Testing 300
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Optimizing Risk and Exposure Management – Roundtable Highlights

Cloudera

We recently hosted a roundtable focused on o ptimizing risk and exposure management with data insights. The regulatory oversight coupled with potential AI applications launched a discussion about the quality of the data – the classic “garbage-in, garbage-out” challenge.

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Oracle makes its pitch for the enterprise cloud. Should CIOs listen?

CIO Business Intelligence

Oracle Cloud Infrastructure is now capable of hosting a full range of traditional and modern IT workloads, and for many enterprise customers, Oracle is a proven vendor,” says David Wright, vice president of research for cloud infrastructure strategies at research firm Gartner.

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Saving Data Costs with Data Lineage

Octopai

How can you save your organizational data management and hosting cost using automated data lineage. Do you think you did everything already to save organizational data management costs? What kind of costs organization has that data lineage can help with? Well, you probably haven’t done this yet!

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack. Moreover, running advanced analytics and ML on disparate data sources proved challenging.

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Introducing the technology behind watsonx.ai, IBM’s AI and data platform for enterprise

IBM Big Data Hub

Over the past decade, deep learning arose from a seismic collision of data availability and sheer compute power, enabling a host of impressive AI capabilities. Data: the foundation of your foundation model Data quality matters. When objectionable data is identified, we remove it, retrain the model, and repeat.