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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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Amazon DataZone now integrates with AWS Glue Data Quality and external data quality solutions

AWS Big Data

Today, we are pleased to announce that Amazon DataZone is now able to present data quality information for data assets. Other organizations monitor the quality of their data through third-party solutions. Additionally, Amazon DataZone now offers APIs for importing data quality scores from external systems.

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OneFamily’s response to the data quality question

CIO Business Intelligence

OneFamily is a financial services organization, operating broadly in savings and investments, and responsible for £8 billion of assets under management. But hearing those voices, and how to effectively respond, is dictated by the quality of data available, and understanding how to properly utilize it. “We

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ON-DEMAND WEBINAR: Managing Stress in Data Engineering: Data Quality and Testing Techniques for Data Observability

DataKitchen

Why do 78% of data engineers wish their job came with a therapist to help manage work-related stress? The post ON-DEMAND WEBINAR: Managing Stress in Data Engineering: Data Quality and Testing Techniques for Data Observability first appeared on DataKitchen. THEY DO NOT TEST.

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Forrester Research Report: How Sales and Marketing Intelligence Drive Improved Business Outcomes

of companies achieved a score indicating maturity in data management practices in the space.". Those implementing a B2B sales and marketing intelligence solution reported that they have realized 35% more leads in their pipeline and 45% higher-quality leads leading to higher revenue and growth. The primary takeaway?

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Measure performance of AWS Glue Data Quality for ETL pipelines

AWS Big Data

In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.

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AWS Glue Data Quality is Generally Available

AWS Big Data

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning. It takes days for data engineers to identify and implement data quality rules.

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Best Practices for a Marketing Database Cleanse

Multiple industry studies confirm that regardless of industry, revenue, or company size, poor data quality is an epidemic for marketing teams. As frustrating as contact and account data management is, this is still your database – a massive asset to your organization, even if it is rife with holes and inaccurate information.

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How to Overcome the Pain Points of Your CRM

The promise of a CRM ( customer relationship management ) led organizations to believe each could digitally transform its businesses through tracking touchpoints throughout the buyer’s journey. Combatting low adoption rates and data quality. It’s no secret, only 13% of salespeople are satisfied with their CRM.

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Why B2B Contact and Account Data Management Is Critical to Your ROI

64% of successful data-driven marketers say improving data quality is the most challenging obstacle to achieving success. The digital age has brought about increased investment in data quality solutions. Download this eBook and gain an understanding of the impact of data management on your company’s ROI.

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Top 5 Barriers to Supply Chain Network Design Adoption and How to Overcome Them

Speaker: Brian Dooley, Director SC Navigator, AIMMS, and Paul van Nierop, Supply Chain Planning Specialist, AIMMS

This on-demand webinar shares research findings from Supply Chain Insights, including the top 5 obstacles that bog you down when trying to improve your network design efforts: Poor data quality. Lack of upper management support. Lack of skilled resources. Don’t have the right tools/tools are too complex or expensive.