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A Guide to the Six Types of Data Quality Dashboards

DataKitchen

A Guide to the Six Types of Data Quality Dashboards Poor-quality data can derail operations, misguide strategies, and erode the trust of both customers and stakeholders. However, not all data quality dashboards are created equal. These dimensions provide a best practice grouping for assessing data quality.

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The Race For Data Quality in a Medallion Architecture

DataKitchen

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. It sounds great, but how do you prove the data is correct at each layer? How do you ensure data quality in every layer ?

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Data Quality Test Coverage In a Medallion Data Architecture

DataKitchen

Data quality test coverage has become one of the most critical challenges facing modern data engineering teams, particularly as organizations adopt the increasingly popular Medallion data architecture. The Silver layer essentially creates a cleansed, normalized version of the Bronze data without heavy aggregation.

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Data Quality Testing: A Shared Resource for Modern Data Teams

DataKitchen

Data Quality Testing: A Shared Resource for Modern Data Teams In today’s AI-driven landscape, where data is king, every role in the modern data and analytics ecosystem shares one fundamental responsibility: ensuring that incorrect data never reaches business customers. That must change.

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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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Webinar: A Guide to the Six Types of Data Quality Dashboards

DataKitchen

In this exciting webinar , Christopher Bergh discussed various types of data quality dashboards, emphasizing that effective dashboards make data health visible and drive targeted improvements by relying on concrete, actionable tests. He stressed the importance of measuring quality to demonstrate value and extend influence.

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Why Data Quality Is the Keystone of Generative AI

DataFloq

As organizations race to adopt generative AI tools-from AI writing assistants to autonomous coding platforms-one often-overlooked variable makes the difference between game-changing innovation and disastrous missteps: data quality. While often viewed as a backend or IT concern, data quality is now a strategic priority.