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Being Data-Driven Means Embracing Data Quality and Consistency Through Data Governance

Dataversity

The post Being Data-Driven Means Embracing Data Quality and Consistency Through Data Governance appeared first on DATAVERSITY. They want to improve their decision making, shifting the process to be more quantitative and less based on gut and experience.

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Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC

Smart Data Collective

Companies with an in-depth understanding of data analytics will have more successful Amazon PPC marketing strategies. However, it is important to make sure the data is reliable. There is so much data out there that can help you find out more about the people that you intend to sell to.

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Master Data Governance in a Multi-Cloud Environment

Smart Data Collective

Mastering data governance in a multi-cloud environment is key! Delve into best practices for seamless integration, compliance, and data quality management.

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Good Data Quality Is the Secret to Successful GenAI Implementation

Dataversity

So why are many technology leaders attempting to adopt GenAI technologies before ensuring their data quality can be trusted? Reliable and consistent data is the bedrock of a successful AI strategy.

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How to Overcome the Pain Points of Your CRM

However, as a company, sales stack, and database grow, it becomes difficult to uphold structure and governance to keep a CRM up-to-date. Combatting low adoption rates and data quality. When used effectively, a CRM can be the lifeblood of your sales team – keeping everyone organized, efficient, and at peak productivity.

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The Cool Kids Corner: Data Quality Is Not a Fish You Can Catch 

Dataversity

This is my monthly check-in to share with you the people and ideas I encounter as a data evangelist with DATAVERSITY. This month we’re talking about Data Quality (DQ). Read last month’s column here.)

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How to Implement a Data Quality Framework

Dataversity

They have the data they need, but due to the presence of intolerable defects, they cannot use it as needed. These defects – also called Data Quality issues – must be fetched and fixed so that data can be used for successful business […].