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Who Is Responsible for Data Quality Within an Enterprise?

Dataiku

Poor data quality is troublesome for a myriad of reasons, from analyst productivity lost, to bad reporting and decision making, to potential negative impacts on revenue.

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Microsoft and Cognizant team up to boost enterprise Copilot adoption

CIO Business Intelligence

Microsoft is opening another route for extending the reach of its Copilot offerings in the enterprise through an expanded partnership with global professional services company Cognizant. Therefore, while AI can reduce manual labor, its impact on costs should be viewed within the broader context of the added value it brings.”

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What LinkedIn learned leveraging LLMs for its billion users

CIO Business Intelligence

With more than 1 billion users globally, LinkedIn is continuously bumping against the limits of what is technically feasible in the enterprise today. Few companies operate at the scale that LinkedIn does or have access to similar troves of data. We wanted [responses] to be factual but also empathetic. Or at least overly blunt.

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Fire Your Super-Smart Data Consultants with DataOps

DataKitchen

Analytics are prone to frequent data errors and deployment of analytics is slow and laborious. When internal resources fall short, companies outsource data engineering and analytics. There’s no shortage of consultants who will promise to manage the end-to-end lifecycle of data from integration to transformation to visualization. .

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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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.