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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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A summary of Gartner’s recent DataOps-driven data engineering best practices article

DataKitchen

On 24 January 2023, Gartner released the article “ 5 Ways to Enhance Your Data Engineering Practices.” Data team morale is consistent with DataKitchen’s own research. We surveyed 600 data engineers , including 100 managers, to understand how they are faring and feeling about the work that they are doing.

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5 surefire ways to derail a digital transformation (without knowing it)

CIO Business Intelligence

Worse is when prioritized initiatives don’t have a documented shared vision, including a definition of the customer, targeted value propositions, and achievable success criteria. The risk of derailments increases as I hear inconsistent answers or too many conflicting priorities.

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3 key digital transformation priorities for 2024

CIO Business Intelligence

After all, every department is pressured to drive efficiencies and is clamoring for automation, data capabilities, and improvements in employee experiences, some of which could be addressed with generative AI. As every CIO can attest, the aggregate demand for IT and data capabilities is straining their IT leadership teams.

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Sport analytics leverage AI and ML to improve the game

CIO Business Intelligence

Nearly 10 years ago, Bill James, a pioneer in sports analytics methodology, said if there’s one thing he wished more people understood about sabermetrics, pertaining to baseball, it’s that the data is not the point. The point is to use the data like a razor to cut through false convictions to find the truth.

Analytics 103
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Third, any commitment to a disruptive technology (including data-intensive and AI implementations) must start with a business strategy. 3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? So what? (2)

Strategy 289
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Data Mesh 101: How Data Mesh Can Be Used in an Organization

Ontotext

Part one of this three-part series discussed the concept of data mesh and explored what it is and why an organization should care. Here, part two provides best practices for data mesh, including practical guidance, challenges, and limitations. Enterprises should identify and adopt specific data mesh elements to achieve velocity.