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Data Teams A Unified Management Model For Successful Data-Focused Teams at Meripustak

Data Teams A Unified Management Model For Successful Data-Focused Teams by Anderson, Apress India

Books from same Author: Anderson

Books from same Publisher: Apress India

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  • General Information  
    Author(s)Anderson
    PublisherApress India
    ISBN9781484283530
    Pages294
    BindingSoftcover
    LanguageEnglish
    Publish YearJanuary 2022

    Description

    Apress India Data Teams A Unified Management Model For Successful Data-Focused Teams by Anderson

    Learn how to run successful big data projects, how to resource your teams, and how the teams should work with each other to be cost effective. This book introduces the three teams necessary for successful projects, and what each team does. Most organizations fail with big data projects and the failure is almost always blamed on the technologies used. To be successful, organizations need to focus on both technology and management. Making use of data is a team sport. It takes different kinds of people with different skill sets all working together to get things done. In all but the smallest projects, people should be organized into multiple teams to reduce project failure and underperformance. This book focuses on management. A few years ago, there was little to nothing written or talked about on the management of big data projects or teams. Data Teams shows why management failures are at the root of so many project failures and how to proactively prevent such failures with your project. You will: Discover the three teams that you will need to be successful with big data Understand what a data scientist is and what a data science team does Understand what a data engineer is and what a data engineering team does Understand what an operations engineer is and what an operations team does Know how the teams and titles differ and why you need all three teams Recognize the role that the business plays in working with data teams and how the rest of the organization contributes to successful data projects



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