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Data teamsa unified management model...

Anderson, Jesse.

Data teamsa unified management model for successful data-focused teams /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data teamsby Jesse Anderson.
其他題名:	a unified management model for successful data-focused teams /
作者:	Anderson, Jesse.
出版者:	Berkeley, CA :Apress :2020.
面頁冊數:	xxiv, 294 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Big data.
電子資源:	https://doi.org/10.1007/978-1-4842-6228-3
ISBN:	9781484262283$q(electronic bk.)

Data teamsa unified management model for successful data-focused teams /
Anderson, Jesse.

Data teamsa unified management model for successful data-focused teams /[electronic resource] :by Jesse Anderson. - Berkeley, CA :Apress :2020. - xxiv, 294 p. :ill., digital ;24 cm.

Part 1: Introducing Data Teams -- Chapter 1: Data Teams -- Chapter 2: The Good, the Bad, and the Ugly Data Teams -- Part 2: Building Your Data Team -- Chapter 3: The Data Science Team -- Chapter 4: The Data Engineering Team -- Chapter 5: The Operations Team -- Chapter 6: Specialized Staff -- Part 3: Working Together and Managing the Data Teams -- Chapter 7: Working as a Data Team -- Chapter 8: How the Business Interacts with Data Teams -- Chapter 9: Managing Big Data Projects -- Chapter 10: Starting a Team -- Chapter 11: The Steps for Successful Big Data Projects -- Chapter 12: Organizational Changes -- Chapter 13: Diagnosing and Fixing Problems -- Part 4: Case Studies and Interviews -- Chapter 14: Interview with Eric Colson and Brad Klingenberg, Stitch Fix -- Chapter 15: Interview with Dmitriy Ryaboy, Twitter, Cloudera, Zymergen -- Chapter 16: Interview with Bas Geerdink, ING, Rabobank -- Chapter 17: Interview with Harvinder Atwal, Moneysupermarket -- Chapter 18: Interview with a Large British Telecommunications Company -- Chapter 19: Interview with Mikio Braun, Zalando.

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.

ISBN: 9781484262283$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-6228-3doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

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505 0 $a Part 1: Introducing Data Teams -- Chapter 1: Data Teams -- Chapter 2: The Good, the Bad, and the Ugly Data Teams -- Part 2: Building Your Data Team -- Chapter 3: The Data Science Team -- Chapter 4: The Data Engineering Team -- Chapter 5: The Operations Team -- Chapter 6: Specialized Staff -- Part 3: Working Together and Managing the Data Teams -- Chapter 7: Working as a Data Team -- Chapter 8: How the Business Interacts with Data Teams -- Chapter 9: Managing Big Data Projects -- Chapter 10: Starting a Team -- Chapter 11: The Steps for Successful Big Data Projects -- Chapter 12: Organizational Changes -- Chapter 13: Diagnosing and Fixing Problems -- Part 4: Case Studies and Interviews -- Chapter 14: Interview with Eric Colson and Brad Klingenberg, Stitch Fix -- Chapter 15: Interview with Dmitriy Ryaboy, Twitter, Cloudera, Zymergen -- Chapter 16: Interview with Bas Geerdink, ING, Rabobank -- Chapter 17: Interview with Harvinder Atwal, Moneysupermarket -- Chapter 18: Interview with a Large British Telecommunications Company -- Chapter 19: Interview with Mikio Braun, Zalando.
520 $a 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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