What do you think?
Rate this book
Data Teams: A Unified Management Model for Successful Data-Focused Teams
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.
What You Will Learn
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
Who This Book Is For
Management, at all levels, including those who possess some technical ability and are about to embark on a big data project or have already started a big data project. It will be especially helpful for those who have projects which may be stuck and they do not know why, or who attended a conference or read about big data and are beginning their due diligence on what it will take to put a project in place.
This book is also pertinent for leads or technical architects who are: on a team tasked by the business to figure out what it will take to start a project, in a project that is stuck, or need to determine whether there are non-technical problems affecting their project.
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.
What You Will Learn
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
Who This Book Is For
Management, at all levels, including those who possess some technical ability and are about to embark on a big data project or have already started a big data project. It will be especially helpful for those who have projects which may be stuck and they do not know why, or who attended a conference or read about big data and are beginning their due diligence on what it will take to put a project in place.
This book is also pertinent for leads or technical architects who are: on a team tasked by the business to figure out what it will take to start a project, in a project that is stuck, or need to determine whether there are non-technical problems affecting their project.
346 pages, Kindle Edition
Published September 18, 2020
43 people are currently reading
174 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 9 of 9 reviews
December 12, 2020
This book is aimed at management to understand Data Teams (= Data Science + Data Engineering), along with the differences between small data and big data and their impact on your organisation. But as a practitioner too I got amazing value from this read.
Jesse Anderson brings on first hand industry knowledge, as well as a panel of experts (like Paco Nathan or Ben Lorica) and industry interviews with top companies (like Twitter or Zalando) which makes this book dense with insights and also directly applicable.
Many times, I had the feeling the author was talking about my team. CEOs, VP Engineering, Head of Data, Data Engineers, Data Scientists, Data Analysts: the entire Data organisation is described with precision. This makes it relatable to your own organisation and will help me find the right wording for the right audience going forward.
Data Science: The author's definition of Data Science aligns with Paco Nathan's or Chris Wiggins' from the New York Times. This is the one I have been using too, although the market today forces me to explain at length that a Data Analyst is not a Data Science; or that data science projects are different from software engineering and closer to research etc... I will be able to use this book as a reference in my discussions.
Data Engineering: interestingly the author pleads for a data engineering team being not made of N data engineers only. Another important idea is that organisations that fail to understand the difference between a SQL focus and a software engineering focus will not be successful in their data products.
Parts of the books that didn't directly apply to my team are still really insightful. For example, we don't have a team of Operations Engineers because we practice DevOps and because we have only a few data products in production. Also, one part that I was missing is such a description of the BI team or Data Analysts.
Jesse Anderson brings on first hand industry knowledge, as well as a panel of experts (like Paco Nathan or Ben Lorica) and industry interviews with top companies (like Twitter or Zalando) which makes this book dense with insights and also directly applicable.
Many times, I had the feeling the author was talking about my team. CEOs, VP Engineering, Head of Data, Data Engineers, Data Scientists, Data Analysts: the entire Data organisation is described with precision. This makes it relatable to your own organisation and will help me find the right wording for the right audience going forward.
Data Science: The author's definition of Data Science aligns with Paco Nathan's or Chris Wiggins' from the New York Times. This is the one I have been using too, although the market today forces me to explain at length that a Data Analyst is not a Data Science; or that data science projects are different from software engineering and closer to research etc... I will be able to use this book as a reference in my discussions.
Data Engineering: interestingly the author pleads for a data engineering team being not made of N data engineers only. Another important idea is that organisations that fail to understand the difference between a SQL focus and a software engineering focus will not be successful in their data products.
Parts of the books that didn't directly apply to my team are still really insightful. For example, we don't have a team of Operations Engineers because we practice DevOps and because we have only a few data products in production. Also, one part that I was missing is such a description of the BI team or Data Analysts.
This entire review has been hidden because of spoilers.
October 25, 2021
Overall a decent read. I've been directly involved building data teams at a start-up over the last eight years, and I found myself nodding through most of the book. It is a good high-level overview distinguishing between roles/responsibilities (data engineering vs data science vs data ops), small data vs big data, and common challenges. The end of the book includes a series of interviews with organizational leaders who describe their own journey. There were a few points where I felt like the book was either a bit repetitive or didn't have enough meat, but taken in whole, it does a decent job giving the lay of the land.
August 24, 2025
Not sure who this book is for to be honest. Had high hopes going in to it, but as someone who works in the field it feels mostly like stating obvious things like what a data scientist does and how it differs from what a data engineer does.
May 7, 2021
This book did an excellent job describing the three key data roles (data science, data engineering, and data operations) and how they interact with each other and the business. It also covers common issues that teams run into and their root causes. The book closes with a few excellent case study interviews from different companies (these alone make the book worthwhile). I'd recommend this to anyone working in the data space, but especially to managers and executives who want to understand how to successfully structure and manage their teams.
March 14, 2021
I really liked this position. It's very specialized, contains actionable advice, has a lot of useful insights. This one of the most comprehensive topics on constructing, managing, leading, and diagnosing data teams. It feels that it's written by an experienced practitioner. Definitely recommended for anybody in the field of project management for data analysis, data engineering, and data science.
July 9, 2021
Great organization of ideas and clear, concise points for enabling the best infrastructure around distributed systems. Having data engineers in place that can understand how important the storing / pipeline goes as well as the proper business-aligned teams around them, doing data projects.
End had 4-5 case studies of different companies around aligning business + data teams.
End had 4-5 case studies of different companies around aligning business + data teams.
October 12, 2023
recommend to every data team leader
very comprehensive and provide hands-on experience and insightful advice from an industry guru. the book is especially valuable for those who are in the process of building or revamping their data team, which helps avoiding costly mistakes.
very comprehensive and provide hands-on experience and insightful advice from an industry guru. the book is especially valuable for those who are in the process of building or revamping their data team, which helps avoiding costly mistakes.
January 15, 2023
O livro me pareceu mto focado em quem não tem conhecimento sobre time de dados e não cobre todo os aspectos de uma área de dados.
June 15, 2023
Very useful and practical resource for understanding the optimal ways to structure a data team and bring value to an org.
Displaying 1 - 9 of 9 reviews