
During my first years of working in the area of Data Analytics, I read many books that I found very helpful in making me a better Data Analyst. Among them, 4 books stand out, which I would recommend to anyone who wants to enter this area or continue to develop.
All the books that are named here are non-tech. None of them talk about programming skills (SQL, R, Python, etc) or statistics – there are already many reading lists that I would highly recommend as this one! In my post, I would like to focus on the books that helped me improve my problem-solving skills, structured thinking & communication, business sense, and stakeholder management, which are very valuable qualities of a great Data Analyst as I summarised in a previous post.
These 4 books are:
❓ Start with Why, which trains you to start by asking good questions and become more decision-driven.
🪜 The Pyramid Principle, which teaches you how to define and solve problems in a structured manner and how to present your insights in an effective way.
📈 Lean Analytics, which presents the metric systems under six business models as well as the benchmarks, making it a perfect material for preparing analyst interviews.
🛒 Freakonomics, which helps those without a business/economics background to formulate better hypotheses and validate them.
Below you could find my personal takeaways from these books. Let’s get started!
Start with Why: How Great Leaders Inspire Everyone to Take Action

In this book Simon Sinek shared many examples of why some organizations succeed and why others don’t (my favorite one is Southwest Airlines) and eventually present the theory of the Golden Circle. The main idea is that, if you are able to effectively communicate why you do what you do, rather than what you are doing and how you do it, is a very impactful way to communicate with other humans and inspire them to follow you.
From the very first pages, the book may seem counter-intuitive to any data person since, in the very first chapter, the author states that more data or information does not necessarily lead to better decisions, especially when people start with false assumptions. However, when I look back to my path as a Data Analyst, I realize that this message actually makes perfect sense.
As I shared in my previous post, I believe that as a data person, I create value for my team not by executing exactly the wishes of my colleagues but by helping them achieve their ultimate goals. This means that sometimes I need to stop gathering data to answer those ill-formatted questions or hypotheses, and instead help people to ask good questions – questions that are truly relevant to the ultimate goals. In the end, "starting with goals" is an incredibly valuable skill that marks the transition from a Junior to a Senior Data Analyst.
This learning echoes with a profound post I recently read, where the author points out the differences between decision-driven analytics and data-driven analytics and argues for being more decision-driven, that is, to start with questions/hypotheses, not data; to search first wide instead of deep; and to be more readily challenged than perpetuated.
Overall, I believe that this book brings unique value to anyone who is or wants to be a Data Analyst, in a way that it helps you understand why good questions matter more than good answers and eventually prepares you to be more decision-driven than data-driven.
The Pyramid Principle: Logic in Writing and Thinking

Those who have read my previous posts can already tell how much I love this book! As a true advocate, I could not recommend this book more. In fact, I have even hosted a Browbag session with my team to share my learnings. If you are interested, have a look here!
There are two major things from this book that I found particularly helpful in my career as a data person. First of all, there are many tips that help build structured thinking. The most well-known MECE principle, i.e., Mutually Exclusive, Collectively Exhaustive, is a very powerful framework for any Data Analyst to dissect the problem and formulate hypotheses for second-order questions like "Why is there a drop in sales revenue?" or "Why do we see an increase in churn rate?". In addition, the Sequential Analysis and the SCQ framework help one to quickly define and scope problems, which is the key to any successful problem-solving!
Second, the book also shares many best practices for presenting and communicating insights, which is a daily job for every Data Analyst. For example, always put your conclusion first in any presentation to help your audiences to get the key idea, usually a "what", at the first sight, and let them decide on their own whether or when they want to know more details behind the "what". Another trick is to always limit your supporting arguments to a maximum of three (i.e. the Rule of Three), which helps your arguments get attention and become memorable. If you would like to know more about how I applied what I’ve learned from this book in my daily presentation of insights, feel free to check this post!
Lean Analytics: Use Data to Build a Better Startup Faster (Lean Series)

This book is a classic in the world of startup and product analytics so I would not expand too much here. 🙂
Personally, what I found most valuable from the book are the criteria of good KPIs and the concept of The One Metric That Matters. I wrote a post on how we defined and measured the OMTM in our case – check out here!
Another thing I want to highlight is that the book provides unique value to a Data Analyst, especially those who are in their early career stage. Imagine that you have just spent 1–2 years at a company and are looking for new opportunities, especially those from a different business model, the metric systems under six business models (i.e., e-commerce, SaaS, free app, media, UGC, marketplace) as well as the benchmarks for these metrics are best sources to prepare for interviews and challenge tests when he/she does not have practical work experience in a different area.
Freakonomics: A Rogue Economist Explores the Hidden Side of Everything

Technically speaking, I just started to read this book recently and haven’t finished it yet. I’ve heard a lot about this book before, but I did not really read it since I have received formal training in the area of economics and econometrics during my bachelor’s study. That said, I realized that it was actually a pity that I did not read this book before! It gives tons of real-life examples and explains the mechanism behind those phenomena in an easily digestible manner. It is a great introductory book for anyone who has not received training in economics.
What unique value does it bring to one who has a passion for data analytics? Well, very often Data Analysts are able to find correlations or patterns in a huge amount of data using different techniques such as scatterplots, regression, statistical tests, etc. What is missing sometimes, as I personally observed from our hiring process, is the ability to propose the second-order hypotheses behind the observed patterns. For instance, when an analyst spots from a bar plot that users with higher income generate more revenue for our business, we also expect him/her to propose hypotheses that could explain this pattern, e.g., people with higher income have more valuable properties so they need more insurances. The ability to incessantly create good hypotheses and try to validate them is a highly valued trait of a good Data Analyst.
This book provides many practical examples in this exact case! For instance, it shares a study that investigates the relationship between many factors and students’ academic performance. In addition to presenting the results from the regression model, the author also tries to analyze why some factors are relevant (i.e., the number of books in a family is positively correlated with kids’ performance) and some are not (i.e., reading books to the kids is not correlated) – the former one is indicative of parents’ education while the latter is simply a behavior that can be easily copied.
Therefore, I would highly recommend this book to anyone who aspires to start a career as a Data Analyst without a business or economics background!
What books would you recommend to a (to-be) Data Analyst?