Why a Book Club?
Automattic has a creed that informs everything we do. And the very first part of the creed is “I will never stop learning”.
The first and most important line of the creed means that you’re never finished. There is always more to learn. Your list of unread books is always going to be longer than the ones you’ve read.
In the Data division, we’ve taken that advice literally, and created a monthly data book club. It’s lots of fun, so we thought we’d share how we organize ours so that you can do the same.
How-To Guide
Step 1: Choose Some Books
To start off, we looked at some of the recommended reading lists posted on the web. Many data learning programs provide one, and Towards Data Science regularly posts book recommendations. We also subscribe to a few data newsletters that include book recommendations.The one from David Green is excellent.
We put the best books in a spreadsheet and invited anyone in our division to add their recommendations. Our list currently has 39 suggestions in it – probably more than any of us could read in a year!
We let people sign up for the books they like, so we generally choose the books that attract the most interest. But we also mix it up a little: for example, we would avoid reading two books in a row on, say, data visualization.
We do have a few guidelines.
- The books should be available in a digital version. We are a distributed workforce and online deliveries can take weeks to arrive in some parts of the world. We look for books that you can receive and read in a single month.
- Whenever possible, we choose books that offer an audio version. We want to make sure the books are accessible to our colleagues with different learning styles and with disabilities.
- The price of the books should be reasonable. Sometimes we splurge a bit for a particular title, but most of the books cost under $40 USD each.
- The books should be related to data, but here we are also pretty flexible. We have read books about data visualization, of course, but also ones on productivity.
Step 2: Find Readers
Even though our book club is focused on data-related books, it is open to the whole company. This means that we include people doing data-related work both in and outside of the data division, those who are considering switching roles to join the division, and folks who are simply interested in the book or topic. We advertise our meetings in our own team and division Slack channels, but also in company-wide channels. This is a 100% optional activity, so participation varies month-by-month. The biggest factor in determining participation is the book title.
Joining this book club and having discussions among peers… This is the first time I’ve managed to finish reading a book in years.
– Jie Bao
Step 3: Pick A Schedule and Organize Some Discussions
We read one book a month and discuss it only once. We find this usually works better than scheduling multiple discussions for a single title. When the meetings take place depends on who signs up. Even when people have lots to discuss, it can be hard to find times to meet. We’re busy!
Because we are distributed, our meetings are always video conference calls. This lets people join in from all over the world.
As a distributed company, we also document our meetings and post the discussion notes internally. This empowers people to contribute asynchronously. This keeps learning accessible to the whole team. It also means that we have a record of our book reviews. That makes it easier for others to decide if a book is worth reading later on.
Our Reading List
Below are a few of the books we’ve read so far and have found especially interesting.
- Delivering Data Analytics: A Step-By-Step Guide to Driving Adoption of Business Intelligence from Planning to Launch by Nicholas Kelly (2021).
- Search Inside Yourself: The Unexpected Path to Achieving Success, Happiness by Chade-Meng Tan (2014).
- Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic (2015).
- Dialogue: The Art Of Thinking Together by William Isaacs and Peter M. Senge (1999).
- Excellence in People Analytics: How to Use Workforce Data to Create Business Value by David Green and Jonathan Ferrar (2021).
And here are a few titles we hope to tackle soon.
- Bayesian Data Analysis by Andrew Gelman, Aki Vehtari, John Carlin, Hal S. Stern, David Dunson, Donald Rubin (1995).
- Software Engineering at Google by Titus Winters, Tom Manshreck, Hyrum Wright (2020).
- Grokking Algorithms: An illustrated guide for programmers and other curious people by Aditya Y. Bhargava (2015).
For more reading suggestions, check out our list here.
Give it a try! We’ve found our book club to be a rewarding, inclusive way to incorporate team learning into our work lives. We hope you will too!
Data for Breakfast 