3 Business Books For Data People
Seth Godin likes to say books are a bargain. If you get one idea from a book, it already paid for itself. I get most ideas about data science, ML and AI from business books. I don’t really know why, maybe it is the test of time, but for whatever reason I find myself recommending more business books than data books (shy of my own of course, everyone should read that.)
So, here are three that I’ve recommended to people in the last couple of months. All of them will pay for themselves probably after you’ve read the description below ;)
When Coffee and Kale Compete: Become great at making products people will buy - A. Klement
The key idea in this book is this: The alternative to any product is not what you think (like coffee and kale.), However, you need to understand the alternative to build an effective product in the first place.
It is a subtle idea, but the book makes it practical. Now, why do I think it’s so essential to data people that I keep recommending it? Because whether you have an internal data initiative or a data-heavy product, what you’re likely to miss is the alternative! It’s such a common problem that I even wrote about “How To Estimate The Value Of Data Products.”
My thinking is this: If your product lacks a “cancel” button, the alternatives for people to cancel your product are pretty far off. They need to call or mail and sue you. But if you haven’t yet deployed your recommendation engine, people will still buy stuff! People will always make decisions and take action using any kind of crutch.
Given that this is true, that the alternative for data projects is always pretty close but hard to grasp, this book is perfect for letting you wrestle with this.
The book is about the jobs-to-be-done framework and is competing with another book and a person selling a slightly different version of it. I don’t engage in the controversy. I’ve read both, and I can tell you that this one is better for data people.
Moving on to distributing your data product once you’ve built the right one!
Traction: How Any Startup Can Achieve Explosive Customer Growth - G. Weinberg, J. Mares
Gabriel Weinberg is the founder and CEO of DuckDuckGo, a data-based company that saw exponential growth for ten years despite the dominance of Google. With an almost constant 90% market share.
However, Gabriel wrote this book based on his experience as a data leader, and he wrote it to address a general audience. The book Traction offers three things:
One insight: You will fail if you don’t have good distribution. And there is likely one distribution channel that is 10x better than any others.
A straightforward process for finding this one best distribution channel.
A short guide to setting up tests inside the usual 18 channels and optimizing them once you find your good one
Why do I recommend this to data people and leaders? Because most data people and leaders are technical. And technical people tend to assign a lower importance to distribution. But make no mistake, just like DuckDuckGo's success is a function of its distribution, so is yours.
Without distribution, your internal data project will fail. Without great distribution, your startup will tank. If you currently distribute with a wide array of channels, you need this book.
Read this book; it will pay off within the first 40 pages.
Finally, let’s get to the core of many data products: network effects!
Turning the Flywheel - J. Collins
Network effects, platforms, feedback loops. There are dozens of concepts that describe why companies like Amazon, Netflix, Google, and Airbnb are successful. And all of them are about data and algorithms.
But to my knowledge, there is only one concept that has the scars to prove it’s value: that’s the flywheel. Jim Collins had the luck to consult for Amazon in the early days, and since that time, the Amazon flywheel has been a core mechanism enforcing the leadership principles inside Amazon.
We can, without a doubt, say that the flywheel concept has worked at least once, and it has been fundamental to Amazon’s success. We simply can’t say that of any other concept.
That’s a shame because the idea of the flywheel, a reinforcing mechanism that makes a product/strategy more potent as it continues on, is at the center of almost all data-heavy products.
A flywheel is a linked chain of steps that feed into each other such that the next one has to follow from the first. For Google, this looks like this:
more search queries run through Google
more data is captured by Google
the algorithms at Google serve better results
more people use Google/ people use Google more
… back to step 1….
If you look closely at this series, each step almost automatically follows the next one. That means it will build momentum no matter where Google focuses its efforts.
Since this dynamic is at the core of almost all data powerhouses, this book is an essential read to me. Jim lays out how to find your own flywheel as a company or boil it down for an individual product.
Now, if your shopping basket isn’t full yet, I got a few bonuses!
Three Bonus Recommendations
While the books above would be for everyone in the data space, there are some I only recommend in particular situations to particular readers:
Good Strategy Bad Strategy - for data leaders! For the ones with the appetite to digest and do the hard work. Frankly, those have been the most I’ve met; I just didn’t want to resurface the same book I had in my last article on books.
Working Backwards is interesting in many ways, but I find one very crucial: Chapter 6 on metrics and the DMAIC framework. It’s all about how you need to first do the hard work to understand what influences a process before you can do anything to work data-driven. Almost everyone forgets that step!
Information Rules - is foundational to everything that has to do with data. But it’s also almost as old as Google, and written from a slightly academic perspective. If you have the guts to dig through it, congrats, you will learn a ton! FWIW If you have questions about that book, feel free to reach out to me.