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My Amazon Book Review of Building Analytics Teams

Introduction

I’d like to make a strong point in this review of John Thompson’s book, Building Analytics Teams. The best way I know to do that is with a story. This story isn’t exactly true though. However, in an allegorical way, it is VERY true!

The Story

You’re new in town. You’ve just started a new data science job. You don’t have many friends yet, so you’re excited to go to a data science meetup. The presentation was actually pretty good. But you were there to get to know the community. Thus, you go to the after “meetup” at the pub nearby.

You just got your beer and sat down among the ranks of DS geeks. A new guy comes up to you and asks to sit with you. Wait. Everyone is new to you. But this guy ain’t new. He has an air about him. He’s got that I’ve lived through industry heaven and hell look about him. You know that look. He actually looks like he came through all of that heaven and hell career experience roughly on top. From the stories your dad and uncles have shared with you about their decades in industry, you know that doesn’t always happen.

Before you’ve even realized it, you’re telling this guy everything about yourself. Your childhood history. Why you were inspired to become a data scientist. Where you went for your BS. Where you went for your MS. What your goals are. Somewhere in there you’ve told him that you want to eventually be the Chief Analytics Officer (CAO) of a great company. You are into a serious monologue about yourself, and he’s been encouraging it the whole time.

Then you hear your dad in your head. “Now you get to know this guy”. Suddenly you sort of wakeup. You find your manners and start asking about him. Before he starts, he gives you some final words of encouragement about your CAO goal.

Then he starts answering your questions about him. Whoa, this guy never had it easy. He’s not sugar coating anything. Oh crap, you think, “Was I as authentic and as real as this guy is being now”? You seriously doubt it now. But you can’t think about that for long. 

You become thoroughly enthralled in his industry war stories. This guy has been in predictive data based analytics for decades! Maybe you’re talking to the first Admiral Nimitz of data analytics history. A Batman explicative almost passes over your lips. Close save – it didn’t come out. But you were about to say, “Holy logical dilemma Batman”. This guy has dealt with a broad range of @#$% fighting to bring the value of data science type analytics to … wait, you’ve already lost count! You think he’s covered at least 5 industries, but you get the feeling that it’s far more than 5!

You can’t contain your curiosity, so you interrupt as politely as possible. “How many industries have you applied data analytics in”? He smiles. “My wife asked me to add them up recently, and it was at least 20”. You barely save yourself from another Batman explicative. Maybe those explicatives keep trying to escape, because you kinda want to be this guy’s sidekick! 

You start asking him how he handled some of those ridiculous situations he encountered. In spite of their ridiculous nature, he still sought to understand why. Why others don’t think like us about data and seeking to make predictions with it. Miraculously, he found ways to talk to them and get production level data analytics into place. It clearly hadn’t been easy. But he had accomplished this multiple times in multiple businesses and industries.

You realize too that this guy wasn’t making himself out to be some arch type. This guy was real. He was airing his own past issues and shortcomings. He had learned some things the hard way … even the embarrassing way at times. It was inspiring to hear how he’d figured out so many things in learning to deal with people at all levels. He just remained humble and tenacious and open to how he could get production level analytics into place to benefit businesses. You feel a bit guilty about your own attitudes towards linear thinkers as he called them.

He helped you see why domain experts, functional leaders, and executives were good at their jobs. He helped you see how people like you and him were different. He also helped you see how you’d have to “bring them along carefully” to see the value that we bring to the bottom lines of businesses. Wow. If you hadn’t heard this, you could have seriously stepped in it … for decades! Holy crap Batman, you might still step in it until you learn to work with others as well as this guy has!

You talk for hours. The other data geeks left a long time ago. You both keep talking. You’re surprised when the pub staff comes over and politely lets you both know that they have to close. You settle up your bills and walk outside. You both stand and talk for a bit and then just grab a street bench and kept talking. He covered how he’d learned to actually build up analytics teams in different ways. He explained how he’d learned to prep functional leaders for change. The tools that his team built for others’ teams would require changes. He had to learn how to get buy-in from the leaders of those teams ahead of time. It had taken him some time to learn this stuff, but he’d obviously developed a recipe that maximized the odds of success.

He looks at you and apologizes. “Sorry, you’ve got to work tomorrow”. You tell him not to worry, and then say, “I’m no stranger to staying up too late occasionally. After all, I am a data scientist”. He replies, “I resemble that remark”.

As briefly and concisely as possible, he then ends on a very serious and solemn note. He encourages you to contribute to our arts in every place and area that you possibly can over the course of your career. It is at that point that you realize there is a whole extra dimension to this guy’s devotion to our field. He clearly feels very strongly about the benefits that data analytics can (No – WILL) bring to mankind.

He’d already shown me that people wouldn’t always see the value. They might not understand the benefits. They might very well feel threatened by the improvements that our arts bring. Why, because they could only see the changes that the new tools would bring to their teams and not necessarily the benefits. You begin to see that your duty as a data scientist must take on this dimension of consideration. You must be a patient and a compassionate educator to all about the value of data science in general. You’ll need to do this at many levels in your company and in society. If you and other data scientists do this, it can accelerate the application of our data science arts and speed up the benefits to mankind.

You both stand up and shake hands. The end for this night had clearly arrived. Then you both hug. Whoa, you’re not sure how that happened. Was that OK? That’s something you often did with your dad and uncles, who are true mentors to you. You had long talks with them about your upcoming career. They’d clearly wanted to prep you for the hard road ahead. No career was easy and there’d be many hurdles and walls at times. They knew that they couldn’t prepare you completely. The world was changing fast. Your chosen area is exploding the fastest in all that change. They knew this and said as much. You’d always hug each other at the end of those long night time chats. This guy was filling a hole for you that your dad and uncles clearly couldn’t fill. They simply didn’t have the background. Data science / analytics was truly unique! So after shaking hands, you both simply and instinctually hugged like he was a dad or an uncle. This guy, John is his name, was actually living what he was preaching. For at least 5 hours, he mentored you, because he cared about the future application of data science arts and the people that would deliver those arts to benefit mankind. 

You wake up groggy the next morning. The shower helped a lot. The bulletproof coffee helped more. You have a full day ahead. Your coding flies by. Holy Elegance Batman, you love python! You kinda want to learn R, but dang! You love python!

You’re on a high all day. You get home and finally have time to reflect on last night. Then it dawns on you. Holy Idiocracy Batman (“Why do I say those”? You think to yourself – “Oh right, your dad and uncles’ weird humor has corrupted you”), how can you possibly remember all the good mentoring from last night? 

A Pseudo Return To The Real World

This is Thom speaking now. As I read John’s book, Building Analytics Teams, I felt like he’d spied on my own career. Then I began to appreciate how hard he’d worked to “figure out how to get through the barriers” to implementing data analytics to the benefit of many industries. I realized, this stuff doesn’t come easy to anyone. Thank GOD John took the time to write out his hard earned lessons carefully in a book that I am certain will be the most valuable book to my career as a data scientist. Like him, I too want to see mankind benefit greatly from data science / analytics in every corner of every area possible. 

Let’s continue our allegorical story now. I happen to know this new data scientist. I hope you’ve guessed who it is. It’s Ta! (See the first mentorship post please – “Your Personal Data Science Learning Plan”). As I’ve been honored to be asked to mentor many new data scientists, I created a fictitious data scientist named Ta. Ta is a passionate data scientist that I hope all those I mentor (myself included) will seek to emulate.

I’ve been seeking to encourage Ta. We caught up this same night that Ta had uttered his latest Batman explicative. I kind of enjoy Ta’s Batman explicatives. Like Ta’s dad and uncles, I grew up watching the original series. Heck, I even rode my self declared Bat Bike around our neighborhood as a kid. But I digress. How was Ta to remember the wealth of information Ta was given last night? I listened carefully to Ta, and asked, “You said this guy was named John”? Ta replied, “Yes”.

Hmm. We were chatting on instant messaging. I sent Ta a link. “Do you see the picture on that book cover that I just sent you”? Ta, replied, “Holy Saint Thomas Batman! That’s him!” I explained to Ta that this book was actually better than having perfect notes from his talk with John last night.

Hey, it’s great that Ta could spend time with John like that. But the book! The book is John’s carefully distilled account of lessons from his whole career. In it he shows the hard and sometimes embarrassing road he had to walk. He shows how he learned to effectively build and lead data analytics teams. And he learned to do this to the benefit of businesses in multiple industries. When you go into uncharted territory, the wisest move is to hire a guide. I haven’t looked hard, but I don’t think there’s another book quite like this one that deals with the unusual challenges of building and leading analytic teams to become effective change agents in their businesses. Our challenges in these regards are truly unique.

I continued, “Ta, the value to price ratio of this book makes it an unquestionable purchase. You and I both know that there are many good books on the math and algorithms for our arts. This is the one book that’s unique. You’re not likely to hit too many barriers on algorithms and ETL needs for data. But trust me, the things that John is trying to help all of us with in his book are the really hard things. Those things are the tallest hurdles! Even if you thought you’d never want to be a data science leader, this book is a must so that you will know how to help your chain of command”! 

Ta completely agreed. He ordered an eBook version before our instant message chat ended. I assured him that we could talk about many materials that are nice to have. However, I felt that this is the one book that all data scientists should have in their suite of materials”! Ta got what I was saying. Ta is really serious about becoming a great CAO someday.

I’m a bit envious of Ta. You should see how Ta’s learning plan keeps growing and improving each month. It was really bad at first. Ooops! Don’t tell Ta that I said that. Actually, I guess I told Ta that Ta’s original learning plan version would be bad. “Ta, you just need to start one and commit to growing and revising it. You need to steadily add good materials to your suite of resources. Your plan and your collection will get better over time”. Ta’s clearly grown from that first plan. Ta’s plan is REALLY good now, and it’s good, because it is living and growing! And Ta has now added the first must have book to Ta’s suite of data science materials. John’s book has disciplines that we all should seek to master.

Closing

I need to stop writing this story now and go study some data science! I’m behind. Aren’t we all. So many cool things to learn and master. So little time! Therefore, until next time …