We recently caught up with Michael Watson, Partner at Opex Analytics and Adjunct Professor at Northwestern University, where he teaches Analytics in two of their masters programs. We were keen to learn more about his background and his various current roles and projects...
Hi Michael, firstly thank you for the interview. Let's start with your background and how you became interested in working with data...
Q - What is your 30 second bio?
A - I am a partner with a new company called Opex Analytics. We are helping bring new data science techniques and products to operational companies (retailers, manufacturers, etc). I’m also an adjunct professor at Northwestern University, teaching in two of their masters programs. Previously I was at IBM.
Q - How did you get interested in data / analytics?
A - I have always been interested. I used to track statistics of our sandlot baseball and basketball games as kid — probably much to the annoyance of the other kids. And, this carried with me through schooling — I loved my math classes and I loved the idea that mathematical models could help you understand the real world.
Q - What was the first data set you remember working with? What did you do with it?
A - Back in 1989 (or so) when I was a sophomore in college, I got our school to enter in the Mathematical Contest in Modeling. This gave us access to a realistic data set that we had to analyze and come up with a solution over a weekend. I can’t remember the tools we used to analyze the data, but we calculated basic statistics and built simple models to analyze the problem. This was a great way to see that data could have an impact on real problems.
Q - Was there a specific "aha" moment when you realized the power of data ?
A - In the summer of 1991, I was north of Cairns, Australia, in a small camp in the middle of the rainforest. After dinner, I had the most interesting conversation with someone who was convinced that mathematical modeling was the way to solve complex and real business problems. It was during this conversation that I decided to take the career path that I did.
Very interesting background - thanks for sharing! Let's talk in more detail about the work you're doing now at Opex Analytics...
Q - What attracted you to the intersection of advanced analytics and operations?
A - At IBM (and ILOG and LogicTools through the successive acquisitions), I was focused on helping companies apply optimization to make better supply chain decisions. So, I knew the challenges faced by operational companies. I wanted to be in a position to bring the new data science ideas to these firms. Combining techniques like optimization and data science can be a powerful combination.
Q - What has been the most interesting project you have worked on? Why?
A - The most interesting project I worked on was a merger of two large manufacturing companies. We had to help them figure out which facilities to keep, which ones to remove, and how to structure their supply chain. It was interesting because the savings were huge (more than $100 million) and the project received the attention of the CEO. And, the project was mentioned several times in the CEO’s quarterly earnings announcements.
Q - That's great! And how did advanced analytics help?
A - There is no way to solve these types of problems without advanced analytics. You have an enormous amount of data to sort through, and you need to build models that help you analyze the alternatives.
Q - Where do you think advanced analytics can create most value going forward? (i.e., what industries, what types of problems etc.)
A - I think that a lot of web companies have been taking advantage of analytics for a long time. There is a lot of potential to apply some of these ideas to traditional manufacturing companies, traditional retailers, or traditional service providers. These firms have a lot of data that they are not leveraging.
Makes sense! Let's switch gears and talk in more detail about your teaching - you've been an Adjunct Professor at Northwestern for a long time (since 1999) ...
Q - What are you teaching currently?
A - I’ve recently started teaching in Northwestern’s new Masters in Analytics program (a great program for readers of this newsletter to check out) and have started teaching "Managerial Analtyics" in Northwestern’s Masters in Engineering Management (a program for engineers who are working full time and want a more technical business masters degree).
Students are hearing a lot about analytics, but aren’t quite sure what it is. It is exciting to be able to teach a new and fast-moving field to students who are eager to learn more about it. And, I get to learn a lot by teaching analytics.
Q - How has the subject matter changed since you first started teaching? What has driven the evolution?
A - Back in 1999 when I started teaching, no one was talking about analytics. And, my course was focused on operational excellence — how to run a better factory, a better supply chain, or a better services operation. Now, with the rise of analytics, managers realize the importance of data in many different parts of the business. So, there is a desire to learn the basics of data science so managers can apply it to whatever area they happen to be working in.
Let's switch gears and talk about your new book - Managerial Analytics: An Applied Guide to Principles, Methods, Tools, and Best Practices - which has been receiving terrific reviews...
Q - What does the book cover?
A - The first thing the book does is to help people understand what the field of analytics and Big Data is all about—what do these terms mean, what do they include. When we first started researching analytics (and Big Data), we found that a lot of people were using the terms, but not defining them. And, based on how people were using the terms, they were not talking about the same thing. Second, we discuss what it means to have an analytics mindset—how do you need to think about data. And, then we devote the third section of the book talking about the science behind analytics. The science section is meant to show the manager that these techniques are accessible and show how they might be used.
Q - Who is the book best suited for? What can readers hope to learn?
A - The book is best suited for managers who need to understand analytics or for people who are just getting into the field of analytics. The book paints a broad picture of the field and helps people understand how the different pieces fit together and what the different terms mean.
Q - What was your favorite part of writing the book?
A - Since the field is moving so fast, a lot of people are coming out with important ideas or new ways to look at things. My favorite part was learning about new developments and incorporating these into our book. Readers have told me that they have found our bibliography and references in the endnotes very helpful.
That's great - congratulations! Finally, let's talk a little about the future and share some advice...
Q - What excites you most about recent developments and the future of Advanced Analytics?
A - I like the fact that the technology of advanced analytics continues to push into new areas. But, just as important, it is great to see that companies are starting to embrace the idea that they should be taking advantage of the data they have. This combination should allow us to see many new developments that have a big impact in the marketplace.
Q - Any words of wisdom for Advanced Analytics students or practitioners starting out?
A - For analytics students, make sure you also understand business — to make analytics stick in a company or organization, you need more than just the technology. And, for companies and organizations just starting out, my suggestion is to start with a small team, pick a few projects and grow from there.
Michael - Thank you so much for your time! Really enjoyed learning more about your background and your various current roles and projects! Opex Analytics can be found online at http://OpexAnalytics.com and Michael on his faculty page.
Readers, thanks for joining us!
P.S.If you enjoyed this interview and want to learn more about
- what it takes to become a data scientist
- what skills do I need
- what type of work is currently being done in the field