Innovation Pathways: Tapping Into the Power of Data Analytics

To help illustrate where the pursuit of innovation management can lead, our Innovation Pathways series asks our current students, recent graduates, and established alumni to describe their journeys.

Matt Gates headshotMatt Gates WG’21 spent the summer at Ford working on autonomous vehicle go-to-market strategy. Prior to Wharton, he worked as a systems engineer at Lockheed Martin on various Naval systems for submarines and surface combatants. He also spent time performing data analytics for the Navy at a boutique consulting firm, Technomics. He has a bachelor’s of science in aerospace engineering from the University of Notre Dame and a master’s of science in aeronautics and astronautics from Purdue University. At Wharton he is co-president of the Tech Club and a Venture Fellow for McNulty Leadership Ventures.

What have you learned about managing innovation in your career?
I spent a few years working at a boutique consulting firm specializing in data analytics projects for the Navy. My team and I were working to estimate the program cost of the next generation of submarines, and we developed an innovative methodology for estimating indirect costs that indicated a significant decrease from the historical method. I was convinced this news would be well received because of the potential budget savings, but we met with serious resistance. Since this was an innovative approach, people were hesitant to adopt it. In order to gain adoption, we had to explain not just the data and assumptions involved in building the model, but also the story of why a new methodology was needed and what it would mean moving forward. And we had to have this conversation not once, but over a half dozen times. Ultimately it was a great lesson on the potentially uphill battle of gaining adoption for innovative solutions. I realized the importance and necessity of telling a complete and compelling story to all relevant stakeholders because change can be hard and, unless you really work at it, the status quo will triumph.

How did the Mack Institute’s Collaborative Innovation Program (CIP) offer you an opportunity to explore innovation in a new way?
My CIP project involved optimizing sales for a German OEM. We were given a dataset and an end goal and then left to our own devices on how we wanted to approach it. In my previous consulting work, there was always a broad framework or model that could be leveraged and extrapolated from, so I knew what data I needed to collect and how to proceed. With our CIP project, we didn’t have all the data we needed or a framework to start with. We didn’t even know we didn’t have all the data we needed. It wasn’t until we developed a hypothesis and started to build a model that we were able to realize what we did and didn’t have. For me, it was this aspect that drove my personal growth and learning. We were building a model from the ground up, with no reference structure or template to fall back on. There was no certainty that our output would be accurate or even useful. We just had to hypothesize, build, test, evaluate, then repeat. This was an awesome challenge and I recommend everyone take advantage of the program.

It also reaffirmed for me that data analysis has the potential to be incredibly powerful in nearly every decision a company makes, from understanding the customer to optimizing a company strategy for growth.

What did you find most rewarding about working with your CIP team?
The differing perspectives we were all bringing to the table. Since the scope was not well defined at the outset of the project, we sought clarity from our client. I went in to the first meeting with a list of questions I thought we needed to know to move forward. However, during the meeting, each of us asked completely different clarifying questions based on where we personally sought answers. My questions centered around data, for instance, while another of my teammates was concerned about the most valuable performance metrics for the client. The combined responses gave us a much more holistic understanding of what problem the client was trying to solve.

Once we began developing a model, our diversity of backgrounds and skill sets provided both opportunities and safety checks. For instance, when I could not explain the justification for why I was bucketing the data a certain way to my team, I took a step back and reconsidered if it was the right approach. This task conflict drove us to a better solution, one that could deliver real value to our client. I look back on the work we delivered and know categorically that I would not and could not have delivered as effective of a model on my own.

How did CIP impact your career goals?
CIP provided me the opportunity to work directly with a company in the automotive sector, providing crucial insight into the way that company functioned and what factors the broader industry prioritized. This knowledge was helpful in ramping up more quickly during my internship and could potentially aid my career after graduation. It also reaffirmed for me that data analysis has the potential to be incredibly powerful in nearly every decision a company makes, from understanding the customer to optimizing a company strategy for growth.

What advice do you have for current students interested in pursuing careers that involve innovation management?
Embrace the ambiguity of the situation. The very definition of innovation management involves something new. So, while you cannot perfectly prepare for how to handle a specific instance before it happens, acknowledging the unknown allows you to shift your mindset into problem-solving mode. From here, you are still able to push for your original plan but are quick to adapt to the changing circumstances simply because you expected the unexpected.

Embracing ambiguity is easier said than done, however, and is something I’ve made a concerted effort to improve over the years. So, keep pushing yourself into growth experiences and challenging your own status quo, just as you hope to do with innovative solutions.