Last week I spent two glorious days deeply engaging with my alma mater, the University of Michigan, and the Ann Arbor tech community. I had the privilege of presenting at the Ann Arbor New Tech Meetup (thanks, Dug!), exchanging ideas with the Wolverine Venture Fund, and spending lots of time with professors and heads of different programs and institutes such as the Zell Lurie Institute, the Center for Enterpreneurship and the School of Information. I also got to see Ann Arbor’s take on the collaborative start-up model, Tech Brewery, which houses some very cool companies. My 48 hours in Ann Arbor were a whirlwind that left me both amazed at the progress the University and the community have made towards fostering entrepreneurship while keenly aware of how much more there is to be done. It also reinforced my already-existant mission of wanting to re-think exactly what a data scientist is and, in its wake, how we help create more of these people towards building a better future.
One thing I noticed at Michigan is how developed and entrepreneurial its Office of Technology Transfer is relative to many of its peers. My sense is that because of Ann Arbor’s physical location (a land-locked jewel of innovation), it has had to be incredibly scrappy and experimental in order to achieve its goals. There simply aren’t the deep network effects that exist in San Francisco/Silicon Valley, New York/Silicon Alley or Boston/Cambridge. And while it is still early in the game, they have done a great job cultivating relationships across the University and working closely with the departments to get technology successfully spun-out from the School (kudos to Wes Huffstutter for greasing the wheels of cross-institutional progress). But the fact that “tech transfer” at Michigan doesn’t conjure up thoughts of the usual hard-to-work-with, inflexible bureaucracy is a tribute to what they’ve accomplished in the past decade. Other schools have much to learn from Michigan’s progress.
I was also impressed with the pockets of entrepreneurship across the schools of Business, Engineering/CS and Information. Yes, these efforts are still way too concentrated within the programs as opposed to truly horizontal across the University, but these efforts give me hope that collaboration-as-necessity will eventually break down these artificial boundaries over time. But the energy an enthusiasm from the program heads, mentors and entrepreneurs themselves is palpable. It is hard not to get caught up in the excitement of what is going on and how much more could be going on, and better, too.
What Ann Arbor currently lacks is a bunch of successful exits where the entrepreneurs re-invest back into the Michigan ecosystem. Firms like Google, Facebook, Twitter and IBM descend upon Ann Arbor to hire the best and brightest, and several tech firms are establishing local presences. However, for the flywheel of entrepreneurship to take hold companies need to be be invented and built in Ann Arbor, with founders coming back and seeding businesses locally after they’ve had an exit. They also need to start new companies as experienced entrepreneurs in order to deepen the management talent that is sorely lacking. A local start-up will get funded but then relocate to Silicon Valley or New York, and this has to be ok. But it is important for people to remember where they came from and got their break. Ann Arbor needs this kind of memory to keep the Michigan diaspora engaged and invested in the University and Ann Arbor ecosystems.
Also, my sense is also that there is not yet a robust software-knowledgable base of angels around town. Life sciences has historically been very strong as well as businesses related to the auto industry, but pure software does not have the same shape as these legacy businesses. Start-ups in general, and software start-ups in particular, can look really ugly. A few coders in a little crappy office or shared work space is what a software start-up almost always looks like. Yes, they might be building a profound product but it doesn’t look like a “real” company to inexperienced eyes. More experienced angel eyes are needed in A2 to help nascent companies move beyond Friends & Family and get to a real seed round.
While I’ll deal with my view of the next generation of data scientists in a subsequent post, I am incredibly interested in helping to build a dedicated program towards this end at Michigan. All the pieces are there. It just requires some cross-departmental cooperation in order to bring it to life. This is one of my missions for my alma mater: help to pull together a data science program that empowers student/practitioners to solve tomorrow’s problems today. It can be done. It must be done. It will be done.