I truly admire the Brown University Entrepreneurship Program. The university goes out of its way to connect student-run startups with investors and experts through its West Coast Accelerator program thanks to committed alumni. I blogged about their event last year and I had to attend this year to see what's new. I attended the accelerator's Innovation Summit yesterday and the caliber of experts they attracted was impressive.
Brown alum Jonathan Speed led off by thanking everyone for attending. This guy is relentlessly positive and he's always loaded with business tips. Jonathan's team recruited high-powered sponsorship from IDEO, Y Combinator, Singularity University, Salesforce.com Foundation, Presidio Graduate School, and others who offered their support. Our hosts at the de Young Museum let us know about their own Artist Fellows and Artist Studio programs for innovators they want to showcase. The Brown folks introduced this year's lineup of participating startups and remarked on how the good working relationship between Brown and the Rhode Island School of Design (RISD) has spawned ventures. I'm not familiar enough with the startups' business models to evaluate their chances for success, but I was particularly impressed that students were willing to tackle India's "last mile" of water infrastructure and Big Data sorting in real estate investment. Those are big market opportunities but startups will face tons of competition.
MIT's Jose Estabil was a featured speaker on the origins of innovation. He graphed university-driven innovations in a 2x2 matrix I've never seen before, where the quest for understanding competes with considerations for practical use. The ideal combination of both narratives gives us "Pasteur's quadrant." I suspect a similar dynamic plays out at government research laboratories, so I'll have to show that chart to folks at the Federal Laboratory Consortium if I can get myself invited to one of their tech transfer events. Jose made the case that entrepreneurial inclinations aren't as simple as a nature versus nurture debate and that innovation specialties can be taught much like health care disciplines. I like his emphasis on probabilistic thinking, which is teachable but runs against our programming from evolutionary biology. Studies in psychology and the social sciences have shown that most people don't incorporate more recent evidence into their presumptions (as Bayes' Theorem indicates is necessary for more accurate observations) because our programming biases us in favor of behaviors learned early. Fighting our genes' inertia is an entrepreneurial trait, or as Steve Jobs put it while relaunching Apple, we should "think different."
I liked Jose's practical tips for success. First, being crisp with the "grandmother rule" avoids overly long technical explanations and drives the point about how a startup's tech solves real problems. Second, knowing your audience means understanding that not all investors have the same "win" scenarios. Finally, recruiting partners with the "rule of three" means measuring your networking success by obtaining three or more commitments from a follow-up contact. Jose also presented Roy Rodenstein's matrix of startup funding from how2startup and I found it in Roy's "Overview of Startup Fundraising" deck at Slideshare.
Phizzle's Ben Davis spoke on Big Data's relationship with innovation. According to Ben, the holy grail of marketing is multi-service data aggregation leading to multi-channel delivery. The key to understanding how to make that happen will come from sentiment analysis. aka opinion mining. That's a new construct driving machine learning that will push marketing messages at us thanks to all of the likes we've clicked on Facebook and shares we've clicked on Tumbler. I don't know enough about sentiment analysis to apply it, but rest assured that hot marketing careers will attract computer scientists for the next few years. Google shows their developers how to build a sentiment analysis model with the Google Prediction API. IBM's Social Sentiment Index shows how analytics results are customizable. The good news is that we'll all get what we want before we even know we want it. The bad news is that the era of privacy that coincided roughly with the first and second waves of the Industrial Revolution is ending right now. Don't worry, we won't miss it once it's gone.
Intel's Brandon Barnett shared insights related to his work with the Business Innovation Factory and We The Data. I'll give him special props for helping military veterans transition to civilian careers through Intel's hiring programs, according to his Factory bio. My readers know how important that is to me. Brandon reminded us about threats to a startup from outside its ecosystem when a Copernican Revolution changes sociocultural values. These values are in flux today and we see their redefinition of ownership and productivity driving new business opportunities. I consider outsourcing and the sharing economy to be symptoms of these changes. Brandon cited a study by Bain and the WEF on personal data as a new asset class. I'm pretty sure I heard about this concept at one of the telecom conferences I attended last year, and I'm still trying to find a decent accounting rule treatment for assigning a financial valuation to data. I noticed this Harvard Business School blog mentioning Clayton Christensen's revelation that 95% of new product launches fail. This supports Brandon's contention that disruptive startups win when they launch something that doesn't play to existing competition. Check out Michael Raynor's book The Innovator's Manifesto for clues on how disruption engenders tremendous growth. I think entrepreneurs can adapt Alex Osterwalder's business model canvas along the lines of Brandon's ideas to create some game-changing global experiment.
I've got a few game-changing ideas of my own. My longtime readers know I'm optimistic about smart grid technology in energy use. Much of the tech I've read about so far focuses on embedded sensors that transmit machine log data to utilities. It is divorced from human-generated data in social media channels. The first technology to bridge the two worlds, while protecting user privacy, will see the world beating a path to its door. Some startup may have that answer.
I like that more startup accelerators are using emerging templates to reduce the errors entrepreneurs commit. The growing acceptance of lean startup approaches, Customer Development, technology readiness levels, investment readiness levels, and the business model canvas are having the same effect on startups that Moneyball had on the conventional wisdom governing baseball. Read what Steve Blank says about the relevance of a Moneyball approach. Relying on less formal knowledge is so 1990s. The Big Data era is formalizing paths to success and helping us avoid the things that don't work. Moving from data to analysis to decision making should raise the odds of success for high-risk enterprises. I believe Brown's West Coast Accelerator makes that happen.
Brown alum Jonathan Speed led off by thanking everyone for attending. This guy is relentlessly positive and he's always loaded with business tips. Jonathan's team recruited high-powered sponsorship from IDEO, Y Combinator, Singularity University, Salesforce.com Foundation, Presidio Graduate School, and others who offered their support. Our hosts at the de Young Museum let us know about their own Artist Fellows and Artist Studio programs for innovators they want to showcase. The Brown folks introduced this year's lineup of participating startups and remarked on how the good working relationship between Brown and the Rhode Island School of Design (RISD) has spawned ventures. I'm not familiar enough with the startups' business models to evaluate their chances for success, but I was particularly impressed that students were willing to tackle India's "last mile" of water infrastructure and Big Data sorting in real estate investment. Those are big market opportunities but startups will face tons of competition.
MIT's Jose Estabil was a featured speaker on the origins of innovation. He graphed university-driven innovations in a 2x2 matrix I've never seen before, where the quest for understanding competes with considerations for practical use. The ideal combination of both narratives gives us "Pasteur's quadrant." I suspect a similar dynamic plays out at government research laboratories, so I'll have to show that chart to folks at the Federal Laboratory Consortium if I can get myself invited to one of their tech transfer events. Jose made the case that entrepreneurial inclinations aren't as simple as a nature versus nurture debate and that innovation specialties can be taught much like health care disciplines. I like his emphasis on probabilistic thinking, which is teachable but runs against our programming from evolutionary biology. Studies in psychology and the social sciences have shown that most people don't incorporate more recent evidence into their presumptions (as Bayes' Theorem indicates is necessary for more accurate observations) because our programming biases us in favor of behaviors learned early. Fighting our genes' inertia is an entrepreneurial trait, or as Steve Jobs put it while relaunching Apple, we should "think different."
I liked Jose's practical tips for success. First, being crisp with the "grandmother rule" avoids overly long technical explanations and drives the point about how a startup's tech solves real problems. Second, knowing your audience means understanding that not all investors have the same "win" scenarios. Finally, recruiting partners with the "rule of three" means measuring your networking success by obtaining three or more commitments from a follow-up contact. Jose also presented Roy Rodenstein's matrix of startup funding from how2startup and I found it in Roy's "Overview of Startup Fundraising" deck at Slideshare.
Phizzle's Ben Davis spoke on Big Data's relationship with innovation. According to Ben, the holy grail of marketing is multi-service data aggregation leading to multi-channel delivery. The key to understanding how to make that happen will come from sentiment analysis. aka opinion mining. That's a new construct driving machine learning that will push marketing messages at us thanks to all of the likes we've clicked on Facebook and shares we've clicked on Tumbler. I don't know enough about sentiment analysis to apply it, but rest assured that hot marketing careers will attract computer scientists for the next few years. Google shows their developers how to build a sentiment analysis model with the Google Prediction API. IBM's Social Sentiment Index shows how analytics results are customizable. The good news is that we'll all get what we want before we even know we want it. The bad news is that the era of privacy that coincided roughly with the first and second waves of the Industrial Revolution is ending right now. Don't worry, we won't miss it once it's gone.
Intel's Brandon Barnett shared insights related to his work with the Business Innovation Factory and We The Data. I'll give him special props for helping military veterans transition to civilian careers through Intel's hiring programs, according to his Factory bio. My readers know how important that is to me. Brandon reminded us about threats to a startup from outside its ecosystem when a Copernican Revolution changes sociocultural values. These values are in flux today and we see their redefinition of ownership and productivity driving new business opportunities. I consider outsourcing and the sharing economy to be symptoms of these changes. Brandon cited a study by Bain and the WEF on personal data as a new asset class. I'm pretty sure I heard about this concept at one of the telecom conferences I attended last year, and I'm still trying to find a decent accounting rule treatment for assigning a financial valuation to data. I noticed this Harvard Business School blog mentioning Clayton Christensen's revelation that 95% of new product launches fail. This supports Brandon's contention that disruptive startups win when they launch something that doesn't play to existing competition. Check out Michael Raynor's book The Innovator's Manifesto for clues on how disruption engenders tremendous growth. I think entrepreneurs can adapt Alex Osterwalder's business model canvas along the lines of Brandon's ideas to create some game-changing global experiment.
I've got a few game-changing ideas of my own. My longtime readers know I'm optimistic about smart grid technology in energy use. Much of the tech I've read about so far focuses on embedded sensors that transmit machine log data to utilities. It is divorced from human-generated data in social media channels. The first technology to bridge the two worlds, while protecting user privacy, will see the world beating a path to its door. Some startup may have that answer.
I like that more startup accelerators are using emerging templates to reduce the errors entrepreneurs commit. The growing acceptance of lean startup approaches, Customer Development, technology readiness levels, investment readiness levels, and the business model canvas are having the same effect on startups that Moneyball had on the conventional wisdom governing baseball. Read what Steve Blank says about the relevance of a Moneyball approach. Relying on less formal knowledge is so 1990s. The Big Data era is formalizing paths to success and helping us avoid the things that don't work. Moving from data to analysis to decision making should raise the odds of success for high-risk enterprises. I believe Brown's West Coast Accelerator makes that happen.
