A 2013 Innovation Reading List (ish)

Whether it’s by junior talent looking for their start or by the latest victim to whom I’m dropping Clayton Christensen-isms, I’m often asked what books to read to “get better at innovation.” It’s a fair question with an unfair answer (“everything”). Still, there are three books I’ve read this year that I’ve found very relevant, so I thought I’d share them along with some cliff notes of my takeaways. Although they aren’t explicitly about innovation, they offer insights about how and when people, companies and governments tackle “the introduction of the relevant new.”

 

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1. The Signal & The Noise by Nate Silver (2012)
There’s a reason this book is on the bookshelf of nearly everyone in the technology and innovation field. It offers a great mix of data and human insights and is delivered in a fantastically practical way. A few of the key takeaways:

Don’t confuse uncertainty with risk. Attempting to quantify uncertainty sounds like a predictor of risk. The problem is, uncertainty is nearly impossible to quantify. And demanding the impossible rarely turns out the way it did for Steve Jobs. For most people, uncertainty becomes a haphazard proxy that leads to a false sense of security. Just ask anyone who opened an innovation lab, only to shut it down 18 months later, when they realized they didn’t truly understand the risks.

Foxes make better forecasters than hedgehogs. This is a model Silver uses to describe archetypes of people. Those with a “fox” mentality don’t buy into the lure of “the big idea.” Given how many creative organizations glorify big ideas, it was great to get the perspective of an established forecaster on all the ways this mentality could lead to trouble.

Creating models for insights is not like forecasting the future. Silver uses an excellent example of modeling infectious diseases whereby predicting outbreaks is more difficult than seeing them and reacting to them, and thus, often incorrect. The mistake confuses success (e.g., fewer people got sick due to precautionary measures) with a failed prediction (e.g., not that many people got sick, so the model overemphasized the risk). This is a very common occurrence at big companies that let their success lead to cutbacks in innovation investments, rather than increases.

 

Code Book
2. The Code Book by Simon Singh (1999)
This book covers the history of cryptography from ancient endeavors to quantum computing. Given its age, the bits on web encryption are less relevant (especially in the Bitcoin era), but it’s full of incredibly interesting anecdotes– and nerd cipher tables(!). Here are some of the innovation lessons embedded within:

Cultural adoption is as important as technological advancement. There are innumerable examples of superior technologies losing out to more popular, inferior ones (just ask the Sony team that invented Betamax). In this case, the Vigenère cipher was the most powerful ever invented and was impenetrable in 1586. Encoding messages impacts the fate of nations and yet the Vigenère cipher was largely ignored for two centuries because other models were simply more en vogue. So few organizations focus on adoption strategies as part of their innovation agenda, but ultimately it’s as important a variable in success.

Divergent skill sets are better equipped to solve unprecedented problems. This is something organizations always claim to know, but never seem to execute well. Generally, companies define skill diversity as having different facets of the same job (e.g., front end design vs back end development). A truly diverse mix of talent (recruited via crossword puzzles!) was used at Bletchley Park to crack codes in considerably different ways. The problem was the same every day, but the solution varied. That’s an incredibly interesting proof point when thinking about innovation.

Similar innovations arise separately and simultaneously. History books tend to claim a single visionary person led the world into something new. In reality though, there tends to be multiple visionaries and cultural forces (especially in today’s globalized, networked world) that push toward the creation of similar ideas or solutions. For this reason, speed is becoming more important to the process of innovation. Instead of being too precious with an idea, hiding it away because you think it’s one of a kind, get it out into the world, share, get feedback, learn and make it better. Because if you’re not, someone else already is.

 

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3. Healthcare Industry Coverage by Wired (2013)
Ok, so I’m a total cheater and this isn’t a book. I’ve been avidly following the changes in healthcare this year and Wired seems to be truly dialed into the conversation. A collection of some notable articles are here. Watching an industry in a moment like this provides tremendous insight into how innovation works and what challenges can arise as a result of complicated ecosystems and relationships. Making this a consistent part of the stream is akin to reading a book (or watching a soap opera).

Unorganized data isn’t a missed opportunity, it’s a burden. Although health data is particularly sensitive, it’s interesting to note how healthcare industry members shift their view of data once they have the right tools. This is happening every day as more hospitals and caregivers install systems that help them manage data. For instance, after making tremendous infrastructure investments, the University of Pittsburgh Medical Center was able to turn overwhelming amounts of data into clear indicators of patients likely to have staph infections. Data innovation has long been a challenge because there was no focus. This isn’t strategic absence; this is a systemic void. Without a system, companies may be able to find new things, but not relevant ones. Now, Stanford data scientists have realized a cure for lung cancer may exist in plain sight in the form of an already FDA-approved antidepressant. That type of innovation is operational, technological and distributional all at once– thanks to the role of data.

Open innovation is moving into the spotlight. Innovation has been shifting consistently toward an open model for years, be it via companies bringing in outside partners for help, or collaborating with their customers. Seeing the healthcare industry– one of the oldest, most conservative and privacy-driven industries in the world– embrace outside partners from completely different cultures is a positive indicator that open innovation is no longer on the fringes; it’s in the mainstream.

Hopefully some of the material above is interesting enough to add to your reading list, or can simply serve as a cheat sheet for those looking for heuristics. After reading a post this long, the books will seem like quick reads.

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