At its core, a business is in business to solve problems:
- Lawyers solve legal problems.
- Supermarkets provide a solution for what to eat.
- YouTube solves the problem of not having a central repository to watch cat videos.
You get the idea.
As a data scientist, you support the core business function of solving problems by providing data-driven insights to improve the decision-making process. One of the most common — and cliché — approaches for solving problems is “thinking outside the box.”
And while this approach was effective 100 years ago, technological advances — particularly the internet — have rendered it invalid today. The way we conduct business is very different than it was 100 or even 10 years ago, when outside-the-box thinking was sufficient.
But in order to solve tomorrow’s problems, we need to start thinking inside the box.
Thinking Inside the Box
The concept of thinking inside the box came to me after realizing two things. The first was when I read an article about marketing guru Lynda Resnick, who more than likely coined the term. “Everyone says think outside the box,” says Resnick, the brains behind the marketing of such brands as FIJI Water and POM Wonderful, “but the problem is inside the box, and the solution is part of the problem.”
The second arose when a book in the Denver airport caught my eye, The Medici Effect: What Elephants and Epidemics Can Teach Us About Innovation by Frans Johansson.
Johannson’s book revolves around the concept of forming new idea by bringing together seemingly unrelated disciplines, which, as patrons of the arts and banking moguls, the Medicis unwittingly did, thus — many claim — spurring the Renaissance. In effect, says Johannson, the melding of old concepts to spawn innovation was due to the Medicis’ patronage, which brought together unrelated industries and allowed for a then-unorthodox exchange of ideas.
The same has occurred in academia. Two or three decades ago, it was rare to see anything other than discrete departments in colleges: physics, chemistry, business, psychology, etc. Today, however, these silos are disappearing and are being replaced with quasi-hybrid departments and fields like data science (a combination of math, computer science, and business), chemical biology, and chemical engineering (a combination of chemistry and engineering). Interdisciplinary research is also on the rise.
The interdisciplinary nature of the data scientist means that, to truly embrace your inner Medici, you have to look to other industries for inspiration on how to approach problems in your industry. Your success is often determined by the number of intersections and combination of ideas that you can combine.
That’s how scientists created an LCD display that uses far less energy than its predecessors. The design was inspired by studying how butterflies create the pigment in their wings. And many of us already know the tale of how Velcro was inspired by burs stuck to clothing. Those are just of the many examples of cross-disciplinary idea pollination.
Looking outside your discipline becomes important to your work as a data scientist, since the whole crux of your job is to find solutions to novel problems. And as Einstein himself once noted, “We cannot solve our problems with the same thinking we used when we created them.”
Creating Your Own Medici Effect
Johansson talks about several ways to replicate the Medici effect. Here are three ways you can apply this way of thinking in your day-to-day.
1. Break out of your network.
You can become stagnant if you remain within the confines of your current network. If you’re not exposed to different ways of thinking and doing things, then you’re bound to adopt the “we’ve always done it that way” mentality — which is pure poison to innovation. Entrepreneur Jim Rohn once said that “you’re the average of the five people you spend the most time with.” If you’re always around the same people, it’s hard to create the Medici effect since you’ll never see what is going on outside your circle.
Go to an event, such as a trade show or seminar, in a field other than your own. For example, if you work in the energy sector, attend an event in big data or consumer psychology. Borrow ideas for solving problems in that industry and bring it to yours. A perfect example of this is how software is being used to modernize “old” industries. Think WeWork.
2. Break down the barriers between fields.
More-established enterprises tend to have very strong “organizational gravity,” or a predisposition for the status quo. This is due to a combination of “if it ain’t broke, don’t fix it” mentality and old-school organizational structures, which tended to favor discrete departments and silos. Today, however, a lot of companies are flattening their hierarchies and providing more opportunities for cross-pollination between departments. At Zappos, for example, managerial roles don’t even exist.
Within these flattened organizations, it’s common to have very different and dissenting opinions that need to be resolved before moving forward. One way to overcome this obstacle is to apply “yes-and” thinking. Here’s an example of the traditional approach to a problem versus the “yes-and” approach.
Traditional Approach
Employee: I’d like to attend a weeklong data science conference in Germany next month. I know that’s in the middle of our busy season, but I think it would be beneficial for me and the company.
Boss: No, we didn’t plan for it in the budget and we need you in the office to get these reports out.
Employee: Okay, maybe next year. [Immediately starts to look for a new job.]
The “Yes-And” Approach
Employee: I’d like to attend a weeklong data science conference in Germany next month. I know that’s in the middle of our busy season, but I think it would be beneficial for me and the company.
Boss: Yes, and since we didn’t budget for it this year, I can make an adjustment for next year. Also, I can pitch in and help out with your reports while you’re gone. We need to kick things up in this department. It’s going stagnant …
Employee: Okay, awesome. I can’t wait to come back and share what is going on in other industries about X.
[Morale immediately goes up, like, 1,000%.]
You may not be surprised to learn that “yes-and” thinking is the basis of all improv comedy. Author and entrepreneur Karen Hough applies this strategy in her workshops for managers, where participants are encouraged to continually say yes. By shifting their language, Hough notes, they shift their thinking.
You can read more about the yes-and method is Dan Pink’s book To Sell Is Human: The Surprising Truth About Moving Others.
3. Randomly combine concepts.
Venture capitalist James Altucher states that “ideas are the currency of life.” His belief in this concept runs so deep that he recommends keeping a waiter’s pad handy and jotting down ten ideas per day so that, after one year, you’ll have 3,650 ideas. Altucher notes that, although most of those ideas won’t be viable, “one or two might be decent.” However, as with any practice, the more you do it, the better you’ll become at it.
But the real way to make an impact with ideas, Altucher says, is through idea sex:
Combine two ideas [from your notepad] to come up with a better idea. Don’t forget that idea evolution works much faster than human evolution. You will ALWAYS come up with better ideas after generations of idea sex. This is the DNA of all idea generation.
Let’s tie these three concepts together:
As a data scientist, to embrace the Medici effect, you should capture problem-solving concepts from two different industries and combine them to create a new set of ideas that may be of value in your specific situation.
There you have it. Go forth and multiply your ideas.
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Grant