The Harvard Business Review this month features a fascinating piece by Jeffrey Dyer of Brigham Young University, Hal Gregersen of Insead, and the omnipresent Clayton Christensen, entitled “The Innovator’s DNA.” The authors have completed a six-year study, summarized in the article, involving an in-depth analysis of 25 innovators and a further survey of 3,500 others who were connected to innovation in some way. The study attempted to identify key skills that separated great innovators from the rest of us.
The authors found five key innovative skills – Associating, Questioning, Observing, Experimenting and Networking.
In the article, a chart compares four iconic modern innovators (Michael Dell, Pierre Omidyar, Scott Cook and Mike Lazaridis) with noninnovators, in each of the five skills. The innovators are much above the noninnovators in each dimension, but in two skills the difference is stark: Associating (according to the authors, “the ability to successfully connect seemingly unrelated questions, problems or ideas from different fields”) and Questioning (”ask[ing] questions that challenge common wisdom”). Noninnovators fell below the 50th percentile on these dimensions, while the icons were with one exception above the 95th percentile of those studied.
I’ve been reading the book “Innovation Tournaments” by Christian Terwiesch and Karl Ulrich of the Wharton School. The book sets out a methodology (the “tournament” of the title) for companies to generate and systematically winnow down innovation ideas to eliminate all but the most exceptional opportunities.
Two brief observations:
One, the authors suggest that almost any company’s innovation performance would be helped by increasing the number of ideas going into the top of the funnel. Early-stage evaluation (a la “Discovery-Driven Growth“) is cheap and fast, so the cost of, say, doubling the number of ideas reviewed isn’t significant when compared with an overall innovation budget.
[It was interesting to read today's post by tech venture capitalist Fred Wilson, in which he outlined his approach to finding new opportunities: (1) making public his strategies, ideas, and passions so that entrepreneurs know in advance what he's looking for, and (2) meeting with as many people as he can, every day. In short, a strategy to add lots of opportunities to the top of his funnel.]
Two, along with the sheer number of ideas, the variability of the ideas is important. High variability increases the possibility that a truly outstanding idea is found (given that truly outstanding ideas, like 7-footers with great athletic ability, are few and far between). In that event, increasing the number of ideas coming into the funnel increases the likelihood that a truly outstanding idea is looked at.
Ironically, methodologies like Six Sigma seek to limit the variability of processes. When (mis)applied to disciplines like innovation, they are very successful at impeding the success of the effort.
Subject: Hot on the heels of Tim Brown’s “Change by Design,” Rotman School dean Roger Martin, author of “The Opposable Mind” discusses how design thinking can help businesses balance exploration (the search for new solutions) and exploitation (extracting value from existing solutions) to improve their innovative capability.
Did you like it? How many stars would you give it (1-5)? 4
Summary: Martin describes the process of innovation in three steps, something he calls the “knowledge funnel”: (1) staring into a mystery; (2) coming up with a heuristic, or rule of thumb, that allows you to address the mystery; (3) systematizing your solution – in Martin’s words, turning the heuristic into an algorithm. This process, to Martin, is design thinking.
He spends time discussing the preference business has for reliability (i.e., consistency and repeatability) over validity (meeting a desired objective). Validity is the starting point for innovation – the discovery of something new that helps illuminate a mystery. Since validity is not predictable or repeatable, and tends to rely on qualitative, intuitive assessments (i.e., pattern matching), companies that rely on quantitative measurement struggle with it. It was easiest for me to understand validity, as Martin uses it, as a synonym for “right-brained” or “artistic.” Successful businesses balance the desire for reliability with a relentless search for new validity.
As Martin described this process – taking mysteries, developing heuristics and then refining algorithms from it, it seemed quite simple. Why doesn’t every company do this? But I also thought that there are lots of mysteries that don’t lend themselves to heuristics, and lots of heuristics that can’t turn into algorithms. There are lots of failures on the way to the next great business algorithm. Not only that, there are lots of successful businesses built on heuristics alone [for example, your favorite restaurant, assuming it's not part of a chain]. Martin’s point, which is not stated explicitly, is that you can’t build large businesses without this transition to algorithms. You can’t have McDonald’s without a cooking and serving system. You couldn’t have Wal-mart without its distribution model.
There’s not a discussion of the cost of algorithmized businesses to society. On my last trip to downtown Boston I was hard pressed to find a business that was not part of a national chain; much different from when I Iived there in the 1990’s. But I digress – Martin isn’t writing as a social critic; he’s a business professor.
Favorite quotes:
“Vice President of Marketing” denotes a permanent position with a set of ongoing tasks…. As well suited as that construct is for running known heuristics and algorithms, it is not an effective way to move along the knowledge funnel. That activity is by definition a project; it is a finite effort to move something from mystery to heuristic or from heuristic to algorithm. pp.118-119
Designers produce prototypes for feedback, but managers are accustomed to delivering final products. p.121
Status comes from running large, high-revenue business units whose operations have been reduced to reasonably reliable algorithms that product results on time and on budget. Those are the highest goals, that is, the ones that command the highest compensation. That is why most executives prefer the known to the unknown. p.125
Was it similar to anything you have read before? Of course, there are echoes of “Change by Design” (Brown’s earlier HBR article is referenced). And the idea of “staring into mysteries” reminds me somewhat of “changing the inherent meaning of a product” from Roberto Verganti’s “Design Driven Innovation.” 2009 is definitely the year of design thinking in business!
Martin’s book is less ambitious than Verganti’s, but broader (in a good way) than Brown’s. And his ability to create a powerful, memorable metaphor remains intact (I think I’ll be using “knowledge funnel” and maybe even “validity vs. reliability” in the future).
Will this book end up on your bookshelf or in the library donation pile? The bookshelf.
But it’s very interesting to watch, as the amount of time spent cooking has fallen by about half since the 1960s, you know, obesity has risen dramatically. Now why should that be? Well, there is some very interesting research that correlates the amount of time that a culture spends cooking with its obesity rates, and that when you don’t cook and you rely on corporations to cook for you, you tend to eat more special-occasion food, things like French fries.
I mean, take the French fry. It’s a great example. I mean, the French fry did not become the most popular vegetable in America, which it now is, until corporations relieved us of all the work of preparing them. French fries are a whole lot of trouble to make. You’ve got to wash the potato. You’ve got to peel the potato, slice the potato, fry the potato and then clean up a kitchen that’s going to be a wreck. And, you know, you wouldn’t do that very often, and indeed, people didn’t do it very often.
But now, since corporations are making all the French fries, we can have them two or three times a day, and many of us do. So, you see, when there’s something built into the process of cooking that delays gratification, the work itself makes you think twice before you embark on a cake or French fries or fried chicken. And so as soon as you outsource that work, it becomes possible to indulge in all these special-occasion foods that no longer are special-occasion foods. They’re everyday foods.
And French fries wouldn’t be something we could eat two or three times a day without the Frialator. Rather than pouring oil into a pan, cooking, then discarding the oil, the Frialator allows restaurants to cook many dishes in the same oil, with only occasional filtering of the oil to remove food particles, until the oil is replaced, approximately one to two weeks in some cases.
In 1918 J.C. Pitman and Sons Hotel & Restaurant Equipment Manurfacturers, while attempting to work out a more satisfactory method of frying, made some important discoveries. One was that if the small particles of food which ordinarily settled to the bottom of the French Fry pot (where they collected and burned) could be kept away from the intense heat of that part of the kettle, the quality of fried food could be greatly improved. The Pitco Frialator was invented on this basic principle – and patented. This brought about a complete change in the method of deep fat frying. The fat medium was heated by tubes running through the center of the fat container. This construction permitted all sediment from the food being fried to drip below the heating tubes into a cool zone where it could not carbonize and break down the frying fat.
The importance of this construction is the reduction in fat costs, which exceeds by a wide margin the initial cost of the equipment, its depreciation and upkeep. Thanks to the thousands of Pitco Frialators now in use from coast to coast, deep fat frying has indeed become an art.
Half a million or more in use in US
The National Restaurant Association reported that as of 2009 there were approximately 945,000 restaurants in the US. Estimating that at least 50% of these restaurants use a deep fryer, there are a lot of Frialators out there.
The Impact of “Special Occasion Foods as Everyday Foods”: McDonald’s French fries have 380 calories per 4.1 oz serving. Eric Schlosser wrote in “Fast Food Nation” that “in 1960 Americans consumed an average of about eighty-one pounds of fresh potatoes and four pounds of frozen french fries. In 2000 they consumed an average of about fifty pounds of fresh potatoes and thirty pounds of frozen fries.” Thirty pounds of French fries equates to about 45,000 calories per person per year, using McDonald’s calorie counts. Meaning 307 million Americans (according to the US Census Population clock) will consume 13.65 trillion calories of French fries in 2009.
Bob Sutton posted on this San Francisco Chronicle article today, but it had so much good stuff relating to areas I’m passionate about that I need to write about it too.
The article concerns an effort by Bay Area nurses to reduce the occurrence of medication errors, which, according to the Chronicle, cause 400,000 preventable injuries and cost an extra $3.5 billion in medical costs each year. The results of the effort: a 88% reduction in medication errors in the participating hospitals.
Here are a few quotes that talk about areas I’m interested in – listening to and empowering customer-facing (patient-facing?) personnel, and the value of simple, low-tech solutions to business problems:
Striving to reduce interruptions that lead to mistakes, teams of nurses at the different hospitals came up with a variety of methods – often surprisingly low tech – to alert others they were administering medications….
The solutions “have to be low tech because we, as staff nurses, don’t have the money or ability to make high-tech changes,” said Celeste Arbis, a registered nurse in the medical-surgical unit there. “Something as simple as changing the process just a little bit can make a big difference.”…
Nurses attributed much of the program’s success to allowing those on the front lines to develop and tailor their own solutions.
I’ve seen both these situations in action: the ability of front-line personnel to understand and fix problems with the processes they use, and the effectiveness of often-overlooked simple and low-tech solutions. Sutton wrote something very profound in his post on this subject: “I think that people — especially managers — often use spending money as a substitute for thinking, when inexpensive and low-tech solutions work just fine.”
I learned today that Axe Body Spray for Men is running an ad in Uruguay where readers sending an SMS to their address receive on their phone the missing bits of a picture of a beautiful woman. (Those bits are clothed, BTW.)
This tells me there’s nothing about Axe the product that is distinctive, and the ad, despite being fun and engaging (especially for teenaged and 20-something males), won’t do much to make people select Axe over one of the thirty other male scent products out there.
I started thinking about this after listening to Jonathan Salem Baskin’s neat Listrak webinar last week, entitled, “Marketing Ideas for the First Post-Brand Decade.” Baskin did a nice job of showing that while customers and markets have moved beyond the days of “Mad Men” – where a well-crafted, creative advertisement could influence us to buy the latest dish detergent or safety razor – marketers, by and large, have not. Even “social media marketing,” like, say, the Axe campaign, is taking the same old ideas and porting them to new technology.Houston, we have a problem. Marketers are pushing the same old buttons to sell more variations of the same old products. It’s a negative-sum game. Variations increase cost without enlarging the overall market. Redundancy pushes down prices, invites private label competitors and overloads consumers’ minds.
Clearly, we’ve got to do something different. Marketing needs to pull back from its focus on distribution, packaging, and communication, and refocus on helping create great new products, that deliver distinctive value and make people’s lives better. Then it will be easy to communicate that to prospective customers.
Gary Hamel writes in “The Future of Management” that product & service innovation are near the bottom of the innovation hierarchy, and the pinnacle is “management innovation.” To Hamel, products are easily duplicated, quickly eliminating their added value. But as Roberto Verganti pointed out in “Design-Driven Innovation,” companies that create truly visionary products enjoy long periods of competitive advantage and profits.
Life is too difficult for many and too complex for everyone else. Everyone would like to have more fun. Therefore, there’s lots of need for products & services that allow us to manage our lives better or have diverting or engrossing experiences.
I’ve been reading “Change by Design,” by Tim Brown, and he asserts that companies need to adopt “design thinking” to create great new products and services. I can’t disagree with him, but also feel that design thinking is not that different from what great product managers and developers have been doing and should be doing. So, if your new-product group wants to hand over the reins to design thinkers, that’s their prerogative. For me, that’s the fun part of the job and I’d rather not outsource that.
One of the most useful lessons in Rita Gunther McGrath’s and Ian MacMillan’s “Discovery-Driven Growth” is a basic one: when you’re planning a new business venture, your initial plans are laden with assumptions, and treating these assumptions as facts will get you in trouble. Yet it’s done all the time.
This lesson was reinforced in the nice interview with Rita that appeared in yesterday’s Wall Street Journal Business Insight section (”Learning from Corporate Flops“). The headline is a bit misleading – she talks less about learning from your flops than about carefully documenting your assumptions, testing them as early and cheaply as possible, and revising them when you learn they don’t hold up.
Having a detailed list of assumptions allows many people to weigh in on a new business idea – even if you can’t speak authoritatively on the whole concept, you may have very good insight on one particular assumption. And assumptions are expected to be wrong much of the time, so questioning one or showing evidence why it’s not valid is easy for the new product team to accept.
On the other hand, probing and questioning a business plan in which the key assumptions are buried and not distinguishable from known facts tends to invite emotional arguments which rarely improve the quality of the plan.
Autism is a vast problem; no single researcher or lab can take on its full breadth. I focused on just one piece of it: the data that everybody else was throwing away. I call it the garbage-can approach. My belief is that answers to really difficult problems can often be found in the data points that don’t seem to fit existing frameworks.
Scherer looked for patterns in this outlying data. His success in finding some genetic markers for autism reminded me of the plea a couple of years ago to “free the dark data” from failed scientific experiments. And it reinforces the value of the “beginner’s mind” when approaching new challenges.
I am posting this from my laptop, about 30,000 feet in the air, somewhere over Virginia, I’m guessing. And it’s a thrill. Not only because it’s novel to have WiFi access in the air – I have a personal connection to this service.
Roberto Verganti mentioned in our podcast (to be posted tomorrow) that many CEOs of highly innovative companies have a personal pride in their products. They announce them and are personally invested in their success. If they fail, the CEO takes responsibility.
Of course, this kind of pride isn’t limited to CEOs (Prof. Verganti might observe that CEOs are sometimes less proud of their products than other employees). In fact, people who don’t even work for the company may feel connected to the product and share this pride.
And one illustration of it is the delight you get when you realize the product is being used by real people. In fact, this feeling may be the biggest rush in innovation. I remember, years and years ago, creating requirements for a tiny new component of an outside plant management system for GTE. I worked on it for a few months, then moved to a new assignment at the company and promptly forgot about it. A couple of years later, I ran into one of my co-workers from that project at a meeting. He told me that my module had been part of a release that was now in widespread use throughout the company. I was walking on air for a few hours after that.
The product I’m using now is called Gogo Inflight from a company called Aircell. I spent the better part of a year and a half consulting for the company that developed and now runs the ordering/billing/customer management system, Martin Dawes Systems. My work was way behind the curtain – negotiating with the company that hosts the servers, arranging hardware purchase and delivery, liaising between Aircell’s operations team and Martin Dawes’. Yet I’m no less proud of the end product than if my picture were on the Gogo home page.
It’s a bit of a miracle, actually, WiFi in the air. And though my work on the project was more blocking and tackling than anything else, I feel as if I own a tiny part of that miracle.
An interesting piece in today’s WSJ Business Insight section (”The New, Faster Face of Innovation” by Eric Brynjolffson and Michael Schrage of MIT) asserts that information technologies are reducing the cost of business experimentation and increasing the speed of rolling out new processes and approaches to the organization as a whole. As a result, more and more businesses are moving to use experimentation as a basis of their innovation programs. Here’s an excerpt:
Innovation initiatives that used to take months and megabucks to coordinate and launch can often be started in seconds for cents.
And that makes innovation, the lifeblood of growth, more efficient and cheaper. Companies are able to get a much better idea of how their customers behave and what they want. This gives new offerings and marketing efforts a better shot at success.
Companies will also be willing to try new things, because the price of failure is so much lower. That will bring big changes for corporate culture—making it easier to challenge accepted wisdom, for instance, and forcing managers to give more employees a say in the innovation process.
There will be even better payoffs for customers: Their likes and dislikes will have much more impact on companies’ decisions. In globally competitive markets, they will ultimately end up getting products and services better tailored to their needs.
I agree with Brynjolffson and Schrage that experimentation-based innovation will have tremendous impact on improving products and reducing companies’ innovation costs. But while they credit IT enablers, I think there’s another crucial reason that experimentation is growing in popularity. Schrage touches on it in this video companion to the article:
The framework is useful for lots of purposes: knowledge management, strategic planning, managerial action (the subject of Snowden and Boone’s HBR article, “A Leader’s Framework for Decision Making“). But here I’m discussing applying it to thinking about innovation.
Schrage’s casual comment illuminates innovation’s relationship to Cynefin. “The culture of grand planning and initiatives” is dominant in most companies, and shows that they view innovation initiatives in the Complicated domain of the Cynefin framework (I find the original terminology from Kurtz and Snowden helpful–”knowable”). Knowable or complicated systems are ones where cause and effect are related–but may be separated in time. You often need expertise to diagnose and act on a situation, but once the system is solved, the way forward is clear.
Traditional innovation initiatives treat the interactions between companies, customers and markets as a Complicated system. Innovation projects are expensive and time-consuming – you often hire consultants to lend their expertise. There is a solution–certain objectives and expectations that the initiatives must meet (these are often encoded into business plans and pro forma P&L’s). Of course, you only need to be involved in one such initiative to know that they never deliver to plan. Innovation initiatives are always surprises–sometimes delightful upside surprises, but more often long, expensive failures. This is because they treat a Complex problem (in the Cynefin definition) with a tool suited for the Complicated domain.
In the Complex domain, cause and effect are not observable in advance–”grand planning” is not productive. The outcomes of a complex process seem logical – but only in retrospect. Why did Twitter evolve the way it did? Why was iPod/iTunes so revolutionary and so successful?
“The culture of experimentation,” on the other hand, acknowledges this complexity – that the objectives of innovation – creating interesting, popular, valuable and attractive new products and getting them into the hands of customers – are not straightforwardly attained and cannot be planned. When customers buy certain products, when they linger on certain web pages over others, when they flock to some brand-new platform, they are exhibiting behavior best described by the language of complexity. And the way to achieve progress in this domain is to use experimentation: generate lots of ideas – perhaps even some deliberate mistakes. Try them out. If something works, spread it around. If it doesn’t, kill it quickly and move on. Iterate. [The Toyota Production System applies this experimental thinking to manufacturing innovation.]
So, is the ability of information technology to make experimentation fast & cheap responsible for the increasing use of experimentation to achieve innovation’s goals? Yes, in part. But a great deal of the reason lies in the fact that the old way of innovating, “grand planning,” isn’t the right tool for the task.
[Another viewpoint on innovation and experimentation is in McGrath and MacMillan's recent book, "Discovery-Driven Growth." While the book doesn't use the Cynefin terminology or share the complex adaptive systems roots, it nonetheless focuses on the uncertainty of the innovation process and emphasizes the need to cheaply and quickly experiment, allowing successful projects to emerge.]
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