Debunking the Myths of Innovation
Myth 4: Innovation is expensive
Reality 4: While emerging technology and drug research are expensive, most innovations require a modest disciplined investment of time and brain power
About 15 years ago, the economist Paul Krugman compared research trends in economics to the evolution of map-making in Africa:
“The coastline …was first explored, then with growing accuracy, and by the eighteenth century that coastline was shown in a manner essentially indistinguishable from that of modern maps… on the other hand, the interior had emptied out.
"The weird mythical creatures were gone, but so were the real cities and rivers. In a way, the Europeans had become more ignorant about Africa than they had been before… Improvement in the art of mapmaking raised the standard for what was considered valid data. Second-hand reports of the form “six days south of the desert you encounter a vast flowing river from east to west” were no longer something you would use to draw your map. Only features of the landscape that had been visited by reliable informants equipped with sextants and compass now qualified as valid data…”
The same thing has happened with “market research” and “trend forecasting”. We are so reliant and only accept as valid information from “reliable informant” (the analysts) with sextants and compass (surveys of CIO’s) that we have lost sight of the more general (and more than good enough) insights from immersion and observation of what is going on. This means much if not most of the (less precise and perhaps less accurate) information that was available to old style explorers is now missing from the modern forecasters radar.
Existing trends are easy to identify. Analysts, the press, statistical analysis of social media and other sources can all tell you what existing trends are and the values such trends represent. The crux of innovation is to detect where, when and how these trends are evolving – where the trends will lead – and also what these signal about future trends that are not yet obvious. It can be thought of as jumping from one S curve to another. The trick is not in developing the new technology (including process technologies) but in knowing when and how to apply it through careful analysis of existing and emerging trends.
A number of techniques can be (and are) used depending upon what one’s “animal instincts” suggest is happening in the market. Millions of years of evolution have given us one of the best pattern matching (and analogue operating) computers ever – the human brain. Too often in business, we rely only on digital and visual inputs, in other words, words and numbers/data that are one or two times removed from actual experience.
The result – to build on Henry Ford’s statement about what customers want – is breeding faster horses rather than establishing the mass production of automobiles. To become true visionary innovators, we must provide the human brain with the greatest volume and variety of inputs (this is why we have so many senses). Then, we must allow the intellect to summarize this input into a general theme of what is happening and where the inputs are leading. At that point, multiple techniques can be used for the necessary testing of instinctive insights.
One of the major techniques is simply content analysis: What is and is not being written about and talked about – not only in business and technical literature, but also in the general press, social media, fiction (science fiction is always a good leading indicator – just look at that “Star Trek” communicator strapped to your belt). Innovators should spend a significant amount of looking beyond traditional business and technical literature. We also must be aware of what is occurring legislatively, socially, economically (it is amazing to see how often business misses basic economic activity and resulting opportunities) as well as others areas.
Other techniques include:
1. Application (what are people applying existing stuff in unintended ways),
2. Abstraction (is there a higher level description of what is going on),
3. Identification (you say “mammal,” but do you mean a duck bill platypus, or do you mean a female astronaut with a PhD in astrophysics and an MD in cardiovascular surgery?). For example, within IT, identification is critically important for technologies such as “cloud,” and “security” and “virtualization.”
4. Mimicry (looking at variations on what is being seen or sensed – “this is like that”,
5. Symmetry (if this, then the opposite should also happen),
6. Unification / convergence (multiple themes collapsing into one or fewer). History does repeat even as the world progresses. One must determine if the observation points to a retro theme (for instance, returning to shared services organizations, or a future theme (work being mobile across organizational and even corporate boundaries). We also must seek out arithmetic applications to themes, meaning can you add to, subtract from, divide or multiply to get a more desirable state (including economic, social, demographic, legal, regulatory, business, technological and other states).
Ways of doing all this include:
1. Brainstorming (immersion with customers, prospects, sales forces, academics, etc.),
2. Storytelling and its resonance with market elements;
3. Shadowing, or “skulking” on social media sites to observe what people / organizations are really doing versus what they say they are doing (and yes, you can still ask them what they are doing),
4. Applying human factors and design principles to existing and evolving usage patterns to see paths of least resistance for trends to evolve to, prototypes to test with, and just good old fashioned creativity processes (like a whack on the side of the head courtesy of von Ott!).
5. The real key is to be able to build the framework, an idea ecosystem, of themes and scenarios, which we discussed in Myth 3. Such a framework enables quick validation of innovative ideas against when they occur. This is necessary so truly “new ideas that are forward thinking, feasible, viable and valuable” are implemented rather than getting lost.
I would argue the assumption about the need for even more investment in innovation. One of the unintended consequences of the move to cloud / utility computing is that it totally disrupts the economic frictions that heretofore dictated the invention / innovation cycle of IT. With much less funding than required before, startups can create highly niched offerings that can be very profitable (due to reduced costs or friction in sales, distribution, support, maintenance, etc.).
As more leveraged infrastructure and services (IaaS, PaaS, SaaS) are brought into play, the cost of IT and IT investment goes down and gets reallocated from “maintenance, hygienic and housekeeping activities” to true customer value creation and competitive differentiation activities. This, in turn, requires a much more systemic process for innovation.
Through careful consideration and analysis of trends using old-style forecasting techniques, businesses will find that innovation is not expensive. Knowing when and how to apply a new technology is of vital importance and can save businesses developing and investing in a new product that can become outdated quickly.