From Mystery To Algorithm A More Reliable Innovation Process
Think of innovation and change differently: as a learning journey where we try to make newness as automatic as quickly as possible.
Arguably, much of the innovation work I was part of in East Lothian in the 2000s was a mix of luck, created by listening hard and often to what people in the community were saying. My colleague at the time, David Gilmour, was an expert listener, and continues to this day to tweak not only the online offerings for teachers across the district, but also the professional development offerings for them. But there are more structured mechanisms to avoid luck alone, and the effort of pounding the streets that David and the rest of the team undertook.
After three years in Government working on projects like those in East Lothian, I went on to experience innovation with Channel 4 Television Corporation and various regional investment agencies across the country, investing millions in technology ideas that would bring public services to the masses. The setting was totally different — and not directly related to education at all — but the challenges were remarkably similar.
Part of the downside of working with and investing in startups, is that you have to hear a never-ending stream of good ideas, and not all of them are the next Facebook — far from it, in fact. Over five years of investing during an economic downturn, in my capacity as an investor or advisor for media companies, I read through over 3000 ideas from innovators, entrepreneurs, business people and public servants. All of them think their idea is innovative. Only 30 have seen their idea receive investment. Only a dozen or so of these have made any money worth talking about. One is consistently in Apple’s Top 30 all-time best-selling games list.
This might sound like a low success rate, and it is. But the advantage of expending real effort on so few ideas is that those which succeed do so in grand style. The advantage for the 2970 or so others is that they receive regular critique on why certain ideas might work better than others.
Paralysed by initiatives
In the world of education, it sometimes feels like the opposite is true. Most schools and their teachers, leaders and school boards, are paralysed by a terrible, perennial case of initiativitis. Instead of dealing with the two or three big ideas that will make their fortune, schools seem to be expending cash, hours and energy on the 2970 other ideas that feel cute at first blush, or important for the here and now, but whose long-term value, their long-term ‘business model’, is debatable.
Coming up with great ideas, buying stuff, and trying things out is not an Innovation Strategy. When you apply this to the 2970 ‘dead’ ideas it is also incredibly costly and demoralising. Trial and error discussions, like those we undertook in East Lothian district, are not an innovation strategy either. Neither approach is systemic, and both rely on the wits, experience, and luck of those invested in making the idea come about. To move ideas beyond their islands of excellence, out of the black box of the classroom, we need something that is more reliable.
Taking ideas beyond the Black Box
At Canada’s Rotman School of Management, Professor Roger Martin has spent time trying to work out systems for making creative and critical thinking in innovation projects more predictable, more “systematised”. Martin describes the “knowledge funnel” of every organisation.
At the beginning of any new idea or innovation, much of the organisation sees that innovation as “mystery” — it’s unknown, perceived as unimportant, perhaps, or even dangerous to the organisation. As more is learned about an idea by those in the organisation, that knowledge forces a better understanding, one of gut feel, instinctual understanding of the new idea — “heuristics”. It is only once knowledge about the idea has become a strong enough foundation, an understanding about which the organisation doesn’t have to think too hard, that we can talk about “algorithmic” understanding of the innovation, and it’s often at this point that we see innovations adopted on a much larger scale. This pushing of knowledge through the funnel demands that some of the initial complexity and nuance of an innovation might be lost:
“Algorithms demand stripping away extraneous ideas and the potential of possibility. It’s hard to reverse back up the knowledge funnel once ideas have been stripped away. They’re often lost forever.” (p.17)
We see this clearly with technology innovation. In the early days of blogging, there were a bunch of subtleties and complexity in creating, maintaining and growing a loyal readership of the blog — innovators were those who, frankly, could write well, often and about interesting things, but who could also code and manage the searchability of their text. I remember a twelve year old kid in my class, Hassan, editing my hand coded XML in order to get podcasts appearing in iTunes. One-click publishing in 2004 was really three clicks and some code. Today, many of those skills are just not required as the technological interface for publishing online is seamless, and more people are used to the processes involved. Today’s “new innovations” build on the “old innovations” that many of us struggled through in first part of the century.
But today’s “new innovations” can also mean the loss of some of that subtlety from the early days. More people “blog” on Twitter, 140 characters at a time, than spend a morning crafting a long-form blog post. The “half life” of a text online has been reduced from maybe a week of commenting, interaction and replies on a 2004 blog, to minutes of retweeting, replies and reading on Twitter. Writing a good tweet is not the same kind of writing as forming a 600 or 6000 word blog post.
But what Twitter, Facebook, and other social networks have allowed is a growth in the numbers of people sharing something, anything, online, because doing so has become possible without thinking. It has gone from something that was mysterious (a few people did it), to heuristic (a lot of interesting great writers do it) to algorithmic (nearly everyone seems to be doing it, but we pay the price in some of what we see from them being less routinely of interest or of higher quality).
Making sure we don’t lose the good ideas as we scale our innovations beyond the Black Box
The danger with this kind of scale is that some of the “magic” of an initial idea may, and probably will, be lost. The innovator in their classroom is often unconsciously reluctant, in fact, to share their ideas widely for fear of having them watered down, or lost in the inevitable conformity of “the system”. And it is the outlandish ideas, the circles that don’t quite fit the existing square, that can provide the big breakthrough innovations we want to harness.
For example, take Dr Scherer at the Hospital for Sick Children in Toronto, a researcher into autism who is trying to find a cure for millions of children and adults. To do this, he plots autism data from research on a chart. Most scientists would logically concentrate on the majority of cases, trying to find trends and similarities that might lead to a cure for those masses. They would concentrate on the 2970 other ideas. Scherer is different. He goes for the “outlandish cases, the ones ignored by research as extraneous or fluke results”, as these are the cases where often the cure could be found.
We need innovation strategy
Charles Leadbeater calls this The Predilection Gap — the meeting between analytical and intuitive thinking, specificity and flexibility. It is in this often large chasm where some of the best innovations are born, and it is in this gap that school leaders, innovators and creatives love to operate.
But to thrive in the gap, one needs an Innovation Strategy, a few stepping stones, a safety net and a jetpack to move from the status quo on one side to the big hairy ambitious goals that lie beyond the gap, on the horizon.
This is an adaptation from my book, How To Come Up With Great Ideas and Actually Make Them Happen.
