Archive for category selection
When is it OK to Ignore Innovation?
Posted by Tim in book riffs, evolving economic entities, selection, time on 8 January 2012
The earth has been around for 4.5 billion years or so. If you think of the last 10% of that time, a fair bit has happened. There have periods of major global warming, and a few ice ages. There have been asteroid strikes, and other natural disasters too numerous to count. Continents that were one continuous land mass 450 million years ago are now separated by oceans. And there have been five major extinction events.
Through all of that change, disruption and chaos, what has been the most stable environment on earth? The deep ocean. There’s no light down there, so it doesn’t matter if an asteroid strike kicks so much stuff into the air that all of the coral reefs and dinosaurs die out. It’s always cold, so climate change up on the surface doesn’t have much of an impact either. The deep ocean has stayed pretty much the same all the way through.
And that’s where the Coelacanth lives.
I’ve been fascinated with Coelacanths since I first read about them in On Methuselah’s Trail: Living Fossils and the Great Extinctions
The first fish in this family show up in the fossil record about 400 million years ago. Their fossils are pretty consistently around for a long time, until they disappeared about 65 million years ago around the Cretaceous extinction, the one that killed off the dinosaurs.
Because there was no record of them for 65 million years, scientists thought that they were extinct. And then a museum curator found one in the catch of a fishing boat off the coast of South Africa in 1938. In a curious aside, it turns out that the fishermen had known about the Coelacanths for a long time, but whenever they caught one they threw it back because they’re apparently very poor eating. It was only once they realised that museums were willing to pay them for specimens that they started to keep them.
There are two species of Coelacanth around now, and structurally they haven’t changed much at all since the first specimens from 400 million years ago.
In other words, they haven’t innovated one bit in 400 million years.
Why? Because they live in the deep ocean, the most stable environment in the world over that period of time.
So the answer to the question When is it OK to Ignore Innovation? is: when you’re in a stable environment.
Just as the Coelacanth shows that you don’t necessarily have to evolve to survive, in the economy you don’t necessarily have to innovate to survive. If, and it’s a big if, your environment is stable. It doesn’t need to be as stable as the deep ocean, but if you have good market share in an established industry, with little macroeconomic fluctuation, and you’re happy with your overall performance, then go ahead and ignore innovation.
The rest of us probably need to be thinking about how to execute some great new ideas, and also how to get those ideas to spread.
In his book The Evolutionary World: How Adaptation Explains Everything from Seashells to Civilization,
The evolutionary dividends of a warmer world are attainable only if three conditions are met. First, populations must have ready access to a plentiful supply of necessary resources, so that when an imperfect innovation arises, it can linger in the population long enough to be improved by selection. If the population is allowed to grow under a permissive regime of of predictable plenty, not every deviant individual is purged from the population, and selection has enough to work with. Second, competition for locally scarce resources – the main agency of enemy-related selection – must be intense enough and consistent enough to allow improvements to spread in the population. Third, there must be sufficient evolutionary time – thousands to millions of years – to allow selection to do its work.
You can translate these rules of evolutionary innovation over to economic innovation:
- You need slack resources to innovate. This is why efficiency and innovation often come into conflict. As Greg Satell says, most innovation is crappy. Vermeij points out that imperfect evolutionary innovations need sufficient resources to keep them around long enough to be improved by selection. It’s exactly the same for economic innovations. They rarely work as planned at the start – they need feedback from customers, suppliers and others to really become good. That takes time and resources.
- Innovation works best when there’s competition. Even though there are extra resources around, there still needs to be competition to drive improvement. If the environment is too stable, like the Coelacanth’s, the lack of competition leads to no innovation.
- You need time to turn your crappy innovation into something excellent. Innovative ideas diffuse along an S-Curve, and it usually takes a lot longer for this to happen than we expect it to. Fortunately, economic innovations don’t need hundreds of thousands of years for this to happen, but the gap between having the great idea and seeing it adopted is still usually very long.
Innovation is an evolutionary process, and you can learn interesting things about this process by studying natural history. And the story of the Coelacanth shows us that there even times when you don’t have to innovate at all.
Innovation Through Subtraction
Posted by Tim in book riffs, design, evolving economic entities, selection on 1 January 2012
I don’t like focus groups. I’ve found the information that you get from them to be too shallow to be useful. However, this doesn’t mean that when we’re innovating we should just pursue whatever ideas drift across our minds.
Steve Jobs was quoted last year about how Apple doesn’t use focus groups. A number of people used this quote to justify being completely out of touch with their customers, which is a perversion of the main point. The reason that Apple can skip focus groups is that they are incredibly good at understanding what people are really trying to accomplish with technology.
To do this, you have to develop a deep understanding of what the core issues in your field are. Here’s an analogy:
There is a chapter by the scientist/artist Jonathan Kingdon in the excellent new book Field Notes on Science & Nature
In the age of instant digital photography it may seem perversely old-fashioned to put a value on the slow, primitive, and inaccurate techniques of manual drawing. Photography teaches us that the very act of putting a line around the edge of an observed object is an artifice. Such outlines rarely appear in photographs, or, for that matter, in nature, and yet… and yet? Contemporary research on the human brain shows that it does NOT process images as a neutral camera does. The brain finds edges and builds constructions that are at least partly based on previous experience 0 possibly including past contacts with artifacts such as “drawings” as well as previous knowledge of natural objects. Visual neurobiology is a discipline in its infancy, but it confirms that visual constructions are both complex and integral to cognitive development. This implies that even an outline sketch that bears little relationship to the so-called objectivity of a photograph might actually transmit information to another human being more selectively, sometimes even more usefully, than a photograph.
…
If the brain is unlike a camera in actively seeking outlines, there is a strong implication that “outline drawings” (just to take a single type of visual expression) can represent, in themselves, artifacts that may correspond more closely with what the brain seeks than the charts of light-fall that photographs represent.
What does this mean in practice? It means that these drawings of a caracal by Kindgon may well transmit information to us that is more useful, more real, than what we could get from a series of photographs:
Those drawings do a great job of capturing something fundamental about the animal, as simple things often do. But to be able to draw them, you have to invest an enormous amount of time in observing the caracals, looking at what they do, in which contexts, to build up a deep knowledge of how their physical form expresses what they are trying to do.
You can’t ask a caracal (or even a house cat) what they are trying to express when they pin their ears back. But if you watch them long enough, the meaning becomes clear.
Now, customers can answer questions more clearly than a caracal. Usually, at least… But sometimes, this greater ease of communication actually makes it harder to understand what they’re really trying to achieve.
It’s not an accident that the Apple products look like art. The essence of great design is to be able to communicate simply by stripping down an object or a process to it’s fundamentals – which is the same problem with which artists grapple. This is filtering, and it’s how we deal with the avalanche of information which sometimes overwhelms us.
To innovate well, we need the same kind of deep understanding of our customers that artists have of their subjects. This allows us to strip our offerings down to their essence – innovation through subtraction.
(For more examples of some of the beautiful art in Field Notes on Science & Nature, check out this page from Wired.)
The Exigency of Extrapolation
Posted by Tim in complex systems, experiments, selection on 12 December 2011
Noun 1. exigency – an unstable situation of extreme danger or difficulty;
I’ve had some jobs in which I’ve performed pretty well, and some where I haven’t been quite so good. Probably the worst job I’ve ever done was part of my portfolio when I was managing sales & marketing for a polytechnic in New Zealand. The specific job was new course evaluation.
We put in a modified stage-gate type process to evaluate potential new courses. It was my job to fill in the numbers. The one number that drove everything else was the expected number of students. If the expected number of students was high, we’d try to run the course. If not, we’d kill the proposal.
I developed a very elaborate model, based on historical data. I knew how many enquiries we could generate from advertising, how many of those we could convert into applications, and how many enrolments we got per application. In fact, figuring out these numbers was one of the biggest innovations that I executed there, and this model was of tremendous use in trying to figure out around November how many total students we could expect at the start of the new school year the next February.
However, the model was terrible at predicting new course enrolments. Why? In large part, because we’re really lousy at figuring out how something new will perform. We rejigged our new course approval process after I pointed out that we hadn’t approved a single new offering in over 6 months – our process was killing everything.
I was originally going to call this post The Perils of Prediction, but Greg Satell beat me to that title. Also, the specific problem that I’m talking about is really extrapolation. You should read all of Greg’s post, but here’s part of what he says:
The problem starts when smart people in nice suits and lab jackets proclaim that “the data says…” In truth, the data never says anything. We interpret it in one way or another and there are lots of ways to interpret it incorrectly.
Data is, after all, messy. It doesn’t spring forth whole, but must be collected in some way. We count, measure, survey, aggregate, slice and dice, picking up errors all the time. We need to make choices about which data we want to focus on and which fades into the background.
How do we deal with this? Usually by finding some numbers from the past and extrapolating them. However, there are a few problems with this approach, including:
- We tend to think in straight lines, but there aren’t any straight lines in business: that’s really the point being made by the xkcd cartoon. Taking a straight line and extrapolating it into the future almost never gives us the right answer.
- It’s really hard to tell what kind of curved line we’re on: this complicates things too. Even when we have historical data, it is nearly impossible to figure out what kind of engine is generating the output. Take a look at this data from an interesting post on climate change:
Is it likely that the data will progress in a straight line? Or will it level out at some point? Or will it increase exponentially? We don’t know. But when we’re predicting, it pays to consider what circumstances might lead to each of these outcomes.
- Even when things are accelerating quickly, they tend to level out: innovations spread through an s-curve, and this is a very common pattern in business.
This is one of the issues with everyone talking about the singularity – it assumes that exponential growth will continue forever. It might, but usually exponential growth levels out, and then it looks like an s-curve.
- However, by the far the biggest problem with extrapolation is that if we depend on extrapolation for predicting, we will never anticipate something new happening: extrapolation can only predict that things in the future will be mostly like things in the past. Here’s how Greg puts it:
And that’s what most analysts miss. The future is hard to predict not just because of our cognitive biases or inexplicable natural events, but because we have the power to make our own future.
The first new course that we approved at my Polytechnic after we scrapped the stage-gate process was a program that offered free computer and internet lessons to people in the community, particularly targeted at older adults. And the number of enrolments that we got went so far beyond anything that we had ever seen before that it was almost impossible to believe.
None of my models could have predicted that. When we innovate, our job is to invent the future. The exigency of extrapolation is that if that is the tool we use to predict, we won’t be able to invent anything that doesn’t already exist. And what kind of innovation is that?
You Have a Choice – Act!
Posted by Tim in book riffs, selection on 31 October 2011
There are plenty of excuses for not innovating – for not taking steps to change things. However, if you see a way to make things better and you don’t do anything, then you’re letting your situation control you. If you’re dissatisfied with the situation, you have to change the way you act.
Here is the way that Tom Peters puts it in a post from Innovation Excellence:
I believe there is one and only one source of innovation – pissed off people.
(If you go to the link, you can see a good video from Peters, but I had to take it out of this post since it only autoplays, which is annoying.)
But in addition to being pissed off, you actually have to take action. Here is how Gary Cox frames it in his book How to Be an Existentialist:
Existentialism holds that you can only truly change the way you think and feel about your life by acting differently, by acting rather simply reacting, by asserting your will rather than simply allowing yourself to be swept along by circumstances, by always taking responsibility for yourself and what you do.
When there’s a gap between where we are and where we want to be, we need to innovate.
These are some of the issues that I was thinking about when I did an interview for Brian Driggs’ excellent Distillery series – here is what I said there:
If you could distill everything you’ve learned so far into a single word of advice to yourself, what would that one word be?
Impact
Why does this one word mean so much to you?
For much of my life “think” has been more important than “act”, and that’s been a source of weakness for me. So I constantly remind myself that to get anything done, I have to act. One way to remind myself to do this is to focus on having an impact – on people and on events.
How does this one word impact what you do (or want to do) with your life?
It guides how I interact with people. On my best days, I remember that I’m trying to have a positive impact on everyone with whom I interact. On less good days, that slips down the priority list, but I try to keep it as an objective as much as I can.
What has this word done for you so far?
It’s helped me figure out what things I should and shouldn’t be doing. There are always more opportunities than time, so filtering is really important. Sorting based on impact can be useful at even the very micro level – it’s what helps me in the evenings when I know that writing a blog post does more good than watching television (at least, I hope it does!). It also helps me make decisions about what jobs I should have, and what projects I should be doing. That said, it took me nearly fifteen years in the workforce before I even started to get this right. But what I’ve found is that the more I focus on the impact that I want to have, the better my decisions get.
Here’s another way of approaching it, also from Gary Cox’s book:
If a person really did live each as though it were his last he would spend each day panicking while partying and rapidly reduce himself to a nervous, drunken, insolvent wreck. Nonetheless, a person should live his life recognizing that each moment, each day, is precious and utterly irreplaceable.
If each moment and day is precious and utterly irreplaceable, then there’s really only one choice: act!
Succeed by Failing
“If you want to succeed, double your failure rate.”
-Thomas Watson, IBM
That’s a pretty succinct way to say make a point that I was trying to get a couple of weeks ago.
The key point here is that you can fail at different levels. I’ve talked before about a taxonomy of economic failure. We can actually think of failure as a hierarchy that looks something like this:
- System failure (the collapse of communism)
- System component failure (stock market crashes)
- Major firm failure (Enron going out of business)
- Start-up failure (pets.com going out of business)
- Product failure (New Coke tanking)
- Idea failure (Apple Navigator prototyped but never launched)
As you go down that list, failure gets less expensive. When I talk about tolerating failure, I’m talking about trying to set up systems that encourage cheap fast failure. This is usually at the level of ideas.
I think that this is the point that Watson was making as well. He’s not advocating big, expensive, public failure. He was advocating quick, cheap experiments.
We need to push our failures down that list, so that we are testing ideas and finding the ones that don’t work when they are still ideas, rather than things. One of the key skills in this is prototyping – figuring out a small-scale way to test your idea.
As Diego Rodriguez says, anything can be prototyped, and you can prototype with anything.
(photo from flickr/polapix under a Creative Commons License)
Innovation as an Evolutionary Process
Posted by Tim in innovation strategy, replication, selection, variety on 13 June 2010
Here’s another clip from the video series that we did a couple of years ago for our Innovation Leadership course. This time it’s John talking about how innovation is an evolutionary process:
Generic evolutionary processes have three parts – generation of variety, selection, and replication. This maps on to the three steps in the innovation value chain. The Innovation Value Chain also has three steps – idea generation, idea selection and execution, and idea diffusion. The connections between the two models should be fairly apparent!
Innovation as evolution has some interesting implications, including:
- The ideas that spread are often not optimal solutions to problems, they simply happen to be the best solutions currently available. In other words, our innovations just have to be good enough, not perfect.
- Consequently, the idea that we’re not looking for a perfect execution of our new ideas is a strong argument in favour of taking a build, launch, tweak approach to getting our new ideas out there. We’re most likely to get to the best solutions to the problems we are interested in through an iterative process, rather than through pure development.
- This leads to the last point, which is that the evolution of our great ideas is built on collaborative networks. The sooner we can enlist the help of our network (customers, partners, suppliers, etc.), the more likely we are to come up with the best version of our great new idea.
The economy is an evolving system. Thinking of it in this way gives us some important insights into how to best manage innovation.
The Changing Innovation Process
Posted by Tim in innovation, selection, variety on 10 June 2010
How has the internet changed the innovation process? It has had a number of impacts, particularly on collaborative innovation, which is becoming increasingly important. Here is a short discussion on this topic from one of our previous Innovation Leadership Executive Education courses:
George Dyson has a nice metaphor for the changes involved in answer to one of the big questions from edge.org – how has the internet changed the way you think? (via Simon Bostock’s excellent blog)
KAYAKS vs CANOES
In the North Pacific ocean, there were two approaches to boatbuilding. The Aleuts (and their kayak-building relatives) lived on barren, treeless islands and built their vessels by piecing together skeletal frameworks from fragments of beach-combed wood. The Tlingit (and their dugout canoe-building relatives) built their vessels by selecting entire trees out of the rainforest and removing wood until there was nothing left but a canoe.
The Aleut and the Tlingit achieved similar results — maximum boat / minimum material — by opposite means. The flood of information unleashed by the Internet has produced a similar cultural split. We used to be kayak builders, collecting all available fragments of information to assemble the framework that kept us afloat. Now, we have to learn to become dugout-canoe builders, discarding unneccessary information to reveal the shape of knowledge hidden within.
I was a hardened kayak builder, trained to collect every available stick. I resent having to learn the new skills. But those who don’t will be left paddling logs, not canoes.
This same process drives the shift towards distributed innovation. When the raw materials (great ideas) for innovation are relatively rare, it makes sense to try to control the source (creative people) as much as possible. So you hire as many innovative people as you can, and you retain all the resources inside of your firm.
However, when the raw materials are abundant, the problem isn’t finding them, it’s figuring out which ones are good. In this environment, it makes more sense to let idea generation come from anywhere, while you focus on getting very good at selecting and executing great ideas.
All of us are building canoes now.
Finding the Best Way to Fail
Posted by Tim in filter, innovation strategy, selection on 14 March 2010
Nancy and I were talking about a kind of strange newspaper article that her sister sent her discussing the upcoming release of the DSM-V (the official diagnostic manual for mental illnesses). The author of the article was a psychiatrist advocating going back to the 19th century definition of depression – melancholia. I joked that we might as well go back to using phrenology.
If you’re not familiar with it, phrenology was a diagnostic system [sic] based on the idea that the bumps on your head could tell you something about the brain structures underneath the skull. The theory goes on to suggest that the different brain structures reflect different personality traits. As a science, phrenology was discredited a long time ago – around the same time we stopped talking about “melancholia”.
But Nancy had a fascinating response to my joke about phrenology. She said (approximately):
Phrenology was actually really important because it was the first time that people started thinking about the localisation in the brain. Before that, they thought of it as a pretty undifferentiated organ – like a kidney – where each part did the same thing. So phrenology was actually one of the first steps towards modern neuroscience.
That reminded me of a great post by Randy Haykin about the Apple Navigator (which I talked about earlier here). Haykin talks about how many of the key features of the Apple iPad were first introduced in the Apple Navigator – a prototype from 1987 that never launched as a product.
The stories of phrenology and the Navigator show how both science and the economy are evolutionary processes. They both build on earlier ideas to create new ones – usually through creating new combinations. Phrenology failed as a scientific theory, and the Navigator failed as a product, but both contained ideas that could be combined with others to form new, better ideas. We learned from the failures.
That’s why a lot of people, including me, advocate developing a tolerance of failure when we’re innovating. Failure gives us a chance to learn, and it helps us execute ideas that might form building blocks of better ideas in the future. If at least some of our ideas aren’t failing, we’re not trying out enough new things.
However, failure also has consequences – something that venture capitalist Mark Suster forcefully points out in Why the ‘Fail Fast’ Mantra Needs to Fail. His key point is that when fast failure is encouraged, it can have several major drawbacks for start-ups. It can encourage poor business model development, premature abandonment of start-ups, and a cavalier attitude towards the money that others have sunk into the venture.
All of these are valid points. But I think it shows that we are using ‘fast failure’ to cover many different things. One of the key quotes in Suster’s post is this:
You want to talk about the ultimate “fail fast” – how about if you fail before you’ve spent any money building product because you validate there isn’t a big enough market or you can’t make money?
This got me thinking. I think that what we need is a taxonomy of economic failure. We can actually think of failure as a hierarchy that looks something like this:
- System failure (the collapse of communism)
- System component failure (stock market crashes)
- Major firm failure (Enron going out of business)
- Start-up failure (pets.com going out of business)
- Product failure (New Coke tanking)
- Idea failure (Apple Navigator prototyped but never launched)
As you go down that list, failure gets less expensive. When I talk about tolerating failure, I’m talking about trying to set up systems that encourage cheap fast failure. This is usually at the level of ideas. I agree with Suster that encouraging failure at higher levels can be irresponsible.
Innovation courts failure. Not every great new idea will work – and since it is nearly impossible to tell in advance which ones will work and which ones won’t, we have to find cheap, quick ways to test them out. This can be done through the use of experiment as in rapid prototyping combined with iteration based on feedback, through the use of modelling or other simulations, or through the use of a screening tool like the stage-gate process.
The main point is that we need to try to encourage failure before new ideas get too embedded into the economic network. At the top level of the failure hierarchy, failure causes enormous disruption and pain, because those parts of the system are so deeply interconnected. It is much better for ideas to fail than it is for products, firms or economic systems to do so.
(photo from flickr/evansville under a Creative Commons License)
How to Fail at Innovation
Posted by Tim in evolving economic entities, filter, selection on 25 February 2010
The way to fail at innovation is to try to avoid failing.
The idea of failure has popped up quite a bit this week for some reason. Innovation is filled with tensions that we have to become comfortable with if we’re going to succeed. One of the big tensions is between success and failure – when you’re innovating, you can’t have one without the other. In a very interesting post, Arne van Oosterom suggests that this is good argument for emphasising adaptability rather than innovation for many firms, as this eliminates the discomfort caused by the tension between the two.
I am in complete agreement with van Oosterom that adaptability is a desirable trait for organisations to develop. But in doing so, I don’t think we can abandon innovation. I think that we need to develop strategies for dealing with failure.
This was the conclusion reached by both Peter Yates (ex-CEO of PBL, among other things) and Patricia Cross (Non-Executive Director of Wesfarmers and numerous other organisations) in their talks at the Leaders’ Edge Luncheon here in Brisbane on Tuesday. The topic of the talks was ‘Tales from the Corporate Battlefield’ – and it sounds like both of them have been in plenty of battles. And one of the common themes that they touched on is that if you’re doing anything worthwhile, you will experience failures. It’s not fun, it’s not something to be embraced, but it’s inevitable.
This theme was also addressed by Hutch Carpenter in a fantastic post this morning. In making the point that innovative firms will fail, he included this picture:
He includes this quote from Jeffrey Phillips – one of the best innovation bloggers around:
As Edison and countless others have demonstrated, you rarely get it right the first time, and if you are stymied by early failure, then you’ll never find and implement the best ideas. Innovation, as has been pointed out by individuals with far more to say about it than me, will create some failures. Your job isn’t to avoid the failures, since you can’t predict them in advance, but to reduce the cost and impact of the inevitable failures. In other words, keep moving.
So there’s the contradiction that we have to deal with – if we’re going to successfully innovate, we have to fail. The key is to figure out how to do it as cheaply as possible. As I’ve said before, if everything that you try works, then you’re not trying enough things. These contradictions are one of the things that makes managing hard, but it’s also one of the things that makes good managers so valuable. Failing isn’t fun, and it’s natural to try to avoid it. However, it is a necessary element of success.
In other words, the one guaranteed way to fail at innovation is to try to avoid failing.
Filtering, Crowdsourcing and Innovation
Posted by Tim in book riffs, filter, innovation strategy, selection on 1 February 2010
How can we take advantage of the ‘wisdom of crowds’ in our innovation efforts? There are some distinct challenges in trying to do this. The basic idea is this: if you get a large number of people to estimate something – the weight of an ox, or the number of jellybeans in a jar, for example – usually the average of all of the estimates is closer to the actual number than any individual’s guess. Consequently, there is a strong argument for taking advantage of this phenomenon if you are trying to get a handle on estimating a particular number. Businesses have used these techniques to improve their sales forecasting for example (Gary Hamel includes a really nice example of how Best Buy used this method in The Future of Management).
Can this work to improve innovation? It’s not as obvious that it will. I’m currently reading You Are Not a Gadget by Jaron Lanier (more on this book in a later post). Lanier has this to say about using crowds:
The reason the collective can be valuable is precisely that its peaks of intelligence and stupidity are not the same as the ones usually displayed by individuals.
What makes a market work, for instance, is the marriage of collective and individual intelligence. A marketplace can’t exist only on the basis of having prices determined by competition. It also needs entrepreneurs to come up with the products that are competing in the first place.
Since the internet makes crowds more accessible, it would be beneficial to have a wide-ranging, clear set of rules explaining when the wisdom of crowds is likely to produce meaningful results… Among other safeguards, I would add that a crowd should never be allowed to frame its own questions, and its answers should never be more complicated than a single number of multiple choice answer.
Crowds can be useful, but also dangerous. Nassim Nicholas Taleb says that crowdsourcing should be avoided in situations where the potential payoffs are very complex, and when we don’t know what the outcome probability distribution looks like. Unfortunately, this is precisely the case for most innovations.
Relying on crowds can lead to innovation problems. Stefan Lindegaard identifies this as one of the common causes of open innovation failure (the comments on that post are worth reading too):
Many companies start off with idea generation platforms hoping that external contributors will contribute with great ideas and/or technologies. Most do not deliver on the expectations as they get more trash than gold.
And in a post that addresses some of the issues with crowdsourcing really nicely, Graham Horton says:
In conclusion, customer idea portals as they are currently popularly advocated will produce limited results; they will only provide suggestions for solutions that are apparent to customers, given their level of expertise and self-knowledge.
All this might suggest that we can’t use crowds to help innovation. However, I think that these two quotes suggest a possible way that we can still take advantage of crowds in our innovation efforts. One of the issues is that we often misunderstand how crowdsourcing actually works. The Lindegaard quote suggests that people think that we can turn to our crowd (customers, stakeholders, etc.) and just wait for the good ideas to roll in. This is in line with a common understanding of crowdsourced systems – people often talk about Linux, for example, as a process where thousands of people write bug fixes for the software, and all of these fixes get put into the program, making it better. This misses a critical step.
That’s a diagram that I made last year to explain to some friends how icanhascheezburger.com works – but it explains Linux just as well as it explains lolcats. The critical step in the process is the middle one. Both systems crowdsource content – Linux crowdsources code, icanhascheezburger crowdsources cat drawings. The problem is, not all of the code works, and not all of the lolcats produce lols. In each case, there is a small group that filters the incoming content. We don’t have crowds creating stuff, and then voting on stuff. We have crowds creating stuff that answers questions posed by the group guiding the process. The answers that work are then selected by that group as well.
This leads to the answer that both Lindegaard and Horton suggest: in order to get useful answers from crowds, we have to have good internal capacity ourselves. Crowdsourcing needs to be guided. To use the crowd in innovation, we need to set the questions. And we need to know enough to be able to figure out when the crowd is giving us good answers.
A while ago I talked about using jams to select ideas. This process follows these principles. The questions being asked are set by the organisation, so the crowd is trying to address a specific problem. And the best answers are not just judged by popularity – there are several evaluation mechanisms that can be used. You can use the votes and go with the most popular. You can use the ideas that were most polarizing. You can take the ideas that are generated and plug them into whatever other system you use (stage/gate, gut feel, whatever).
Crowdsourcing then is another tool that we can use in our aggregate, filter and connect strategies. In this case, the filtering is the critical step. If we don’t filter correctly, crowdsourcing simply aggregates, which by itself doesn’t help us much. And the aggregated crowdsourced answers need to be connected to questions that we know are important. Crowdsourcing is not a panacea, but it can be a useful innovation tool if we use it correctly.
Graham Horton has written a terrific post that looks at which questions we should ask the crowd.
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Tim was named #5 in the Top 40 Innovation Bloggers in 2011 at Innovation Excellence.
Tim and John were both named in the Top 40 Innovation Bloggers in 2010 at Innovation Excellence.
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