Innovation for the Long Term
Posted by Tim in innovation strategy, time on 9 February 2010
We had some rain here over the weekend. Here is what was reported in yesterday’s Australian Financial Review (emphasis added):
Heavy rains fell across NSW and south-east Queensland over the weekend, which flooded homes and roads, brought down trees and caused motor accidents. But in good news for Sydney, 115 millimetres of rain fell across the Warragamba catchment which could result in dam levels rising over the coming days. The rainfall is ill-timed for the NSW government as it coincides with the switching-on of a $2 billion desalination plant, which will run regardless of dam levels for the next two years as it undergoes engineering testing. Nevertheless, dam levels remain at 50 per cent of capacity and this is the first significant rainfall over the Sydney catchment since mid-2007…
I am still astonished by the logic in the part that I highlighted. It is basically saying this: Australia has been in a country-wide drought for 7 of the past 8 years; Sydney hasn’t had any significant rain in 2.5 years; the dams have fallen below 50%; but the $2 billion investment in a desalination plant might look bad because it rained over the weekend. This is lunacy.
The desal plant may or may not be a good idea. Whether it is or not will depend on a number of factors – projected rainfall over the coming years, projected population trends for the Sydney region, and trends in water use/conservation. Many of the assumptions that underpin a model of the impact of a plant will be debatable. Nevertheless, there is simply no way to judge the wisdom of a $2 billion investment based on whether it rained this week or not. This is the worst kind of short-term thinking.
We see the same thing happen in business here. Every time there is a down-turn in the price of iron, multiple mining projects are put on hold. To me, this doesn’t make much more sense than judging the merits of a desal plant based on last week’s rainfall. According to some talks I’ve had with folks in mining, the thing that is happening here is that their due diligence systems require them to calculate the net present value of projects based on current prices. So when prices are high, they build like crazy, and when prices drop, everything stops. It seems to me we’d be better off looking at expected long term prices of whatever they’re digging. But then, the miners are all making a ton more money than me, so maybe I’m missing something…
This is also an innovation problem. When the global financial crisis hit at the end of 2008, investment in innovation fell through the floor through pretty much all of 2009. This is, again, crazy – for a couple of reasons. Tom Fishburne illustrates the first reason perfectly (his cartoons are great, and you should check out his site):
He’s drawn it exactly the way that I talk about it in classes – you can’t just turn innovation on and off like a faucet. Innovation takes time, and it needs a process. Short-term thinking combined with NPV hurdles for new ideas is one of the best possible way to ensure that you will never come up with an innovative idea again.
The second reason that it is crazy is that we know that the best way to manage innovation is through the use of some kind of portfolio process. This mixes small bets with big ones, and it is the best way to make sure that we have a constant flow of good new executed ideas. One tool that is very useful for this is the Three Horizons model.
We have talked about this a few times – the very short description is that the first horizon involves implementing innovations that improve your current operations, horizon two innovations are those that extend your current competencies into new, related markets, and horizon three innovations are the ones that will change the nature of your industry.
When we shut off innovation in a downturn, it is usually the longer-term innovation projects that get killed. These are the ones that will ensure that we are still in business in five years, or in ten. It is absolutely critical that we consistently innovate across all three time horizons. Unless our very survival is under threat right now, we can’t afford to stop investment in innovation. Even if it did rain over the weekend.
(Storm photo by flickr/Richard.Fisher under a Creative Commons license)
How to Filter Better
I was at the mall yesterday eating lunch, and I took a moment to listen to the conversations going on around me. They were, without exception, utterly banal. Consequently, I concluded that conversation is a useless tool, and the widespread use of it is nothing more than a symptom of the widespread decline of intellectual discourse, manners, and civilisation as a whole.
Stupid, right? That’s a riff on Clay Shirky’s response to people that complain about the quality of discourse within social networks.
The latest version of that complaint comes from Tom Davenport on the HBR Blog, who asks for us to tweet about something important:
Almost 50 years ago, FCC Commissioner Newton Minow suggested that the then-new medium of television was becoming a “vast wasteland.” One could argue that the same fate is befalling social media.
So here we are again: a promising new medium being used largely for vapid chattering about celebrities. Couldn’t these technologies be used for good?
Here’s an analogy: bird calls. When Nancy and I started birding one of the things that we learned was bird calls. When we’re actively birding, we’re pretty alert to everything, because we want to know where the birds are. However, when we’re working at home, we pay less attention to the calls. And since we’ve got a yard that is pretty attractive to birds, there are a lot of calls.
The vast majority of bird calls don’t contain too much information. The most common form of call is what ornithologists refer to as ‘chip calls’, which are short, brief calls that birds use to let other birds know where they are. It can help them find food (lots of chips in one area), it can keep them from straying from the flock (most of the chips moving further away), and it simply be a Horton-Hears-a-Who type ‘hear I am’ call or an ‘everything’s fine’ call. When we’re working at home, we don’t pay any attention to these calls at all.
However, the birds also have a few calls that contain a whole lot of information. We’re particularly interested in the calls that say ‘predator’. That usually means there’s something pretty interesting in the yard, like a Goshawk, or this Carpet Python:
We only got to see that snake because we heard the bird calls, followed them, and found the snake.
So we’re filtering the bird calls in our heads all the time, waiting to find the interesting bits. We need to do the same thing with conversations (well, maybe not the conversations at the mall food courts…), and social networks like twitter. Most of the words used in these media are like birds’ chips. We need to find the words that contain data. How can we do this?
Howard Rheingold is in the middle of putting together of series of tutorials about Mindful Infotention that explain a few different methods that work.
These are fantastic videos that are well worth your time. In the one above, Rheingold makes several important points. He demonstrates the use of several different tools for aggregating and filtering information on a topic that is of interest to you. He has step-by-step instructions that make it pretty easy to track down specific information from streams that are on average not very useful.
Rheingold also talks about two different forms of filtering – algorithmic and personal. He correctly recommends using a combination of the two. Algorithmic filtering includes tools like PostRank, which uses a formula to determine which posts are most relevant to your search. Personal filtering takes place on sites like delicious and diigo, where you can find the pages that the highest number of people have bookmarked, or the pages that specific people have saved. There’s also using your network to help filter.
Rheingold makes an absolutely critical point at the end of the video – it’s not enough to simply aggregate information, and filter it so that you have only the most relevant information. You also have to do something with it! The information is no good unless you connect it up:
The important part, as I stressed at the beginning, is in your head. It really doesn’t do any good to multiply the amount of information flowing in, and even filtering that information so that only the best gets to you, if you don’t have a mental cognitive and social strategy for how you’re going to deploy your attention.
There is a wealth of great information being shared on a constant basis within social media and elsewhere. None of it does us any good at all if we can’t find the bits that are useful to us in solving the problems on which we’re interested in working. But if we filter well, we’ll discover that we actually are tweeting about things that are important.
(I’ll also just note that Howard is currently getting treated for cancer – the prognosis sounds encouraging, and I hope that he pulls through it in good shape.)
Innovation Lessons from The Checklist Manifesto
Posted by Tim in book reviews, filter, innovation on 7 February 2010
How do we deal with complexity? A while ago I suggested that one strategy that we use to handle complexity is that we outsource some of the rote memorisation of facts and routines that we need regularly. This is essentially the strategy that Atul Gawande also advocates in his outstanding book The Checklist Manifesto: How to Get Things Right. The primary theme of the book is that one of the best tools we can use to handle complex situations is a very simple one – the checklist.
The key story in the book is the World Health Organization’s development of a Safe Surgery Checklist. The checklist consists of a series of items to confirm as part of the surgery process. The are all fairly basic things, and they are put into three groups that are asked at three natural pause points in the surgery – before the anaesthetic is administered, before the first cut is made, and before the patient is taken from the operating room. They address very basic issues that you would think wouldn’t be missed, but which often are: confirm that patient’s name, confirm which side of the body the surgery will take place, confirm the procedure, ensure that all surgery team members have introduced themselves by name and role, discuss key concerns for patient recovery, and several others.
After the checklist was developed, WHO tested in eight hosptials around the world – four were in developed countries, and four were in developing countries. They ran the gamut from well-funded hospitals with all mod cons to rural hospitals that had so few resources that they had to sterilize and re-use surgical gloves until they wore out. In the test study, they gathered data from about 4,000 surgery patients across all eight hospitals for three months, then they introduced the checklists into the surgical procedures, and gathered data from another 4,000 operations.
The results are astonishing. Major post-surgical complications fell 36% with the use of the checklist, deaths were down 47%. All eight hospitals saw major reductions in both categories, and from a statistics standpoint, the relationships are statistically significant. The outcomes were published in the New England Journal of Medicine at the start of 2009.
Gawande is a terrific writer, and I recommend the book highly (his earlier books Complications and Better are also great). I think there are several things that we can learn about innovation from this story, including:
- Behavioural innovation is at least as important as product innovation – probably moreso. Drug companies make a pretty big deal out of new discoveries that have statistically significant impacts of 1 or 2% on their target – what would happen if they had a drug that reduced the incidence of an important problem by over 40%? It would be all over the news, and it would be hailed as one of the biggest breakthroughs in medical history. We’ve seen products with even bigger impacts – for example, the polio vaccine. However, the idea that we can reduce the incidence of several major categories of surgical complications simply by acting differently is mind-boggling.
- So why haven’t we heard more about this? People often resist simple solutions to complex problems. Gawande believes that this is because many people think that relying on things like checklists reduces their autonomy, and their ability to act creatively. Gawande’s response:
It’s ludicrous, though, to suppose that checklists are going to do away with the need for courage, wits, and improvisation. The work of medicine is too intricate and individual for that: good clinicians will not be able to dispense with expert audacity. Yet we should also be redy to accept the virtues of regimentation.
Like I said at the start – checklists are great because they allow us to outsource some complexity. This means that we don’t have to think about the basic things, leaving more time and brain resources to deal with the things that genuinely require our skill and judgement.
- We do not interrogate our failures very well. The first step in building an effective checklist is to find the ways that we commonly screw up, look for systematic patterns, and put steps in the checklist to address these common problems. Checklists have been most widely adopted so far in industries where failures are closely examined – flying and construction. When a plane crashes or a building collapses, a lot of effort goes into learning why, and how to prevent the problem in the future. Once we learn the cause of the failure, the checklist helps prevent its reoccurance.
The application of these ideas to innovation is fairly obvious. When you look at innovation as an evolutionary process, it quickly becomes apparent that a number of ideas need to be selected out before we invest too much into them. Most organisations do not spend too much time thinking about the ideas that didn’t work. One of the things that we need to get better at is learning from our ideas that fail. If we do this, then we can build a checklist.
Gawande has a couple of business examples in the book. He talks about three investment firms that have developed checklists to help them evaluate investment opportunities. And he talks about a study by Geoff Smart that looked at what made Venture Capitalists most effective:
Smart specifically studies how such people made their most difficult decision in judging whether to give an entrepreneur money or not. You would think that this would be whether the entrepreneur’s idea is actually a good one. But finding a good idea is apparently not all that hard. Finding an entrepreneur who can execute a good idea is another matter entirely. One needs a person who can take an idea from proposal to reality, work the long hours, build a team, handle the pressures and the setbacks, manage technical and people problems alike, and stick with the effort for years on end without getting distracted or going insane. Such people are rare and extremely hard to spot.
Which VCs are most successful? According to Smart’s study, the ones that use a checklist. Checklists are simple tools that can help us filter ideas and opportunities. They help us outsource complexity, leaving us more mental capacity to connect up ideas in novel ways – which is the key to innovation.
Grit versus Intelligence in Innovation
Posted by Tim in book reviews, innovation strategy, time on 6 February 2010
When I was younger, I placed a high value on intelligence. It made sense, since I was reasonably smart. However, it wasn’t until I added some grit that I started to really get things done. Up until my university years, most things came pretty easily to me. I was fortunate in that I was able to concentrate and learn a lot about things that interested me, so almost by accident there were some things that I ended up building some skills in. But it wasn’t until university that I started having to make the conscious decision to work harder, and do things that weren’t immediately fun if I wanted to get anywhere.
The summer after my first year, I got a job as a floor hand in a feed mill. It was really hard work. For the first couple of weeks, I could barely drag myself home at the end of each day – I felt broken. At the end of those two weeks, my boss Doug called me and the other new floor hand in, and chewed us out pretty comprehensively. The basic message was that we had to work a whole lot harder, or he’d get rid of us.
My first impulse was to tell him to screw himself. I was working harder than I ever had before – I had no idea even where to begin. I went home and talked about it with my Dad. He agreed with me that it didn’t seem fair. But then I started thinking about what it would mean if I quit. What if I didn’t find another job? I had to make money over the summer as part of my scholarship arrangements. After a lot of thought, I went in the next morning and asked Doug for some specific suggestions that I could follow to get better. He gave some, and I discovered that I could indeed work harder than I had been.
That’s when I got some grit. It still took a long time for me to get better at digging in and working at things, but I’m getting there, slowly.
I bring this up because Julien Smith highlighted an excellent article yesterday by Jonah Lehrer in the Boston Globe on the issue of grit versus intelligence. Lehrer starts by discussing a stream of research from psychologists that has shown in many cases, grit accounts for success more than intelligence does. It starts by talking about the story of Isaac Newton and the falling apple:
There is something appealing about such narratives. They reduce the scientific process to a sudden epiphany: There is no sweat or toil, just a new idea, produced by a genius. Everybody knows that things fall – it took Newton to explain why.
Unfortunately, the story of the apple is almost certainly false; Voltaire probably made it up. Even if Newton started thinking about gravity in 1666, it took him years of painstaking work before he understood it. He filled entire vellum notebooks with his scribbles and spent weeks recording the exact movements of a pendulum. (It made, on average, 1,512 ticks per hour.) The discovery of gravity, in other words, wasn’t a flash of insight – it required decades of effort, which is one of the reasons Newton didn’t publish his theory until 1687, in the “Principia.”
I think that this is a critical issue for innovation. We’ve talked a lot about the gaps that are common between inventing something and the idea actually getting embedded in the economy – there are several examples here, and a discussion of Edison and the light bulb also illustrates the point. Randy Haykin talks about another great example by showing how many of the features in the new iPad from Apple originated in the Navigator – a prototype the firm made in 1987.
You have to have intelligence to come up with these great ideas, but you have to have grit to get them to spread. It’s not an either or situation – one way or another, you need both. As Lehrer says, the genius idea is attractive, because it doesn’t require nearly as much effort. But the simple fact of the matter is that to successfully innovate, we need perseverance. Execution is at least as important as ideas, probably more so.
That’s why I think that Ignore Everybody and 39 Other Keys to Creativity by Hugh MacLeod is one of the best innovation books I’ve ever read. He talks about creativity not as inspiration, but as hard work. Here’s an excerpt from one of his 39 other keys ‘Dying Young is Overrated’:
But the kid thinks it’s all about talent; he thinks it’s all about ‘potential’. He underestimates how much time, discipline and stamina also play their part. Sure, there are exceptions. But that is why we like their stories when we’re young. Because they are exceptional stories. And every kid with a guitar or a pen or a paintbrush or an idea for a new business wants to be exceptional. Every kid underestimates his competition, and overestimates his chances. Every kid is a sucker for the idea that there’s a way to make it without having to do the actual hard work.
So the bars of West Hollywood and New York are awash with people throwing their lives away in the desperate hope of finding a shortcut, any shortcut. And a lot of them aren’t even young anymore; their B-plans having been washed away by Vodka & Tonics years ago.
Meanwhile their competition is at home, working their asses off.
Innovation is not about having great ideas. It is about having great ideas, and getting them to spread. You need both grit and intelligence to do this. If you need some more grit, I can put in a good word for you with Doug at the feed mill…
(Feed mill picture (not the one I worked at!) from flickr/rverspirit under a Creative Commons license)
Institutional Innovation
Posted by Tim in complex systems, innovation strategy on 5 February 2010
Here’s a fairly radical idea: if the problem with economic development is that many poorly developed countries have poor institutions, maybe instead of trying to improve their institutions it makes more sense to move the people that live there to a place with better institutions. Let’s break that down a bit.
There is a line of economic research that I find pretty persuasive – it says that the biggest problem in economic development is poor institutions. One of the leading researchers in this area is Dani Rodrik – his book One Economics, Many Recipes: Globalization, Institutions and Economic Growth is the best development economics book that I’ve read. His basic idea is that economic institutions both drive and constrain economic growth, and the the correct mix will vary from country to country based on each nation’s history, culture, economic system, and so on. The research that supports this approach is solid, and to me it makes a lot more sense than the one size fits all approach followed by the IMF, among others.
The second part of the opening paragraph paraphrases some of the recent talks by Paul Romer – another outstanding international economist. His fundamental idea is to try a charter cities approach to develop – the overly simplified one sentence version of that idea is that he wants to create more Hong Kongs.
Here’s a slightly longer explanation from his piece that came out last week in Prospect Magazine:
So, two days later, I opened my own TED talk with a different photo, one of African students doing their homework at night under streetlights. I hoped the image would provoke astonishment rather than guilt or pity—for how could it be that the 100-year-old technology for lighting homes was still not available for the students? I argued that the failure could be traced to weak or wrong rules. The right rules can harness self-interest and use it to reduce poverty. The wrong rules stifle this force or channel it in ways that harm society.
The deeper problem, widely recognised but seldom addressed, is how to free people from bad rules. I floated a provocative idea. Instead of focusing on poor nations and how to change their rules, we should focus on poor people and how they can move somewhere with better rules. One way to do this is with dozens, perhaps hundreds, of new “charter cities,” where developed countries frame the rules and hundreds of millions of poor families could become residents.
It’s a pretty good example of how hard it is for ideas to spread – even good ones like electricity. We’ve already talked about how Edison’s idea for the light bulb didn’t diffuse until he built a generating station and power lines. So it clearly take a fair bit of effort to get ideas to spread. And there are still issues with electricity in some countries. The idea that is gaining momentum in development circles is that it is structural problems that are leading to a lack of development.
Romer argues that the way around this is to stop fighting to change the institutions, but to create new ones in protected regions. I’m not convinced that the idea will work, but I think it is a pretty good example of re-thinking problems that seem intractable to those that are deeply embedded within a system that needs to change (see the discussion from yesterday’s post and the day before’s).
William Easterly has looked at the same ideas about institutions, and advocates an approach quite different from Romer’s. He talks about about the need to take a more bottom-up approach. In his books (The Elusive Quest for Growth and White Man’s Burden), Easterly talks about experimenting with a lot of different development and aid ideas, find the ones that work, and scale those up. It is pretty similar to the idea of algorithmic innovation that I’ve discussed previously.
What does this have to do with you if you’re trying to make innovation work better within your particular organisation – a firm, a university, a government department or whatever? I think economic development is an important issue in and of itself, but there are also a couple of useful ideas for organisations in this. The first is that if you are in one of those organisations that seems highly resistant to innovation, the approaches that Romer and Easterly are using can be taken as blueprints. Both are trying to address the issue of how to get good new ideas to spread within systems that appear to be completely stuck. As I argued yesterday, this is actually a common problem within many organisations.
So one way to attack this problem is to completely reformulate your basic assumptions, like Romer is doing. The advantage to this is that if it works, the payoff might be huge. The risk is that the overall level of risk is also high. This is basically the Apple approach to innovation – trying to reconfigure every market they enter.
A different angle of attack is to unleash a barrage of small experiments, find the ones that work, then scale these up – the Easterly approach. The advantage to this approach is that the cost of failure for each individual experiment is small, and if you try enough, you have a good chance of stumbling across a big idea that will work. Or the cumulative effect of the small institutional innovations might lead to a bigger change. The risk is that you might come up with a number of successful small innovations that fail to change the larger system. This has been the Google approach – their 20% rule for working on your own projects is a classic bottom-up innovation system.
Risks and payoffs – both paths are hard. But both are better than sitting around doing nothing, and better than continuing to try things that demonstrably don’t work. Some ideas to think about at least…
If you’d like to learn more about the Charter Cities idea, there is a good website for it, and Romer’s TED talk is also very good:
New Ideas in Old Systems
Posted by Tim in business models, evolving economic entities, networks on 4 February 2010
The fundamental point that I was trying to make in yesterday’s post is that most of us are facing the same innovation problem: it is extremely difficult to get new ideas to spread within most organisations. We are a bit deceived because we hear about innovation at Google, and 3M, and Apple, and we think that all of our organisations should work like that. Unfortunately, most of them don’t. My examples yesterday came from education, and I know that a lot of people in the public sector think that innovation is unusually hard in their organisations. But nearly everyone resists change. Here are some examples.
First off, here’s ex-Pitney Bowes CEO Mike Critelli on how they faced disruptive innovations:
In 1999, two start-up companies challenged us with online postage solutions. My chief operating officer, Marc Breslawsky, and I were in a minority among the senior team in believing that these companies posed no threat to us. Many employees and high-level executives, one or two board members and many shareholders told me that the world had changed and that I was in danger of ignoring potentially disruptive innovation. The reason Marc and I turned out to be right is that we understood that disruptive technologies are successful only when they are superior to the older technology they replace and when they can be marketed profitably. Neither condition was met.
The blog post discusses how Critelli has consistently had a world view that differed from those around him, and how this made it hard for him to get his ideas across. Many of his examples are cases where he was ultimately right – and in particular, I think that his experiences in changing healthcare are admirable. However, in this case, he wasn’t visionary – he was just lucky (be sure to read Mike’s response to this in the comments!).
Those two conditions are not actually required for disruptive innovations to succeed – especially the first one. Those two conditions are what entrenched incumbents always say when they discount the threat that new challengers pose. As the many studies by Clayton Christenson, Scott Anthony and others show, disruptive innovations are usually technologically inferior when they are introduced. This is precisely why the large firms don’t react – because they correctly perceive that their technology is better. The disruptive innovations change by creating a new market based on different business models, and different value networks.
This misperception of the threat posed by new technologies is one of the reasons that it is often very difficult to introduce innovations within established firms. The fact that P-B’s stock price is now 1/3 what it was when those threats appeared in 1999 suggests that a little more innovation would have proven useful for them.
Here’s another example – Kodak. Simon Waldman has a really nice post on some of the issues that Kodak was grappling with around the same time that Pitney Bowes was thinking about online competitors. He says that they didn’t react to the threat posed by digital cameras because:
* They were distracted by a ferocious price war with Fuji in the late 90s
* They were petrified of cannibalising their film business with digital (further compounding the impact of the Fuji price war)
* They massively underestimated how quickly consumers would ditch film
* Decades of comparable success had made them fat and way, way too happy with themselves
A few months ago, I asked this question to my favourite Swedish PhD student, Christian Sandstrom who has made something of a speciality of creating fabulous Slideshare presentations on the changes in the photographic industry. He responded quickly, but I never posted it here. You can see his answer here.
Here’s the quick summary
* Over aggressive diversification left them burdened with debt and in a weak financial state for dealing with the Fuji price war.
* They put too much focus on ‘hybrid’ solutions – using digital as a way to sell print (eg the Photo CD system)
To me, this sounds a whole lot like the problem that George Siemens is describing in education – they were trapped by their underlying beliefs and ideology. Their fundamental belief was that film would retain its dominance. Digital photos were technologically inferior (especially when they were first introduced), so why would anyone switch from film? Digital cameras took the normal route for disruptive innovations – they found a niche that would value their strength – people that wanted to post pictures on the web. They didn’t care about the poor quality – pictures looked lousy on the web back then anyway. And being able to transfer a digital photo straight to your computer was much easier and much faster than taking a picture, getting it developed, and then scanning it.
Like Pitney-Bowes, Kodak didn’t provide a great environment for innovators back then. Change was being fought hard.
Here’s a third example, going on right now – news. Here’s Felix Salmon arguing that the physical system of producing newspapers is one of the things that is making their transition to digital extremely painful:
Spencer Ackerman uncovers a bit of the hidden point here: newspaper conventions have been built for physical newspapers, and can look silly in the age of the web — especially when the stories themselves appear, pretty much unchanged, on newspapers’ websites. It might make sense for the physical LA Times to run one big story about Afghanistan, but once that decision is made, no one is going to chop that one big story into three smaller ones for the website.
Once again, inertia within the existing system makes it highly resistent to change, as we’ve discussed quite a bit here.
I think that the big difference between the public and private sectors in innovation imperatives is not that the private sector has the profit motive, but rather that occasionally private firms go out of business. It’s not Schumpeter’s “carrot of spectacular reward” that motivates innovation, but the “stick of destitution”. Even with this difference, fighting the inertia within established systems is our fundamental problem – no matter which sector we’re in. It’s hard to get new ideas to spread. That’s our challenge. We’ll keep talking about ways to meet it.
Fighting the System
Posted by Tim in complex systems, networks, replication on 3 February 2010
Today was one of those days when a lot of related ideas just seemed to keep popping up. It started when I read today’s post by George Siemens which discusses the difficulties of changing the educational system. I recommend reading the whole post, but here is part of his argument:
I want to resist the mindset of measuring what is possible by the existing system.
Look at a few of the biggest technological “innovations” of the last decade: learning management systems, student information systems, interactive whiteboards, iclickers, and virtual classrooms. These tools integrate with existing systems, which is why they are successful. The systemic design of education, from curricular planning to delivery to evaluation, has not been recast in light of the web. Instead, the web has been recast in light of existing systems. In many instances, teaching and learning has been transferred to, instead of transformed by, the internet.
What is the impact of this mindset? When I present on alternative views of assessment and accreditation, or suggest non-course approaches to teaching, the inevitable push-back is “well that won’t work because of _____ aspect of the system”.
Perhaps it is time that we turn our attention explicitly to working on, rather than in, the system.
The thing of it is, this is problem is not restricted to the educational system. It is another example of how the embeddedness of ideas makes it difficult for innovative new ideas to spread. I think that the extent to which this is a problem varies along a spectrum. It is an acute problem in education, and in the public sector. However, as I discussed in an earlier post, we see the same thing happen with the introduction of innovative new commercial ideas. Even products that are clearly superior along all dimensions, like the 56k modems versus the 28.8k modems they were designed to replace, innovation is difficult.
John and I were discussing this idea at lunch today, when I realised that another group of people have a similar problem. We are involved with teaching a class called Developing Business From Science. Many of the students in this class are in science-based jobs, and they have an invention or a new idea, and they want to figure out how to make some money with it. Their ideas don’t have any connections to other parts of the economy, and they usually have to displace something that is already economically embedded.
Here’s what I said when discussing the 56k modems:
To get your innovation embedded into the economy, you have to unconnect the members of your value network from whatever they’re currently using (28k modems, for example), and get them to reconnect to you. The unconnecting is a critical step that we often ignore – this is a mistake.
This problem is consistent across all organisations. It’s the problem that George is talking about in education. It’s the problem that innovators in the public service face. It’s the problem that people grapple with in commercial firms, whether their idea is for a new product, a new service, a new way of doing things or a new business model. The hardest part of innovation is getting our ideas to spread.
This idea was then brought home this afternoon, when I read an article by Seymour Papert that Phil Long forwarded to me. It’s called Why School Reform is Impossible, and it is looking at exactly the same issue – how can we revolutionise education when the system swallows every new idea and assimilates it into the existing structure. He concludes with this recommendation:
Complex systems are not made. They evolve. Where I part company from Tyack and Cuban is when they turn from the book’s historical theme of showing that reform will not work to give advice to reformers about how to do it better. My own view is that education activists can be effective in fostering radical change by rejecting the concept of a planned reform and concentrating on creating the obvious conditions for Darwinian evolution: Allow rich diversity to play itself out. Of course, neither of us can prove the other is wrong. That’s what I mean by diversity.
This is very similar to my view. When we’re trying to get new ideas established, we need to experiment, see what works, and do more of that. One of the areas in which we must experiment is that of our basic assumptions. Siemens’ prescription for education will for everyone, I think:
I’m suggesting something much more subtle: that we no longer allow systems-based arguments and criticism to dampen our creative exploration for what is possible in education. A period of “no boundaries” in our thinking. Forget even arguing against those who appeal to integration with existing structures. Just ignore those discussions completely. I’d like to focus instead on creating a compelling vision of what education could be given new technologies and almost global connectivity.
So there it is from two really smart guys plus me: innovation is evolutionary. The way to enact big change is to treat it as an evolutionary process. All of our organisations are operating within complex systems, so this is the approach to use, no matter what industry we’re in. Let’s start experimenting!
Innovations That Last
Posted by Tim in design, innovation strategy, time on 2 February 2010
Here’s another video:
Innovations that Last from Tim Kastelle on Vimeo.
Here’s the brief summary:
Today I wore to work a shirt that I bought in 1994. I’ve worn it a whole lot in the time since I bought it. It was made by Timberland, and it’s a well-made shirt that is still in pretty good shape. Since I bought it, Timberland has become more interested in making shirts that give the appearance of ruggedness rather than providing actual ruggedness. So there are no 16 year shirts for sale from Timberland now.
The shirt contrasts with two pretty cool gadgets I’ve got on my key ring – one is a little pocket knife that folds up and looks like a key, the other is a usb stick that looks like a key. Both are pretty flashy, and it is very convenient to have with me all the time with my keys. However, both have fatal flaws – every time I use the bottle opener on the knife, I cut my fingers, and the usb stick comes with a cap that covers the contacts, which just will not stay on the stick. Now the cap is lost, and if I don’t do something, the contacts will get scraped, and the stick will become unusable.
The shirts and boots that Timberland is making now, and the knife and the usb stick are all examples of poor strategy for the 21st century. They look flashy and they seem innovative, but they’re not built to meet real needs, and they’re not built to last.
I think that we have to focus our innovation efforts on ideas that are more durable. We have to come up with products and services that are sustainable. We have to make shirts that will last 20 years – I know we have the technology for it! We have to make usb sticks that are convenient, but which don’t come with built-in features that will trash them in a short period of time.
In other words, we have to make sure that our innovations take time into account. Whether we know it or not, all innovations have a life span – the way to make them live a long time is to make sure they meet real needs sustainably.
Filtering, Crowdsourcing and Innovation
Posted by Tim in book reviews, 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.
I Have No Idea How the iPad Will Do!
Posted by Tim in business models, innovation strategy, networks, time on 31 January 2010
With all the feverish discussion and prognostication about Apple’s preview of the iPad, I want to be the first person online to make this prediction:
I have absolutely no idea how the iPad will perform.
I’ll go one step further – neither does anyone else. The benefit of making predictions right now is that if you happen to end up being right, you can link back to your post in a few years. If you’re wrong, well, who reads blog posts that are a few years old?
One line of argument that I find really interesting, though, is being taken by people who are arguing that the iPad will revolutionise… something. The argument is by analogy – and what a lot of people are saying in response to critics of the iPad is that people hated the iPod and the iPhone when they were released as well. In particular, the initial response to the iPod introduction was pretty universally tepid.
Garry Tan from Posterous has collected a few of these, and this one pretty well sums them up:
I still can’t believe this! All this hype for something so ridiculous! Who cares about an MP3 player? I want something new! I want them to think differently! Why oh why would they do this?! It’s so wrong! It’s so stupid!
Haha! It wasn’t Apple that was stupid – they were stupid! Right?
Well, maybe. It’s easy now to look at the iPod’s 70%+ market share and wonder how anyone could have missed that it was a game-changing innovation. I’ll tell you how. The fact of the matter is that all the people that were skeptical about the iPod as a product innovation when it was introduced were actually completely correct. There wasn’t much there. Take a look at the iPod sales figures from wikipedia:
The first iPod was introduced at the end of 2001, and you can see that sales figures for the first three years were not good at all. By the middle of 2004, the iPod’s market share had been sitting in the 20-30% range for a while. By the end of 2005, that had shot up to over 70%. What happened?
iTunes happened.
Because the iPod and iTunes are so closely interconnected now, it is easy to forget that iTunes didn’t exist for the first years of the iPod. At the time, the iPod was just another mp3 player. The innovation with the iPod was not in the product – it was the innovation in the product’s value network. It was a similar story with the iPhone. And that is why nearly everyone that is yapping about iPad right now is completely missing the point. Because we don’t know what it’s value network is going to look like yet, and this is what will actually determine whether the iPad will take off quickly like the iPhone did, or slowly like the iPod.
Even when you make great products like Apple, your innovations never stand alone. They work within the context of their economic network. The better you understand this, and the more innovative you are in constructing your value networks, the more successful you’re likely to be.
So the next time someone talks to about all the great new features something has, ignore them. Instead, think about the business model and the value network that will support the great new thing.
Destroyed by excellence
Posted by John in innovation on 29 January 2010
There was a bit of interest in the blog piece that I did on responding to change so I thought I would follow this up with a quick discussion of a really good model for understanding inertia and how resistance to innovation develops.
One of my favorite research studies on excellence and inertia is by Prof. Dorothy Leonard-Barton, who is one of those rare business academics able to do rigorous research and also translate it into useful information for business leaders. Usually we get the situation where the research is unintelligible or the information for business leaders is based upon bad research. As Tim has said in a previous post, there is a real need for business academics to do a better job with filtering business ideas by testing them with good research.
In a research study published in the Strategic Management Journal in 1992, Prof. Leonard-Barton did twenty case studies of new product development within five firms that she later translated into the bestselling book, Wellsprings of Knowledge. Through interviews and observation, she developed a model of core capabilities that shows four interlocking dimensions.
Firms that are really good at doing something (logistics at Wal Mart, for example) will have a combination of employee knowledge and skill (obvious); physical technical systems such as machinery, databases and software; managerial systems such as education, awards and incentives (less obvious); and values and norms such as rituals, status and beliefs, which acts as a powerful knowledge filtering system (often overlooked). Successful innovations in areas where these dimensions are overlapping will reinforce the connections and make further innovations in that particular competency more likely.
The really interesting finding from Leonard-Barton’s work is that the model for inertia is exactly the same as the model for core competencies. A dominance of a particular skill set tends to marginalize people without skills in that area, resulting in one type of legitimate thinking in the business. People become promoted and rewarded for their skills in the dominant technical systems and the cultural effects of status and beliefs effectively screen information from the outside world that might challenge the status quo. This is exactly what I saw in the case study that I did with the Tioxide company and I wonder if I went into News Limited or Fairfax Ltd, would I observe the same factors?
It’s a powerful model because it shows how businesses are most at risk when they are most successful. By using Leonard-Barton’s framework, managers should be able to detect the early warning signs of overconfidence and lock-in to a dominant technology or business model.
So, success sows the seeds of failure but where’s the supporting evidence? Eric Beinhocker quotes a US study which showed that in a sample of 6772 firms from 1974-1997 only 5% of them achieved sustained outperformance for a period of more than 10 years and only 32 of these firms (0.5%) were able to outperform their peers over a twenty year period. The challenge to keep innovating and changing is immense, and most firms won’t succeed. So much for “Built to Last” or “Good to Great”. The cold, hard data tells us a story more like “Condemned to Being Average” but I won’t even try to sell a book with that title.
Finally, here is a positive thought. Failure and disruption presents the best chance to build new core competencies. When we fail, we should really see it as an opportunity to build the next phase for growth.
Perhaps the great evolutionary economist Bob Dylan summed up this paradox best by saying, “There’s no success like failure, and failure’s no success at all”. I wonder if His Bobness has read Beinhocker?
Low Tech Networks
Everything is different now that we’re all knowledge workers, right? The digital world has changed everything… hive mind… singularity… chaos! change! panic! PANIC!
Maybe. Maybe not.
Yesterday I talked about the risks and rewards of low-tech innovation – if we re-think the most basic parts of our value networks, the parts that we take for granted, we can find great opportunities. Then today I read this in Shop Class as Soulcraft by Matthew B. Crawford discussing motorcycle repair (emphasis mine):
You also develop a library of sounds and smells and feels. For example, the backfire of a too-lean fuel mixture is subtly different from an ignition backfire. If the motorcycle is thirty years old, from an obscure maker that went out of business twenty years ago, its proclivities are known mostly through lore. It would probably be impossible to do such work in isolation, without access to a collective historical memory; you have to be embedded in a community of mechanic-antiquarians.
In all this talk of digital transformations, it is easy to forget that we are talking about systems and processes that have been around for a long time. A lot of the digital things that seem new to us now are simply new in digital form, not in general.
Crawford’s example shows how no matter how low- or high-tech our profession, we still depend on our network for storing, filtering and finding information – the extended brain works in all fields. And it is this network that creates value, that generates ideas, that innovates.
All of our economic and intellectual activity takes place within networks. The ones in which we’re embedded play a substantial role in what we are able to accomplish as individuals. It doesn’t matter if we’re twittering, developing a scientific theory in the 19th century, fixing motorcycles, writing a PhD, figuring out a new way to our job, or just thinking about something. Our networks help us create ideas, and they help us spread those ideas. They even help us craft those ideas. The better we know our networks, the more effective our ideas will be. That’s how we deal with the challenges of the digital age – through our networks.
(photo from flickr/zen under a Creative Commons license)
Low Tech Innovation
Posted by Tim in book reviews, business models, innovation strategy on 28 January 2010
At start of my innovation courses, students often think that if their organisation isn’t inventing iPads, then they clearly aren’t (and can’t be) innovative. I end up spending a lot of time trying to help them see the many opportunities available for innovation, even within industries that appear to be pretty tightly constrained. In many cases, innovating in these industries ends up being far more important than coming up with flashy new gadgets. If you’re one of the first people to get one of the new iPads, and it fails miserably, will your life be materially worse than it is right now? Probably not.
Andrew Hargadon recounts an interesting story of failed low-tech innovation: construction companies in Florida introduced a new form of drywall, which has subsequently been found to be defective. The problem may affect as many as 100,000 homes, and the cost of replacing the drywall in all of them may run as high as $10 billion. Who pays is a bit of question too.
The point that Hargadon makes is that innovation in a very low-tech field like construction materials can actually have much higher stakes attached to it than innovation in gadgets. That is one of the reasons that these industries are very un-innovative – the cost of getting things wrong is pretty high.
Jeffrey Phillips, author of one of the best innovation blogs around, points out another reason that conservative industries don’t innovate – they often are heavily regulated. According to Phillips:
Too often, the regulations become a “ceiling” for new products and services. Rather than dream up new products and services that customers need, then try to revise the regulations to fit those products, firms use the regulations as a hard and fast rule, never to be breached or violated. They are in a box of their own making and own choosing, and careful never to question the box. Again, disrupters are going to seek ways to make that box obsolete, and the interesting thing about most regulated firms is that they employ lobbyists, whose job it is to influence or change regulations. A truly innovative firm would identify products and services that met customer needs, then lobby for the changes necessary to implement those products, and force the rest of the industry to follow.
Highly regulated industries are also often low-tech as well. The disenctives to innovation in these industries are substantial: the cost of getting things wrong is often enormously high; the stakes are much higher; regulations may make it difficult to bring in new ideas. So why try to innovate at all in these industries?
Phillips’ post starts to get at the reason – the risks are high, but the potential payoffs are also huge. Here’s an example:
There’s an excellent book on the history of shipping containers – it’s called The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger by Marc Levinson. The impact of containers has been gigantic. When they were introduced, they reduced shipping costs by over 60%. They quickly reduced the amount of labour needed to load and unload ships by over 95%! The first shipping containers were made in the 1920s, but Malcom McLean and his company McLean Industries did what Phillips suggests – he identified the customer need, and then fought regulations until containers went into widespread use in the mid 1950s.
A shipping container is about as low as low-tech gets. The container is one of the primary drivers of the huge increase in international trade from the end of World War II to now – it’s impact has been greater than that of all the high tech gear that gets shipped around, greater than that of the WTO, greater than that of all of the international trade treaties that have been signed.
What does this tell us? A couple of things:
- It’s another great example of the difference between invention and innovation. The box itself was invented 30 years before “containerised shipping” actually got the idea to spread. It’s not enough to have the ideas, or even to show that they work – you have to get your ideas to spread.
- Following directly from that, the big innovation isn’t in the low-tech shipping container, the innovation is in the business model built around the low-tech shipping container. The new business model includes the integration between the container, ships, trucks and eventually rail – a completely different value network. Revenue generation is different too – the cost-cutting through labour saving was unbelievable. All aspects of the shipping business model changed as containers became widespread.
- As in the drywall example, changing the most basic part of the system had a substantial knock-on effect. When McLean started with containers, there was little innovation in the industry. It was highly regulated, and very comfortable – so people weren’t trying many new things. Almost all of the innovative focus was on the high-tech end – making faster ships, increasing the capacity of trucks, and so on. However, all of these innovations only introduced marginal time savings – the bottleneck was still on the docks. The simple container is the innovation that got around that problem.
So here’s a question for you: what low-tech innovation opportunities are available to you? Particularly if you are in an industry with constraints, low-tech is probably the way to go. The challenge for the day is this – find the most basic part of your business model, and start thinking about innovations around that. This will often get you rethinking your entire business model. That’s what makes the stakes high, but it’s also what makes the potential payoffs high as well.
(Photo from flickr/photohome_uk under a Creative Commons license)
Filtering With Your Network
Posted by Tim in complex systems, filter, innovation strategy, networks on 27 January 2010
In yesterday’s post on Personal Aggregate, Filter & Connect Strategies, I didn’t have room for one key point: one of the key filters to use is your network. When he was in Brisbane last month, George Siemans gave a talk with an example that illustrated this perfectly.
For the past couple of years, he has run a course on Connectivism with Stephen Downes. Here is the definition of connectivism from Downes:
At its heart, connectivism is the thesis that knowledge is distributed across a network of connections, and therefore that learning consists of the ability to construct and traverse those networks.
As I understand it, one of the points of the course is to present students with so much data that they can’t possibly process or understand all of it as individuals. This forces them to create networks to build data-gathering and sense-making networks in order to succeed. There are more details about networks, connectivism and the course in this excellent presentation from Downes (the presentation also discusses Downes’ framework for building knowledge within complex networks, which consists of Aggregate – Remix – Repurpose – Feed Forward).
So as individuals, our network is part of our filtering system. This also points out how the three processes – aggregating, filtering and connecting – interact with each other. In the example of my twitter feed, I discussed this as part of my aggregation strategy. But at the same time, I’m actually counting on people within my network to filter. They’re not sending every little thing that they run across into their twitter streams – they are selecting.
This same process happens as part of the aggregate, filter and connect process for organisations. We can use our networks as aggregating/filtering tools. This is what is happening in customer-led innovation, crowd-sourcing and open innovation. We use our network to increase the flow of ideas into our organisation (aggregate), and we also count on our network to decide which things they run across might be important (filter).
And just as for individuals, these processes don’t work well for firms either unless they are good at all three steps. If you only aggregate, you get overwhelmed with ideas. You need some form of selection process. Both forms of connecting are important too. You must be able to connect ideas in novel ways – this is one of the central skills in innovation. If you’re not generating and executing your own innovative ideas, you run into several problems. Open innovation won’t work, because you’re not bringing anything of value to the table – so why would anyone want to partner with you? Customer-led innovation and crowdsourcing won’t work either, because the skills need to tell which ideas are worth pursuing – your filtering is worse if you’re bad at connecting ideas.
Outbound connecting is also critical – this is how we get ideas to spread. In the case of firms, getting ideas to spread is a critical part of innovatgion diffusion. This is also a network function. Using our networks to help with filtering is essential – both for individuals and for firms.
This is one of the reasons that we are doing research on networks. Knowledge creation within firms is also a network function. John and I recently made a video to use to explain network analysis and our main research project to people that are participating in our studies. Because many of them are in remote locations, we can’t visit everyone. And we figured that showing them a video might help them feel more of a connection with us than simply sending them a document with the same information. Take a look at this, and keep in mind the value of your network in executing aggregate, filter and connect strategies:
UQBS Innovation Networks in Project-Based Firms Information Video from Tim Kastelle on Vimeo.
Personal Aggregate, Filter & Connect Strategies
A while back my PhD student Sam and I were talking, and he asked me about my RSS feed. His question was something along the lines of ‘what blogs would I have to read if I wanted to be able to make the connections that you do on your blog?’ As we talked, I realised that it didn’t matter if I gave anyone else my exact RSS feed, they wouldn’t be able to replicate my blog – and the reason for this is aggregate, filter and connect.
When I first thought about aggregate, filter and connect as a framework, it was in an attempt to explain why Amazon’s business model worked better than that of other online bookstores. The first time I talked about it in public, it was to explain how open education might work. I’ve been working on making it in to a general model of how we create something unique when we’re primarily dealing with information.
As such, it can be used to explain business models, like Amazon’s, or blogs, like mine. The more I’ve talked about the model, the more other people are picking it up, which is great. Some of these recent discusssions have gotten me thinking about how aggregate, filter and connect works at a personal level. This was really Sam’s question. I’ve talked about how Charles Darwin basically used an aggregate, filter and connect strategy, Phil Long talks about it as part of personal knowledge management, Harold Jarche has discussed it as both a general model for business and for personal knowledge management (an idea that Jack Vinson picked up, and connected to the concept of enhanced serendipity from Ross Dawson), and Glenn Wiebe used the framework to discuss both Joseph Priestly’s inventions and teaching. So we’re starting to get a bit of discussion Today I’d like to illustrate the concept by discussing how I use it.
Aggregate, filter and connect is a non-linear process, with lots of feedback loops. However, it is unavoidable to talk about it in steps. While I do that, keep in mind that it is all going on at once. Here is how I use the framework to execute ideas in my main area of interest – innovation and networks:
Aggregate: I do a lot of data scanning. The RSS feed that Sam and I were discussing includes 182 blogs. I also follow 306 people on twitter, most of whom usually tweet about things relating to my areas of interest. In addition to that, I finish a book about once every 3 days, and I’ve been doing that for a looooooong time. I also talk to a lot of people, despite being an introvert (see Sacha Chua’s great presentation The Shy Connector to see how that works) – last year had over 80 meetings with people that are practicing innovation management, plus contact with my students, who are nearly all out in the workforce as well. Then there’s the stuff I’ve learned in all the jobs I’ve had. Collectively, this adds up to a fair bit of data.
Filter: This is my weakest area – I don’t outsource nearly enough complexity. I need to get better at taking notes on things I read, in searchable media, so that I don’t do all the filtering in my head. At the moment, I don’t even filter my twitter stream. Ken Gillgren argues that we should be taking in as much data as we can possibly handle, to improve our ability to see patterns and make novel connections. So I’ll say that’s what I’m doing. In addition to my head, I’m also using Evernote, my own tweets, diigo, and my blog as filtering tools. And I’ve used fairly primitive methods like writing reading notes, though that generally hasn’t worked too well for me – I actually find blogging more effective.
Connect: Harold Jarche has been doing some fantastic thinking about this topic recently, and he made this diagram to illustrate the process:
I think this is a nice diagram, which pulls together a lot of the recent discussion on the topic. The one thing that I would like to add to it is this idea: connection works in two related but distinct ways. The first is that we connect ideas to each other. This is the innovative act – as Schumpter said, “(Economic) development in our sense is then defined by the carrying out of new combinations”. This is where I put a lot of effort when I’m coming up with blog posts, with research papers, and even with ideas for consulting jobs. Making novel connections is a skill that I work hard to build.
The second way that connection works is that we connect ideas to people. This is the outbound side of Connection. I use several strategies. When I re-tweet something, I try to make a comment that links the tweet to a broader concept (sometimes a challenge with 140 characters!). I write about the idea connections that I make in my blog – as people read it, they start connecting with the ideas. I give as many public talks as I can – from last September until now I have given more than twice as many public talks as I had in the previous three years combined. In Canberra last week I had a talk with Geoff Garrett, who said “Innovations travel on two legs.” There’s something to be said for that idea – and I have a lot of discussions about my ideas face-to-face – it’s one of the most effective methods of outbound connection.
So that’s a brief summary of how I have been trying to use the Aggregate, Filter and Connect framework over the past few months. In using it, I have learned a few things that might be useful for others too:
- It really helps to think about the three tools explicitly. As I said, I’ve always been reasonably good at making novel connections. But my ability to get my ideas to spread has increased dramatically once I started thinking in this way. Particularly with regard to outbound connections. My use of twitter, and the increase in my public speaking were both ideas that I initiated to increase my connections.
- When people feel overwhelmed by information, it usually means that they aren’t filtering effectively. Like I said, this is my weakest area. But there are some really smart people working on this. In addition to the posts I’ve linked to, check out the rest of Harold Jarche’s blog for some ideas. Venessa Miemis and Ken Gillgren have done some really good thinking in this area too. This is one of the areas in which most of us probably need to improve.
- The other area that we probably need to address is this: we need to get better at connecting ideas. This is where we create value – by making novel connections. And it’s not enough to just make the connections in our head – we have to frame them in a way that others can act upon. That means creating tangible content – a blog, tweets that connect ideas, podcasting, something. My primary recommendation here is to practice making novel connections, and then express them in a way that enables your idea to spread. One good way to do this is to expand the range of areas from which you collect information, and as you read and hear things from outside your area, consciously think about how they connect back to things that you know well. This is the strength of weak ties between ideas.
- Finally, your personal knowledge management scheme isn’t complete until you are doing all three things well. Aggregating is great, but only an initial step. If you don’t filter well, you won’t be able to make sense of the information that you collect. At the same time, even if you aggregate and filter well, you only create real value when you make novel connections between ideas. Information is the fundamental building block of idea connections. Once you make these novel idea connections, you then need outbound people connections to get your ideas to spread. The three skills reinforce each other.
So there’s the answer to Sam – you can replicate my blog by copying my incoming information streams, using the same filtering tools that I do, and then making the same connections between ideas that I do. In other words, you can’t. Aggregate, Filter and Connect is one method you can use to generate unique intellectual value.
NOTE: I’d like to thank everyone I mention in this post, and many others as well for contributing ideas that I’ve been able to use as building blocks in this argument – It’s great that we’ve been able to Connect! George Siemens and Jon Husband have also written things on these topics that have influenced my thinking.
Another NOTE: Venessa has pointed out in the comments that Howard Rheingold has written one of the definitive articles on filtering: Crap Detection 101.
Last NOTE: Follow-up post: Filtering With Your Network
