Archive for August, 2009
good enough
Posted by Tim in evolving economic entities, innovation on 31 August 2009
Do things always get better as they evolve? I touched on this idea recently, and I think the answer is definitely ‘No’. Now Wired has made the point for me again with an interesting article on innovations that are not big jumps forward technologically, but rather simply good enough. The idea is that some products work if they are cheap, flexible and easy to use. The examples they discuss include the flip video camera, mp3s, skype and the predator military plane. Here’s the description of the Flip:
No one understands this better than the folks at Pure Digital Technologies. Two years ago, the Flip Ultra nailed all three of those accessibility traits: It was significantly less expensive than other digital video cameras—so much so, it almost seemed an impulse buy in comparison. It was much easier to use, not only for shooting video but also for uploading clips to the Internet. And its pocketable size and Web-sharing abilities made video available anytime, anywhere. The Flip hit the Good Enough trifecta.
There are a couple of important lessons here for people trying to innovate. One is that we often think that our new ideas have to be perfect. Obviously, in some cases they don’t. Skype is a great example here – when it first started the sound quality was awful. But it was incredibly convenient, and very cheap, and that allowed it to grow. And now a few years later the sound quality has improved all the way up to mostly adequate. And they offer mostly adequate video too!
This leads to the second point – evolution is not a progression up a ladder. It is an exploration of design space looking for things that work. We often forget that – which means that we leave important sections of the design space unexplored, like that of cheap, easy to use video cameras on to which Flip eventually stumbled. Innovations that make existing things work better are important – just look at Toyota. But innovations that jump into a completely new area are the ones that transform industries. And usually, these innovations aren’t perfect right from the word go. Usually, they’re just good enough.
iterations
Posted by Tim in design, innovation, selection, variety on 28 August 2009
Here’s a video from the people that made the iPhone app Convert, showing all of the different versions that they tried:
Convert Design Evolution from tap tap tap on Vimeo.
There are a couple of things worth noting in this. First, they experimented a lot. They generated a ton of variety, all of which would have been pretty cheap. When I keep talking about failure, people often seem to think that it means that we need to launch products that don’t work, when in fact I mean almost exactly the opposite. We need to do what tap tap tap did, and figure out what doesn’t work before we launch. The second big point is that the big change that makes the whole thing actually work well doesn’t come until about 75% of the way through all of their tinkering (at about 1:10 in the video). This shows again that the big changes often don’t come until you start using things.
(Hat tip to Endless Innovation…)
the role of failure
Posted by Tim in book riffs, innovation on 27 August 2009
Atul Gawande from his most recent book, Better:
The third requirement for success is ingenuity – thinking anew. Ingenuity is often misunderstood. It is not a matter of superior intelligence but of character. It demands more than anything a willingness to recognize failure, to not paper over the cracks, and to change. It arises from deliberate, even obsessive, reflection on failure and a constant searching for new solutions.
Exactly.
Even though Gawande writes about medical issues, he does so in a way that imparts a lot of wisdom that can be applied to many endeavours, including innovating.
see more Fail Blog
econophysics to the rescue
Posted by Tim in book riffs, evolving economic entities, time on 26 August 2009
I read Why Stock Markets Crash by Didier Sornette last year, and I thought it was a pretty good book. Sornette builds on the quantitative work of Benoit Mandelbrot to make models of market bubbles using non-linear dynamics. The basic idea is that bubbles are created when the expectations of people in a market become spontaneously synchronised. It has some heavy-duty math to back up this idea, which makes it a less readable book than one might like, but to me Sornette’s models ring true.
One of the features of Sornette’s models is that he believes they include information that can predict the time when a market reaches an inflection point, which may in turn cause a crash. When I first read this, I was fairly skeptical. However, he is doing something that very few orthodox economist are willing to try – he is making public predictions of market crashes – including dates! In collaboration with several other authors, Sornette predicted date of the US housing market crash (roughly), and the date the 2008 oil bubble would burst. Most recently, they predicted a stock market crash in the Shanghai stock market, with the most likely dates being sometime between 17 and 27 August 2009. It didn’t. The market waited to crash until August 4th.
This is actually pretty remarkable. I’ve read several different blog posts discussing this issue, a lot of people are arguing whether or not a 20% drop in value in 2 weeks is a crash or not, and lots of other things. Many others talk about whether or not these are self-fulfilling prophecies. As much as I like econophysics, I’m pretty certain that it’s pretty close to impossible to credibly argue that articles in Physica A are moving the US housing market. Personally, I’m still not convinced that I buy Sornette’s underlying model, but I still think that the general approach has merit. The lesson that I take away from it all is that there is enormous potential value in modeling complex economic systems using non-linear methods. We need to do more of it since when we’re innovating, we are trying to introduce ideas into a complex, non-linear system.
Here’s the article with the heavy-duty math behind the prediction…
Image from an article reporting the story on the arXivBlog, hat tip to Alex Tabarrok)
dominant logic
Posted by Tim in business models on 22 August 2009
One of the key ideas in the business models research is that once a firm develops a successful business model, they tend to replicate it with all of their future innovations. This is Henry Chesbrough’s explanation for why Xerox was unable to successfully launch all the great inventions that came out of their Palo Alto Research Center like the graphical user interface, portable document format (PDFs), ethernet networking, and the mouse. I ran across a good example of dominant logic problems today.
It turns out that Wal-Mart is trying to develop knock-offs of the two most popular flavours of Girl Scout Cookies. This is a pretty bad idea on a number of levels. Bob Sutton describes what’s going on and explains why he thinks it is dumb. The part that jumped out at me though is this:
The brilliance –and the Achilles heel — of Wall-Mart is that they talk and act as if the answer to every problem is to use their scale, bargaining power, and speedy implementation to tackle any problem by driving down the price they pay and pass it along to consumers. This is great, for example, when they use their bargaining power to bring down the cost of environmental friendly LED lights in their refrigerators so that they become cheaper than traditional lights. But when “everyday low prices” is the solution to every problem and — despite lip service to other constraints — almost nothing else drives your behavior even when it hurts you badly (as in this cookie caper), your core cultural values can hurt you badly.
That is dominant logic at work. One of the key ideas in using the business model concept is that you need to customise the business model for each idea that you bring to market. As always, this involves a delicate balancing act (but then, that’s management!) – in this case between the need to customise each business model and the imperative to stick with what you’re good at. Often you’ll find that this poses a problem that can’t be resolved. In that case, you should either drop the idea (which is almost certainly what Wal-Mart should be doing in this case), or find a way to partner with someone that has skills more suited to executing the business model that fits your idea best.
In any case, you definitely need to think these things through, otherwise you end up doing dumb stuff like trying to crush the Girl Scouts like a bug, which isn’t sustainable over the long term…
(photo from flickr/Merelymel13)
the myth of innovative progress?
Posted by Tim in design, evolving economic entities, innovation on 19 August 2009
That’s me wearing my new iPod headphones. Strangely, I caught a fair bit of stick for wearing them today – including a relatively sarcastic ‘nice retro look’. I thought this was particularly interesting in light of a nice post I read this morning called The Myth of Evolutionary Ascent (found via John Wilkins’ blog). The post makes a few key points – that in evolutionary biology, there is no inevitable march towards increasing complexity (contra Kevin Kelly!); “The evolutionary ‘ladder’ may be a valid model for one thing: the history of a single lineage, with height representing nothing more than simply the time axis. Complexity has nothing to do with it”; and that a lot of evolutionary complexity is non-adaptive in that it is discovered as organisms experiment within the design space available to them.
After seeing links to about 10,000 words of blog posts, you may well be ready to ask me what does this have to do with headphones and innovation? And it’s this: even though a lot of technology becomes intentionally more complex, this isn’t necessarily always progress, nor does all the increased complexity always lead to increased functionality. For the second point, just think of word processing software – how many of the extra features that have been added over the past 15+ years do you actually use? There’s a lot of extra complexity there, but the vast majority of us still just type…
So….. headphones. A lot of the recent advances in mobile headphones have been pretty good. The quality of sound in even fairly cheap ones now is pretty good. But not all of the changes are progress – a lot of them are just explorations across all of the available headphone design space. So even though we can get smaller, good-sounding headphones now, they are not ‘higher’ up the headphone hierarchy. They’re just a different design. And even though I can see the appeal of wearing white headphones to signal to everyone that I’ve got a genuine Apple product in my pocket – wait, no, actually I can’t see the appeal to that. The other problem with those headphones is that they won’t stay in my ears – which, for me at least, is one of the key features that I’m looking for in headphones. I don’t hear very much when my slick new iPod headphones are constantly sliding out of my ears. So I’ve travelled across the design space to older-looking headphones, which actually stay on. And I end up listening to my iPod a lot more that way.
So the main point today is that new and more complex products are not always better, nor do they necessarily show progress. The only thing we know for sure is that they are new, and more complex. There are plenty of older designs that may be just as good, if not better. Innovation is important, but new is not by definition better. We judge that by how well the new things meet our needs.
embedded stars
Posted by Tim in innovation, networks on 19 August 2009
Jeffrey Pfeffer has written an interesting post summarising research by Boris Groysberg of Harvard (found via Felix Salmon). The research looks at the impact of banks hiring away star performers from competitors. Here is the description of the research:
Grosyberg studied 1,052 stock analysts who worked for 28 U.S. investment banks over the period 1988 through 1996. He found that when a company hires a star away from another firm, the star’s performance falls (46 percent of the research analysts did poorly in the year they switched jobs and their performance remained lower even after five years), there is a decline in the performance of the group the star joins, the market value of the company hiring the star falls, and the star doesn’t stay with the new employer for very long.
Pfeffer and Salmon go on to discuss this research in terms of what it means for salaries in finance. Pfeffer suggests that the best strategy is to grow your own talent, and Salmon says it means that high salaries in finance are unjustified. I can see the logic in both of their arguments, but I draw a completely different conclusion, based on a related piece of research by Groysberg titled “The Effect of Colleague Quality on Top Performance” (co-written with Linda-Eling Lee). The summary of this piece is:
We show that top performers do not own their performance, even in the knowledge-intensive work performed in this professional business services context. While an individual’s past performance does indicate future performance, the quality of colleagues in one’s organization also significantly affects top performers’ ability to maintain their performance. Specifically, top performers in professional business services rely on high-quality colleagues both to improve the quality of their own work and to deliver it effectively to clients.
In other words, performance depends in large part on the network in which you’re embedded. And I contend that while this is true for financial analysts, it generalises to all economic ideas. The success of an innovation depends in large part on where and how it is embedded within the economy. Why wasn’t Charles Babbage’s Difference Engine the first working computer? In large part because there wasn’t any good way to embed it within the rest of the economy. In particular, there were insufficient machining skills available to make a workable version of Babbage’s plans. The idea and designs were good – we know that because the London Science Museum was able to manufacture a working Difference Engine in 1991, and it did what it was supposed to.
When you have an innovative idea, one of the most important questions to ask is ‘how does this fit into the economy?’ This requires you to think much more broadly – you can’t only consider the technological challenges involved with your idea. The innovations that spread are the ones that build an effective network around themselves.
creative spaces
Posted by Tim in innovation, variety on 16 August 2009
I just want to pick up on a couple of ideas that I raised over the past week. The first was that of scheduling time so that you can pursue innovative activities, and the second talked about intersections between science fiction writing and economics. I thought of both of these things again when I ran across a link from Boing Boing to the ‘Where I Write’ project by Kyle Cassidy, in which Cassidy takes photos of science fiction & fantasy authors in their writing spaces.
Most of the writing spaces are pretty idiosyncratic, like that of Michael Swanwick (above). And then I got to thinking that my office is pretty idiosyncratic too – and I go through all kinds of strange activities when it comes time to write. For me that is a central part of being creative.
The obvious question then is this: if we have to make allowances for creative people to organise their schedules like makers instead of managers, don’t we also have to allow them to organise their personal spaces like writers instead of drones? Do we do this? A lot of times, I don’t think we do. One thing that I’m reasonably sure of though, is that a clean-desk policy does not fit very well with an innovative culture…
What does your creative space look like?
picking winners
Posted by Tim in innovation, selection, variety on 15 August 2009
Now that preseason pro gridiron games have started up again, I’ve been thinking about whether or not I want to play in the game-picking pool I’ve participated in over the past few seasons. I’ve had some interesting results in the pool – I’ve done extremely well in the regular season, but horribly during the playoffs. I know exactly why this is so, but I don’t think I can fix it.
My secret during the regular season is that I’m the only person in the group (usually 15-20 people) who understands that no one can actually pick football games. So I’ve developed an algorithm. It only consists of two rules, which are very simple to apply. Now, instead of agonising over who to pick – looking at point spreads, and stats, and the results of previous match-ups, doing research on who’s hot and who’s not, checking on weather conditions, thinking about whether games are being played on natural grass or astro-turf, and so on – it takes about 2 minutes of research and 3 minutes to write out my picks. And I don’t have to worry about whether or not my logic is right, or if there’s some hidden factor that I’ve forgotten to take into account. It’s much less stressful.
Using my algorithm, I’ve won the regular season pool two years in a row, and I’ve outperformed guys that know a whole lot more about football than I do who pick games for outfits like yahoo sports and espn. Why does this system work? The outcomes of games are genuinely uncertain. When face with uncertainty, we usually like to do things that make us feel in control. That’s why most people trying to pick winners put so much effort into research and number crunching. The problem is that results are pretty random. On average, the better team usually wins, but it’s often difficult to figure out which team is actually better. Having a good algorithm is actually an excellent strategy when you’re facing genuine uncertainty. A lot of people try to have a perfect week, where they pick every game correctly. My system will probably never do that – but on the other hand, it also won’t blow up. I’m very confident that my algorithm will win out over the course of 300 or so games each season. The people that are convinced that they know the game inevitably pick a few too many plausible upsets that don’t occur, or pick their favourite team to win an improbably game – but one way or another they are usually misled by the details of individual games.
However, my algorithm is close to worthless when the playoffs roll around. There are only 12 games in the playoffs. Over that small a number, the strength of the algorithm becomes a weakness – there’s no value in not blowing up, and the winner is the one that actually gambles and gets things right the most. So in the playoffs I get killed. All the analysis that other people do might help here, but so do judgement and luck (the person picking their favourite team all the way through when that team happens to win it all, for example).
What’s this got to do with innovation? A lot, actually. Big firms (or granting agencies, or state governments) with a lot of available resources, need a good innovation algorithm. They don’t need to pick individual projects that will win – they need processes in place that generate enough variety, that can experiment with the ideas relatively cheaply to see what works and what doesn’t, and that can amplify the ideas that are most promising. The focus needs to be on the process – not the individual cases. For these larger economic actors, innovation is the regular season, and over time, the best algorithm will win out.
However, if you’re in charge of an individual innovation project, or if you’re a small firm trying to execute one big idea, then it’s more like the playoffs. You need to be lucky, and you need to be passionate about supporting your particular idea. This requires a different skill set, and a different way of picturing the innovation process.
Over the long run, the people and firms that win through innovation are the ones that can do both – they have a good overall management process in place, but they also have people that can champion and execute individual ideas. Another key skill is to be able to identify when you need an algorithm and when you need judgement and luck. One of the reasons that a lot of big firms and government agencies get in trouble when they try to stimulate innovation is that even though they need an algorithm, the try the judgement and luck approach. This often misfires – leading to the truism that picking winners is bad policy.
The good news in innovation is that we can actually do some things ourselves to improve the odds in our favour – so outcomes aren’t quite as random as they are for football games. Having good processes is one of these things. Especially if we’re in a situation that calls for an algorithm.
(photo from flickr/ladybugbkt)
what’s it for?
Posted by Tim in innovation on 14 August 2009
Chris Anderson from Wired is pretty much always worth reading. His article in the July issue on managing using concepts of abundance rather than scarcity is one of his more interesting recent pieces. He lays out a bit of the argument behind his latest book, Free (and you can download an audio version of the book, for, well, for free!). Anderson makes a number of good points throughout the article, but the one I want to pick up on here comes right at the start:
In 1969, the Neiman Marcus catalog offered the first home PC, a stylish stand-up model called the Honeywell Kitchen Computer, priced at $10,600. The picture shows an aproned housewife caressing the machine, with this tag line: “If she can only cook as well as Honeywell can compute.” That image should be on every cubicle in Silicon Valley; it’s a testament both to what technologists get right and what they get badly wrong.
To their credit, they understood that Moore’s law would bring computing within the reach of regular people. But they had no idea why anyone would want it. Despite countless brainstorming sessions and meetings on the subject, the only application the Honeywell team could think of for a home computer (aside from the perennial checkbook balancing) was recipe card management. So the Kitchen Computer was aimed at housewives and featured integrated counter space. Those housewives would, however, require a programming course (included in the price), since the only way to enter data was with binary toggle switches, and the machine’s only display was binary lights. Needless to say, not a single Kitchen Computer is recorded as having sold.
This story illustrates an extremely important point about innovating. In many cases, even when we have a working version of our idea, we don’t actually know what it’s good for – just like Honeywell. In most cases, innovations are discovered in use. Intel didn’t know that microprocessors were for personal computers until after a lot of experimenting. Honeywell never did learn what personal computers were for – it was up to other hardware and software firms and users, experimenting together, to figure that out.
How do we reconcile this with the idea that we have to embrace constraints? This is another of the tricky balances that we need to find when we’re managing innovation. On the one hand, we need to experiment to figure out what our innovation is actually good for – which is much easier to do when we have unlimited resources. The solution comes from finding ways to experiment as cheaply as possible, to fail, and to learn from the methods that don’t work. A second avenue to explore is to get the innovation into the hands of users as quickly as possible (the actual innovation – not a focus group description!). This is often the only way that we can actually figure out what our own ideas are supposed to do!
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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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