Archive for category selection
How to Fail at Innovation
Posted by Tim in evolving economic entities, filter, selection on 25 February 2010
The way to fail at innovation is to try to avoid failing.
The idea of failure has popped up quite a bit this week for some reason. Innovation is filled with tensions that we have to become comfortable with if we’re going to succeed. One of the big tensions is between success and failure – when you’re innovating, you can’t have one without the other. In a very interesting post, Arne van Oosterom suggests that this is good argument for emphasising adaptability rather than innovation for many firms, as this eliminates the discomfort caused by the tension between the two.
I am in complete agreement with van Oosterom that adaptability is a desirable trait for organisations to develop. But in doing so, I don’t think we can abandon innovation. I think that we need to develop strategies for dealing with failure.
This was the conclusion reached by both Peter Yates (ex-CEO of PBL, among other things) and Patricia Cross (Non-Executive Director of Wesfarmers and numerous other organisations) in their talks at the Leaders’ Edge Luncheon here in Brisbane on Tuesday. The topic of the talks was ‘Tales from the Corporate Battlefield’ – and it sounds like both of them have been in plenty of battles. And one of the common themes that they touched on is that if you’re doing anything worthwhile, you will experience failures. It’s not fun, it’s not something to be embraced, but it’s inevitable.
This theme was also addressed by Hutch Carpenter in a fantastic post this morning. In making the point that innovative firms will fail, he included this picture:
He includes this quote from Jeffrey Phillips – one of the best innovation bloggers around:
As Edison and countless others have demonstrated, you rarely get it right the first time, and if you are stymied by early failure, then you’ll never find and implement the best ideas. Innovation, as has been pointed out by individuals with far more to say about it than me, will create some failures. Your job isn’t to avoid the failures, since you can’t predict them in advance, but to reduce the cost and impact of the inevitable failures. In other words, keep moving.
So there’s the contradiction that we have to deal with – if we’re going to successfully innovate, we have to fail. The key is to figure out how to do it as cheaply as possible. As I’ve said before, if everything that you try works, then you’re not trying enough things. These contradictions are one of the things that makes managing hard, but it’s also one of the things that makes good managers so valuable. Failing isn’t fun, and it’s natural to try to avoid it. However, it is a necessary element of success.
In other words, the one guaranteed way to fail at innovation is to try to avoid failing.
Filtering, Crowdsourcing and Innovation
Posted by Tim in book riffs, filter, innovation strategy, selection on 1 February 2010
How can we take advantage of the ‘wisdom of crowds’ in our innovation efforts? There are some distinct challenges in trying to do this. The basic idea is this: if you get a large number of people to estimate something – the weight of an ox, or the number of jellybeans in a jar, for example – usually the average of all of the estimates is closer to the actual number than any individual’s guess. Consequently, there is a strong argument for taking advantage of this phenomenon if you are trying to get a handle on estimating a particular number. Businesses have used these techniques to improve their sales forecasting for example (Gary Hamel includes a really nice example of how Best Buy used this method in The Future of Management).
Can this work to improve innovation? It’s not as obvious that it will. I’m currently reading You Are Not a Gadget by Jaron Lanier (more on this book in a later post). Lanier has this to say about using crowds:
The reason the collective can be valuable is precisely that its peaks of intelligence and stupidity are not the same as the ones usually displayed by individuals.
What makes a market work, for instance, is the marriage of collective and individual intelligence. A marketplace can’t exist only on the basis of having prices determined by competition. It also needs entrepreneurs to come up with the products that are competing in the first place.
Since the internet makes crowds more accessible, it would be beneficial to have a wide-ranging, clear set of rules explaining when the wisdom of crowds is likely to produce meaningful results… Among other safeguards, I would add that a crowd should never be allowed to frame its own questions, and its answers should never be more complicated than a single number of multiple choice answer.
Crowds can be useful, but also dangerous. Nassim Nicholas Taleb says that crowdsourcing should be avoided in situations where the potential payoffs are very complex, and when we don’t know what the outcome probability distribution looks like. Unfortunately, this is precisely the case for most innovations.
Relying on crowds can lead to innovation problems. Stefan Lindegaard identifies this as one of the common causes of open innovation failure (the comments on that post are worth reading too):
Many companies start off with idea generation platforms hoping that external contributors will contribute with great ideas and/or technologies. Most do not deliver on the expectations as they get more trash than gold.
And in a post that addresses some of the issues with crowdsourcing really nicely, Graham Horton says:
In conclusion, customer idea portals as they are currently popularly advocated will produce limited results; they will only provide suggestions for solutions that are apparent to customers, given their level of expertise and self-knowledge.
All this might suggest that we can’t use crowds to help innovation. However, I think that these two quotes suggest a possible way that we can still take advantage of crowds in our innovation efforts. One of the issues is that we often misunderstand how crowdsourcing actually works. The Lindegaard quote suggests that people think that we can turn to our crowd (customers, stakeholders, etc.) and just wait for the good ideas to roll in. This is in line with a common understanding of crowdsourced systems – people often talk about Linux, for example, as a process where thousands of people write bug fixes for the software, and all of these fixes get put into the program, making it better. This misses a critical step.
That’s a diagram that I made last year to explain to some friends how icanhascheezburger.com works – but it explains Linux just as well as it explains lolcats. The critical step in the process is the middle one. Both systems crowdsource content – Linux crowdsources code, icanhascheezburger crowdsources cat drawings. The problem is, not all of the code works, and not all of the lolcats produce lols. In each case, there is a small group that filters the incoming content. We don’t have crowds creating stuff, and then voting on stuff. We have crowds creating stuff that answers questions posed by the group guiding the process. The answers that work are then selected by that group as well.
This leads to the answer that both Lindegaard and Horton suggest: in order to get useful answers from crowds, we have to have good internal capacity ourselves. Crowdsourcing needs to be guided. To use the crowd in innovation, we need to set the questions. And we need to know enough to be able to figure out when the crowd is giving us good answers.
A while ago I talked about using jams to select ideas. This process follows these principles. The questions being asked are set by the organisation, so the crowd is trying to address a specific problem. And the best answers are not just judged by popularity – there are several evaluation mechanisms that can be used. You can use the votes and go with the most popular. You can use the ideas that were most polarizing. You can take the ideas that are generated and plug them into whatever other system you use (stage/gate, gut feel, whatever).
Crowdsourcing then is another tool that we can use in our aggregate, filter and connect strategies. In this case, the filtering is the critical step. If we don’t filter correctly, crowdsourcing simply aggregates, which by itself doesn’t help us much. And the aggregated crowdsourced answers need to be connected to questions that we know are important. Crowdsourcing is not a panacea, but it can be a useful innovation tool if we use it correctly.
Graham Horton has written a terrific post that looks at which questions we should ask the crowd.
Using Jams to Select Ideas
Posted by Tim in innovation strategy, selection on 4 January 2010
I have talked a couple of times recently about some results from the assignments in my MBA class this year. In assessing the Innovation Value Chains within their firms, 3 out of 60 identified their organisation as ideas-poor, while the other 57 had bigger problems with idea selection and idea execution. The paradox is that when firms try to become more innovative the first thing they usually do is take steps to generate more ideas.
In part, I think that this is due to the fact that we have relatively more resources available to help with this part of the process. There are plenty of books on improving idea generation and creativity. There are plenty of consultants around with methods for doing the same. It is the area where it is the easiest to see quick results, which makes it attractive. This is deceptive though, because the data from my MBA students strongly suggests that this is the area where organisations actually need the least help.
What do we have that can help with idea selection and idea execution? One approach that is very good to use for idea selection is idea jams – a technique originally developed by IBM. I’ve been talking about this some recently with my friend Kate Morrison, who uses this approach in her firm Vulture Street Innovation Software and Services. Here is how she describes the jam process:
A jam is an online, time-limited collaboration event specific to an invited group of participants and focused on a particular organisational problem, opportunity or challenge.
Compared with traditional problem solving and group decision-making techniques, the jam approach offers significant benefits because it is:
* focused around specific themes or challenges, as defined by management – hence avoiding non-productive and open-ended solicitations (such as suggestion boxes) or community discussions;
* specific to the group invited to participate, which can include customers and suppliers as well as employees;
* scalable beyond the limits of physical get-togethers, able to accommodate hundreds of participants; and
* time-limited, allowing a concentration of attention and energy and preventing the process from fading into the background of business-as-usual.
So it is a crowd-sourcing method, but it uses a specific, hand-picked crowd. The thing that I particular like about this approach is that it doesn’t just generate ideas, it selects them. As ideas come in, people are able to comment on them, and vote on the ones that they like. Here are some results from one of Kate’s projects showing the contributions of all of the people participating in a small jam, along with the 6 most popular ideas (indicated by the stars):
These results from illustrate some interesting points:
- Contributions follow a power law (roughly). This is very normal for interactive networks of people. There are usually a small number of people with really large contributions (in this case, the top 3 idea contributers generated 38% of the total ideas!), a slightly bigger number of people with a few contributions, and a majority of people who contribute only one or two ideas (Clay Shirky explains this really well in one of my favourite TED talks). In many situations, it is difficult to solicit the ideas from this last, largest group, which is important because:
- Idea quality is unrelated to idea volume! The idea voted the best was contributed by the 14th most prolific idea generator, and the one voted second best was contributed by the least prolific idea generator. The top two idea contributors support Linus Pauling’s contention that ‘the way to get good ideas is to get lots of ideas, and throw the bads ones away.’ But in good news for us introverts, the people talking the most aren’t necessarily the ones coming up with the best contributions.
- The key point is that at the end of this process, you end up not just with a bunch of ideas, but with an idea of what the best ideas are. This is what I like about jams as innovation tools – it is actually a selection tool, which is one of the areas that firms are often weak. At the end of the process, you don’t have hundreds of new ideas, you have 3 or 4 really promising ones.
So this is one method you can use to improve your capability in selecting new innovation ideas. There are more formal processes available like Stage-Gate too. If you’re going to invest time and money in improving innovation within your organisation, I think it’s essential that you focus on getting better at selecting and executing new ideas, rather than simply generating them.
You Don’t Need Any More New Ideas!
Posted by Tim in innovation strategy, replication, selection, variety on 30 December 2009
Scott Berkun let out the secret of innovation today in an outstanding blog post. It’s a secret that Rowan Gibson tried to let out of the bag recently, and so did Braden Kelley on Blogging Innovation. I’ve tried to tell you about it too, using both analogies and statistics. The secret idea of innovation is this:
You don’t need any more new ideas.
Here is Berkun on the what we really need:
If there’s any secret to be derived from Steve Jobs, Jeff Bezos, or any of the dozens of people who often have the name innovator next to their names, is the diversity of talents they had to posses, or acquire, to overcome the wide range of challenges in converting their ideas into successful businesses.
That’s it. The problem is executing your ideas. Here’s an example – yesterday I talked about mousetraps – here are some interesting stats.
The patent for the flip-trap mousetrap design was filed in 1899. That’s a better mousetrap, right? We’re still using that design over 110 years later, so it’s probably pretty good. And yet, since 1899, the US Patent Office has granted over 4400 mousetrap patents. They receive more than 400 new mousetrap patents every year. So there’s no shortage of ideas. But fewer than 20 mousetrap designs have led to products that have actually made money. The problem in innovation is executing your new idea, and getting it to spread.
There is so much effort put into improving innovation by generating more ideas. This isn’t necessarily wasted effort, but it’s not the smartest use of resources. My MBA students evaluated innovation within their firms:
This approach is flawed, and my MBA students demonstrated why. They came from a wide range of organisations – huge multinationals, small start-ups, government departments, and educational institutions. Despite these different backgrounds, their findings were remarkably consistent – only 3 of the 60 organisations that they work in are ideas-poor. The other 57 (that’s 95%!) have problems with either selecting or diffusing ideas.
Here’s more from Berkun:
The closest thing to a real secret is this: In my years studying and teaching all things innovation, there’s one fact that’s the hardest for people to swallow and it goes as follows – To invent or create is to take a bet against the unknown. No matter what you do, you are still betting you can do well in the face of many things that are out of your control. Don’t like that? Don’t want uncertainty? Then do something else. Comfort with risk and uncertainty is the real secret. Or at least acceptance of the fact you can work your ass off for uncertain rewards.
Where does this leave us? Here are some conclusions:
- If you’re going to get some help to improve innovation at your firm, don’t focus on generating ideas. Get help on selecting ideas, or on getting them to spread. Those are the hard parts.
- Innovation is a bet – you’re betting that your new idea will work better, that it will meet needs, that it will fit into the value network. All of these things have to happen for your innovation to work. Like Berkun says, this is a leap into uncertainty.
- Most of the innovation problems that organisations face are problems with innovation diffusion – the challenge is to get your new ideas to spread.
The new idea that I’d like you to accept is that you don’t need any more new ideas. Instead of generating more ideas, let’s develop some plans for getting better at executing our ideas. That seems like a good idea heading into the new year, doesn’t it?
How to Assess Your Innovation Capability
Posted by Tim in innovation strategy, replication, selection, variety on 18 December 2009
How do you know how good you are at innovation? One of the tools that we have found very useful for assessing innovation within organisations is the Innovation Value Chain. The tool was developed by Morten Hansen and Julien Birkinshaw and published in an article called The Innovation Value Chain in Harvard Business Review in 2007.
I’ve talked about this before – There are two key points with this model. The first is that there are three stages in the process of innovation: idea generation, selecting & developing ideas, and diffusing ideas. The key part, however, is that all three parts of that process have to be working well in order to innovate.
The three step aspect of the innovation process is important. Measuring idea generation, selection and diffusion helps organisations get around the problem of simply equating innovation with ideation. Organisations that do this often find that they have plenty of ideas, but they’re still not being very innovative. This is because innovation actually doesn’t occur until you execute new ideas. To do that, you have to be good at having ideas, but more importantly you also have to be good at selecting ideas and getting them to spread.
This leads to the second key point of the Innovation Value Chain – your innovation process is only as good as your weakest link. This is not simply a linear model of how things happen, it is a description of a complex system. For example, if you are bad at selecting ideas, people will become less willing to give you their new ideas. This means that there are feedback loops between the three parts of the process. If you are going to improve your innovation, the whole system has to get better. You can use the IVC to identify your weak point and take steps to improve it. Then you can move on to whichever step is your weakest point now. If you keep doing this, you will build excellent innovation capability within your organisation.
Here is a Special Deal!
Our research has shown that while organisations usually first try to improve their idea generation, 95% of the time, this is not their weakest area. I’m curious to see how broadly this is true – so I would like you to please
In exchange for your time, I’ll give you some feedback on your results. If you’d like some information about what your organisation’s innovation strengths and weaknesses are relative to others who have taken the survey, just leave an email address when you take the survey. If you would like several people from your organisation to take the survey, I can compile the results – just have everyone indicate the name of the organisation when they fill out the survey. All results are, of course, confidential. To get the most meaningful results, please tell everyone you know that might be interested about this survey. Thanks for your help!
Innovation is about more than just coming up with new ideas. If we’re going to be innovative, we have to be able to execute new ideas. The Innovation Value Chain is one tool that can help us get better at this.
the hardest part of innovation
Posted by Tim in innovation strategy, replication, selection on 28 November 2009
I was thinking about my talk from yesterday, and one bit that I just spontaneously threw in is probably worth expanding on. I spent a lot of this week marking assignments from my MBA students (who were an exceptionally good bunch this year). For the major assignment this year, I had them analyse their own firm or organisation using the Innovation Value Chain model developed by Morten Hansen and Julian Birkinshaw.
There are two key points with this model. The first is that there are three stages in the process of innovation: idea generation, selecting & developing ideas, and diffusing ideas. The key part, however, is that all three parts of that process have to be working well in order to innovate.
Both John and I have talked about the dangers of over-focusing on idea generation at the expense of execution, so we find this to be an extremely useful model. In particular, we have frequently observed organisations decide that they have to improve their innovation, and then sinking all of their resources and effort into idea generation.
This approach is flawed, and my MBA students demonstrated why. They came from a wide range of organisations – huge multinationals, small start-ups, government departments, and educational institutions. Despite these different backgrounds, their findings were remarkably consistent – only 3 of the 60 organisations that they work in are ideas-poor. The other 57 (that’s 95%!) have problems with either selecting or diffusing ideas.
So when firms focus on improving their idea generation, it is a mistake for two reasons. The first is that this is almost certainly not where their problem lies. They’re probably worse at execution. The second is that it does not take into account the entire innovation system. I just saw this quote from Russell Ackoff (via Venessa Miemis):
Improving the performance of the parts of a system taken separately will necessarily improve the performance of the whole.
False. In fact, it can destroy an organization, as is apparent in an example I have used ad nauseum: Installing a Rolls Royce engine in a Hyundai can make it inoperable. This explains why benchmarking has almost always failed. Denial of this principle of performance improvement led me to a series of organizational designs intended to facilitatethe management of interactions: the circular organization, the internal market economy, and the multidimensional organization.
Innovation within an organisation is a system. It is much more than simply idea generation. And if you focus only on improving your ideation, there’s a pretty good chance that your overall innovation performance will actually get worse. The hardest part of innovation is idea execution, and we simply must get better at it.
What is an Innovation Culture?
Posted by Tim in innovation strategy, replication, selection, variety on 27 November 2009
Here are the slides + audio from the talk I gave this morning for the UQ Centre for Educational Innovation and Technology’s planning day. One of the things that they were working on was thinking about what they want their innovation culture to be, so Phil asked me along to give some thoughts on that. I’m not sure how close my talk was to what he wanted, but I gave it a go. It’s too bad I didn’t record the Q&A at the end, because some really good ideas came up during that too. They’re a really bright group and I’m looking forward to seeing what they’re able to do.
Even though slideshare says that this runs for over an hour, the talk is just 18 minutes.
As usual, if I sound like Jabba the Hut, you have to upgrade your flash player – slideshare doesn’t play well with older versions.
Also, I’ve added an index page with links to all of the talks that we’ve put up. There are a couple more (with video) coming soon!
focus on process, not tools
Posted by Tim in book riffs, innovation strategy, networks, replication, selection, variety on 22 November 2009
I’m reading Kill All Your Darlings by Luc Sante at the moment, which is very good. It includes a number of pieces on culture, many originally from Village Voice or the New York Review of Books. Sante is a fantastic writer and there are a number of great lines throughout the book, but one just jumped out at me in his piece on the photographer Walker Evans.
He had never been a camera snob, or even, although he was a superb printer, much concerned with the mechanics of his art (once when a student asked him what camera he had employed to take a particular shot, he became irate, declaring the question tantamount to asking a writer what sort of typewriter he’d used).
I love this little story for a number of reasons. The simplest is because I’ve never been a big fan of camera snobs, or anyone that gets too hung up on equipment. Equipment can make some things easier, but it can’t replace knowledge and experience accumulated over time.
The second reason that I like the quote though is that it illustrates a problem that we often run into in firms that are trying to implement a new innovation program. Often these initiatives come about because someone at the top has said something like “innovation has been one of our ‘core values’ for values, so we better start doing something about it.” The first thing that always happens in these cases is that the organisation goes out and gets some software. It might be something that supports message forums for Communities of Practice, or a tool for capturing ideas. The flaw in this approach is that the minute you approach Knowledge or Innovation Management as an IT problem, the initiative is dead.
Managing innovation is a people and process problem, not a technical one. Yes, it helps to have some tools to use, but if you want your organisation to be more innovative, you have to be good at generating ideas, choosing the best ones, and getting those ideas to spread (variety, selection and replication – an evolutionary process). These are people problems, and they are often network problems. Get your processes right first, then you can get some tools to help facilitate them.
If you focus on improving the innovation process, not the tool, you will be much more likely to be successful.
(photo by Walker Evans)
probabilities
Posted by Tim in innovation strategy, selection, time on 18 November 2009
This might not make much sense to all the readers here in Australia, but I’ll give it a go – an interesting thing happened in the Monday Night Football this week – the Patriots lost after failing to convert a 4th and 2 on their own 28 yard line with a couple of minutes to go. Going for it was unusual – the common knowledge is that you always punt the ball in that situation. And as Matt Yglesias has been pointing out, in this case the common knowledge is wrong. Here’s his summary:
The point of punting is that you’re trading possession of the ball for field position. Whether that’s a good trade depends in part on how likely your offense is to secure a first down if you don’t kick the ball to your opponents, and in part on how good the opposing offense is. But the better their offense is, the worse kicking the ball over to them looks. The only reliable way to stop a really good offense is to be extremely reluctant to surrender the ball to them. Against a poor defense, field position is extremely valuable since they’re unlikely to score unless they get the ball close to your end zone. But what it means to be a great offense is that you’re a legitimate threat to score from any position—you really, really don’t want an offense like that to have the ball.
The problem is that conventional thinking in the NFL is that after three downs the default should be to give up possession of the ball unless it’s a desperation situation or something else special. But most of the time teams should be extremely reluctant to give the ball up. Fourth-and-shorts aren’t that hard to convert, and field position is a lot less valuable than possession of the ball. There’s just a convention of labeling any decision to run an offensive play as “risky” that’s completely independent of any actual assessment of the risks.
This point has been supported by analysis reported by the New York Times as well – it was the correct decision, it’s just that in this case it didn’t work.
What does this have to do with innovation? A couple of things. One is that many firms do things that aren’t what they should be doing simply because everyone else does them. Like NFL coaches, they aren’t even playing the odds, they’re just following routines that may or may not be the best ones. If your firm does this, it will become a serious obstacle to innovation.
The second point is that playing probabilities definitely does not mean that you will win every time. Every now and again you will screw up. Probably not as publicly as the Patriots just did, but it will happen. Being innovative by definition means that some of your ideas won’t work. You have to come to grips with this when managing innovation. But it also means that you can innovate more effectively if you have a regular system in place that allows you to try out lots of ideas and then scale up the ones that work. Previously, I’ve called this having an innovation algorithm.
Stephen Shapiro has a really nice post on this topic too – where he distinguishes between playing safe using statistics and going for it using probability.
If a statistics-driven innovation model does not work, what would a probability-based model look? Probability tells me that if everything is equal, the more bets I have, the more likely one will be successful. The odds of 1 success out of 200 are greater than 1 success out of 20.
But how can you have more bets without diluting your effort and potential returns? The key is to learn as you go.
He then outlines an approach where you place a large number of smaller bets, then scale up the ideas that work. I think this is exactly correct. This approach allows for failure, but it lowers the cost of failing. And it increases the probability of hitting big. It’s a good example of what an innovation algorithm might look like.
implementation
Posted by Tim in filter, innovation, replication, selection on 13 November 2009
I just saw this on Merlin Mann’s twitter feed:
The guy who worries people will “steal” his idea might better ponder why nobody “steals” his implementation.
As I keep saying – ideas are cheap, and implementations are valuable. We need to find better ways to cycle through ideas rapidly. This reminds me of a post from lifehacker which includes this quote from a tech CEO:
Share your great ideas promiscuously as possible to attract collaborators, even in highly specialized science and engineering fields. Otherwise your ideas will never gain traction and actually happen, and you will always be a dreamer. In the unlikely event that someone steals your idea, take it as a compliment and move on to the next great idea.
We generate ideas constantly. The key is to figure a routine for finding the best ones, and implementing them.
Henry Chesbrough on business models
Posted by Tim in business models, filter, innovation strategy, selection on 12 November 2009
Stefan Lindegaard has a post with links to a lot of good innovation related material, including an interview with Henry Chesbrough.
The Chesbrough interview is terrific. He is best known for his research on open innovation, but a central part of that has been his work on business model innovation, which is the focus of the interview. In Chesbrough’s view, many good ideas for new products and services fail not because of any flaw in the idea, but because firms are unable to construct a business model for them that is different from their current one.
The moral of the story, my interest in this business model innovation is that if we are going to sustain research and development in our society, we have to take seriously the task of how to innovate not only new technologies but how to innovate business models to commercialize those technologies.
The main point here is that business model innovation is an essential element of success, but that firms often find it extremely difficult to implement new business models. Consequently, their success in innovation becomes an issue in effective change management, rather than simply relying on developing good ideas. This is a bit alarming because it’s hard enough just coming up with good new ideas! If you then have to change the structure of your firm to implement them, the challenges increase exponentially.
On the other hand, so do the payoffs. Most of the big success stories recently have not been the result of the introduction of novel new products or services, but instead have been due to creating effective new business models. There were book stores before amazon, search engines before google, and mp3 players and online music stores before iPod/iTunes. Those three have all been successful because of new business models – they have created new revenue generation mechanisms, new value propositions, and new value creation networks to build new business ecosystems around products and services that weren’t particularly novel. I am increasingly convinced that this is where we all should be concentrating our innovation efforts – particularly our management attention.
innovating with constraints
Posted by Tim in innovation, networks, replication, selection, variety on 19 October 2009
I’ve been giving further thought to the issue of public sector innovation which I discussed briefly last week. John and I do a lot of work with people in the public sector as that makes up a fairly big part of Brisbane’s economy, and I know that people often find it difficult to be innovative in that area. However, it is essential that we have good public sector innovation because large parts of our economies are in the public sector, and these parts are often very important. We just can’t afford to have industries like health and education stagnate – innovation is critical in these fields, as it is in the other areas that fall within the public sector.
So what’s the problem? There are a few. One is that overall, the public sector is not viewed as being very dynamic. Consequently, it does not attract a lot of attention from those of us that are interested in innovation. The Australian government is currently undertaking a review to try to devise strategies to improve public sector innovation. The website for this project includes a list of links to resources on public sector innovation (at the bottom of the page) – and you can see that there are not a lot of resources available (the project has a twitter feed too which updates new resources as they find them). This reflects a lack of interest at the levels of both research and policy.
The second issue is that government departments are often fairly risk averse – which makes innovation challenging. This issue is consistently raised by people in our innovation classes that come from the public sector, but it is a common issue for many people in other sectors as well – particularly middle managers that don’t have much scope for action. When I talk to people in this situation they often say that the only way they can be more innovative is if they get more support from top management. It is true that top level support generally helps improve innovation. However, if you are waiting for increased upper management support before you start trying to innovate, in most cases, you’re likely to be waiting for a long time.
There are a few things you can do to get out of the straightjacket. The main thing is to figure out how to try things. Experimenting is the key to innovating. “The secret of fast progress is inefficiency, fast and furious and
numerous failures.” — Kevin Kelly Now, obviously, failure is not a very popular idea within most government departments. The key to the whole idea though is to figure out ways to generate ideas and discard the ones that don’t work as quickly and cheaply as possible. There are three steps here.
The first is to generate ideas. “The secret to having good ideas is to have a lot of ideas, then throw the bad ones away.” — Linus Pauling Usually, this isn’t the problem. People are naturally creative, and the number of untapped ideas that are in your organisation will probably surprise you. One way or another, you need to figure out how to tap into these. If you want some place to start, go to the Tom Peters site and download the Innovation Tactics paper that he has there.
The second step is the tricky one in public sector organisations – you have to select which ideas to try out. The central idea here is to look at how much authority you have. This might be as simple as signing authority – if you can authorise items worth up to $100, then what new ideas can you try to implement for $100 or less? What if you can’t authorise any expenditures? The two jobs in which I’ve been the most innovative have actually both been in the public sector. In the first, I worked out at the start 47 ideas that I thought might make my section run better. Over 18 months, I tried out 45, at a total implementation cost of $0. At the end of that time, my section was just under 20% more effective in turning enquiries into new students, in part as a result of some of those 45 ideas that we tried. Not all of them worked, but a lot of them did – and some of the simplest had the biggest impacts. My bosses weren’t too enthusiastic about new ideas when I started, but they were very enthusiastic about results. Most bosses are. So the second step is to figure out what you can get away with, and start trying things that fall within your scope of power. That’s how select the ideas to try – you may have to wait on the big ones that will change the world, but if you succeed with some small ones, you may eventually get to try those out too.
The final step is getting the ideas that work to spread. “Some people look for things that went wrong and try to fix them. I look for things that went right, and try to build off them.” —Bob Stone You need a strategy for amplifying the good ideas. Part of this is selling them to the people around you. To do this, you need to figure out which of the ideas are working. An important activity here is measurement – if you’re able to measure the outcomes of your ideas, it is easier to gain support for trying more things.
Innovating is always hard. It’s especially hard if you don’t feel supported. But the key to innovating when you have constraints is to try things. Try as many as you can, figure out what works, and do more of that. It’s a formula that you can follow in nearly every work setting. Instead of telling me why it won’t work in yours, why don’t you spend the time figuring out a new idea to try yourself instead?
“We have a ‘strategic plan.’ It’s called doing things.” — Herb Kelleher
(photo from flickr/djwudi – creative commons licensed)
This article was one of the winners of Blogging Innovation’s October Innovation Contest.
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…)
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)
more on academic blogging
Posted by Tim in replication, selection, variety on 1 July 2009
I’ve written about Lilia Efimova’s excellent PhD research before, and now she’s written another really good post. It’s structured around this table:
This is a really nice taxonomy, and there’s not a whole lot that needs to be added to it. I suppose I take a bit more of an evolutionary view of academic blogging. You can use the blog to generate variety – which corresponds with Lilia’s low-level blog entry creation. You just use the blog to generate ideas, and it can also function as a catalog of the ideas that you’ve generated in this way. You can also use a blog to help with idea selection, by looking at which ideas people respond to (this can be in terms of number of readers, comments, trackbacks, amount of controversy generated, etc.). Or you can blog to help get your ideas replicated – to get them to spread. This maps on to a couple of different categories of Lilia’s.
In my previous post, Marco commented about how he has found more value from wikis than blogs. He uses wikis really well, so I can see why he says that. On the other hand, I was talking with my friend Alex over the weekend about electronic communication, and how each new medium requires the acquisition of a new set of skill sets. For me, I pretty much know how to make a blog work, so that is mostly what I stick with. I’m not sure that there is one method that is best for everyone… In part, the media that you choose will depend on which of three evolutionary functions you are most interested in achieving, as well as which suits your personal communication style.
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