Archive for December, 2009
Linking Innovation to Strategy, part 2
Posted by Tim in business models, innovation strategy on 15 December 2009
One of the critical elements of managing innovation is linking your innovation efforts to your overall strategy. Over the weekend, I talked about how you can use the Strategy Diamond by Hambrick & Fredrickson to help achieve this coordination. Another tool that you can use is the Business Models idea, something that we’ve discussed here a few times.
Like the strategy diamond, the business model is an integrated analytical framework – but for assessing a particular innovation rather than an entire strategy. It is explicitly built around the proposition that having a good idea is insufficient – you have to execute the idea effectively. Henry Chesbrough developed the concept as a way to explain how to profit from open innovation. His contention is that when multiple organisations have access to similar ideas, the one with the best business model wins. While I really like Chesbrough’s system, there are others around with different elements that work in basically the same way.
This is another tool that we find useful when working with firms to improve their innovation process. There are several key ideas that come from using this framework:
The first is that, again, all elements of the business model are interconnected. This means that when one part of the model changes, the rest probably need to as well. For example, one of the key points that Mitch Joel makes in his book and on his blog Six Pixels of Separation is that because of the web, we are now connected to many more potential customers than we were previously. This means that our market has almost certainly changed from the local people that are interested in our services to potentially everyone. Trying to reach everyone is a fatal mistake, but this is still an important change in our possible market. Few business models scale up without requiring changing. If we decide to try to reach this market, it is likely that we will need to change our value proposition and our revenue generating mechanism.
The second point is that we often get locked in to using the first business model that works for us, even if that is not a good model as our business expands. The example that Chesbrough uses to illustrate this is Xerox. Their original model was built around providing high-volume photocopying services for very large corporate customers. So when they tried to bring all the great ideas coming out of their Palo Alto Research Center to market, they used the same model. However, this model was poorly suited for selling ideas with broader appeal, such as the graphical user interface, or ethernet systems. Consequently, these ideas failed for Xerox because they didn’t not fit in with their dominant business model logic. Innovative new products will often have a value proposition that differs from that of your current core products. If you are going to successfully bring them to market, you probably need different customers and possibly even a new value network.
A third point is that business model innovation can be extremely powerful. For example, the Apple iPod was as much a business model innovation as a design innovation. There were other mp3 players before the iPod – it wasn’t especially innovative as a product. However, it really took off when it was coupled with iTunes. By linking the mp3 player to a convenient, legal source of downloadable music, Apple created a new value network. Adding music, artists and distributors into the network made a new ecosystem for the iPod – and this innovation was more important to its success than any of the new ideas actually embedded within the product itself.
Innovation is not about having the best ideas – it is about having the best execution of new ideas. The business model framework is a tool that can help you improve your execution, and that is why I think that it plays a crucial role in linking innovation to your strategy.
(Figure from Henry Chesbrough, Open Innovation, Harvard Business School Press, 2006)
James Boyle’s Important Ideas on IP & Innovation
Posted by Tim in business models, innovation strategy, replication on 14 December 2009
Intellectual Property rights encourage innovation, right? Right? Well, not necessarily. Actually, people that study this empirically consistently find that the evidence suggests that they don’t. Here’s a fantastic talk by James Boyle discussing his book The Public Domain, which addresses this exact issue:
(Thanks to Gerd Leonhard for the tip on this talk)
Boyle’s book is outstanding (there’s a link to a free download of the book here). His central thesis is that we tend to underestimate the benefits of openness while overestimating the potential downsides. Consequently, we have too much legislation supporting IP rights. However, he effectively documents all of the research on this topic, which consistently shows that IP rights in general do not encourage innovation, they suppress it.
The original intent of patents was to encourage the use of technology so that new ideas could be built on these technologies as soon as possible. At the national level, this has several implications:
Class, repeat after me:
(1). Green jobs are NOT a zero sum game where nations are competing for a fixed number of them.
(2). If China or Germany or anyone develops “innovative energy technology”, that is NOT bad for us. It is in fact *awesome* for us, as we can then adopt it and use it.
People, ideas are public goods. That is the whole basis of new growth theory. If China is now doing cutting edge R&D, that is an unmitigated blessing for everyone on the planet.
Will Wilkinson picks up on that post:
Worrying that other countries are pulling ahead is like worrying that the other oarsman in your boat will beat you to the destination if you’re lazy. You’re in the same boat! The smart thing is to goad everyone else into going as fast and hard as they can.
So if we don’t have IP rights to protect our ideas, how do we make money off of them? We win by building the best business model.
The value of an innovation is not in the idea that it is built on, but on the execution of that idea.
(thanks to Venessa Miemis for the link to the Wilkinson post)
Linking Innovation to Strategy, part 1
Posted by Tim in complex systems, innovation strategy on 13 December 2009
I just read a great post by John Borthwick which reviews the upcoming book about google by Ken Auletta. I encourage you to read the entire post, as I’m only going to focus on this part of it:
What about a corporate statement of intent like Google’s “Don’t be evil”?
“Don’t be evil” resonated with me because it suggested that Google as a company would respect its users first and foremost and that its management would set boundaries on the naturally voracious appetite of its successful businesses.
In the famous cover letter in Google’s registration statement with the SEC before its IPO, its founders said: “Our goal is to develop services that significantly improve the lives of as many people as possible. In pursuing this goal, we may do things that we believe have a positive impact on the world, even if the near term financial returns are not obvious.” The statement suggests that there are a set of things that Google would not do. Yet as Auletta outlines, “don’t be evil” lacks forward looking intent, and most important it doesn’t outline what good might mean.
That immediately made me think of Hambrick & Fredrickson’s strategy diamond, a tool that John & I find useful when we’re working with firms on innovation and strategy issues. It looks like this:
In their original article on the diamond, Hambrick and Fredrickson take issue with statements like google’s – saying that these statements of intent are insufficient as guides to strategy. They contend that a sound strategy requires all five elements, and that these need to be integrated and consistent with each other.
It is a practical model, and you can get some good results with it (we’re currently using it to help redesign our MBA recruiting strategy). I like the systems approach it takes (which, ironically, is listed as a weakness at the Proven Models website!). There are a couple of key ideas to take from the diamond model.
First, innovation must be integrated with strategy. When you work on implementing new ideas, you need to think about how it fits in with what you’re currently doing.
Second, all elements of strategy are interconnected. This is critical when you think about innovations over a longer time horizon. If you are aiming to create a different value proposition, or to move into a new customer segment, all the other parts of your strategy will likely have to change as well. This is a large part of why large firms find it difficult to react to radical innovations – everything has to change. Nevertheless, the strategy diamond can at least help you think through how to approach these more radical changes.
Finally, innovation and strategy are about choices, not planning. I read a blog post yesterday where the author said something along the lines of “if you’re not thinking about all 6 billion people in the world as your potential customers, you’re crazy.” This is stupid. No matter what idea you’re trying to spread, it can never be for everyone. If you try to make something that pleases all 6 billion people in the world, the odds are very high that you will end up pleasing something closer to none of them.
Strategy is about choosing which subset of the 6 billion you’re aiming for – choice here is critical. That’s the real weakness with “don’t be evil” – it doesn’t actually help us make any of those choices.
Lessons from Babbage’s Difference Engine
Posted by Tim in evolving economic entities, innovation on 12 December 2009
Here’s a nice video on Charles Babbage and the Difference Engine:
It’s an example that I use in my classes to illustrate two big points. The first is that invention is not innovation. You don’t have an innovation until you have an idea that is ready to spread, and you can’t have that if you can’t execute your idea at least once.
The second is the fact that it is critical to consider how your new ideas embed themselves within the existing economy. A big part of Babbage’s problem was that manufacturing technology simply wasn’t capable of building his machines until many years later.
It really is possible to be too far ahead of your time. If you are, you can still be a genius like Babbage, but it’s a lot harder to be a successful innovator.
(thanks for profhacker for the original link to the video)
Three Horizons of News Innovation
Posted by Tim in business models, innovation strategy, time on 11 December 2009
Journalism is a fascinating business model innovation case study these days – at least if you’re on the outside looking in. Journalism must exist in some form or another for democracy to work, yet newspapers and other journalism outlets are struggling terribly right now. The availability of inexpensive digital content has made it much harder for news firms to rent out eyeballs at a price that will keep them in business, consequently there is a huge need for business model innovation in this field.
There’s an interesting argument going on at the moment between Jeff Jarvis and Dan Conover – two people who I think are both taking great approaches to these problems. You should read their original posts, but here is an oversimplified summary: As I’ve discussed before, Jarvis is undertaking an interesting project where his group has constructed a number of new business models for news that use new revenue generation mechanisms to support journalism profitably. Conover criticises these models as not being radical enough – he is taking the Kevin Kelly approach and trying to come to grips with how to deal with news in the context of radical change in the business environment. Like this:
Jarvis responded by saying that his emphasis is on what will work now, and Conover’s reply is that he’s not so interested in that.
There are several crucial innovation lessons that come out of this. The first is that they are talking about two different time horizons, and news organisations actually have to be thinking about both. To frame this issue, John and I use the three horizons framework. With this tool, 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. Organisations need to have innovation activities taking place across all three time horizons. If you ignore horizon 1, you don’t stay in business, so all your grand plans will never be executed. On the other hand, if you ignore horizons 2 & 3, you’ll get replaced by someone that comes up with something new and better. Jarvis is looking at horizon 1/horizon 2 type solutions, and Conover is looking at horizon 3. The issue is that it is not an either/or choice – we need to be thinking about both.
The second issue this brings up is that of time. People often make the mistake of framing horizon 3 ideas as those which are ’5-10 years away’ – if you do this, horizon 3 never arrives. The difficulty for news right now is that because they’ve ignored horizon 1 innovation for the past 15 (50?) years, the time scale has compressed. Suddenly we’re at a point where we need to be trying everything, all at once. Innovation is an evolutionary process – as Conover says:
Evolution applies positive feedback in that the more capable methods resulting from one stage of evolutionary progress are used to create the next stage. Each epoch of evolution has progressed more rapidly by building on the products of the previous stage.
This means that we don’t resolve their argument through discourse – we resolve it through trying out every idea that might work, then amplifying the ones that do.
The final point is that I think that Conover’s alternate model is actually really good. His emphasis is on building a new data structure for news, which is a great idea. Here’s the summary:
But in my 2010 example, the structure of this information is the news organization’s primary product. Yes, the story is “given away” both in print and online (a misnomer: the news industry has ALWAYS given away news — it’s a loss-leader that supports our core business: renting your attention to advertisers). But the semi-structured data set that comprises the totality of the news organization’s reporting has intrinsic commercial value to any person or entity that benefits from relevant, useful information.
This is an aggregate, filter and connect strategy, which is exactly what I think is called for here. In this model, value is created by aggregating the news into one location, filtering it through extensive meta-tagging, and connecting the information up in economically valuable ways.
Fifteen years ago this would have seemed so radical that it would never be considered. Now, I think it has to be tried. News needs a new business model – and there will probably be several that end up working. Time to start experimenting so we can figure out what they might be.
Three Informal Project Milestones
How can you tell how well a project is going? Many types of projects have formal milestones that help you track your progress, and there are many tools available that enable project management. However, in our research group meeting yesterday, I realised that there are some informal project milestones that are just as important to track.
In our meeting we were reviewing the year and setting some goals for next year. As we were talking, it occurred to me that several of the accomplishements of my students actually represent milestones that must be passed on any major project.
The three big informal project milestones are:
- Explaining the project to your partner: This is actually a huge one for any big project. Can you explain what you’re doing to someone who is probably reasonably smart, but doesn’t know anything about what you do? When I was an industrial water treatment consultant, my manager used to say that if I couldn’t explain how water treatment worked to the guys in the plants, I didn’t understand it myself. This was a bit of a challenge, because water treatment involved pretty complex chemical processes, and the guys (they were always guys) in the plants usually didn’t have more than a high school education, and English was often their second language. In time, however, I learned that he was right – if you can’t explain what you’re doing to someone who is interested in it, then you don’t understand it yourself. This is true whether you’re doing water treatment, a PhD on how people search for knowledge within a firm, or developing a new piece of software.
- Eliminating Distractions: Long projects are hard because, well, they take a long time to complete. If you’re starting a PhD, you have probably at least three years of work ahead of you. If you’re designing a industrial plant, you’ve got about the same. On long projects it’s easy to get distracted by things – especially things that might have a quick payback. It’s ok to take on side projects occasionally, but usually, if you’re going to finish the big project, you have to stay focused on it.
- Finding an endpoint: This one is critical – how do you know when you’re done? Does your PhD need one more empirical chapter? Does your software need one more feature? When we work on long projects, we often end up defining ourselves by the work we’re doing. This can make stopping difficult sometimes. It’s essential that you have an actual endpoint in mind, and that when you reach it, you stop.
Long projects are challenging. They take patience, focus and perserverance to complete, but finishing them is incredibly rewarding. It’s good to plan out how you’re going to finish a big project, and I think that these are three milestones that need to be added to your critical path: be able to explain what you’re doing (and why), get rid of the distracting side projects, and figure out how you’ll know when you’re done.
Related post: How to Finish Your PhD
(photo from flickr/sleepymyf – CC licensed)
Network Analysis Resources
I have run across a few useful resources for network analysis recently, including:
- Mini-Course on Networks: Howard Rheingold has put together a very nice mini-course on networks and network analysis. It includes two videos where he describes the history of networks and some of the basic concepts of network analysis, links to other videos, a good bibliography for network analysis, and links to other network resources.
- Measuring Online Influence: A post by Allison Fine looking at ways that we can measure influence online – she incorporates some ideas from Valdis Krebs, the key one being that it is not simply our direct connections that are important, but the indirect ones.
- Networks, Crowds and Markets: The upcoming book by David Easley and Jon Kleinberg is available for free download as a pre-publication draft. Here is how they describe it:
Over the past decade there has been a growing public fascination with the complex “connectedness” of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else.
Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior. Drawing on ideas from economics, sociology, computing and information science, and applied mathematics, it describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.
It looks like a terrific resource from two of the best network researchers around. (thanks to Michael Martin for the pointer)
If you want to learn more about the importance of networks and how network analysis works, this is a great set of resources to start with!
The Problem with Measuring Innovation
Posted by Tim in aggregate, connect, filter, innovation strategy on 7 December 2009
The problem with measuring innovation is that you can’t measure innovation. This makes it a difficult thing to manage.
Now obviously, organisations figure out ways to measure how innovative they are – but they usually doing it by finding metrics that approximate some part of the innovation process. The fact that our metrics are all proxies leads to problems when we forget that they are only substitutes for what we are really interested in measuring, not the thing itself.
I ran across an example of this split between the measure and what we care about today when I was looking at twitalyzer. It’s a really nice free twitter analytics site that measures several things: your influence, your signal to noise ratio, your generosity (how frequently you cite other people), your velocity (how frequently you tweet) and your clout (ability to spur people to action). Influence is a composite measure that includes your clout, velocity and generosity, plus how often you are retweeted, and the number of followers that you have on twitter. It’s the last part of the measure that leads to potential trouble.
Getting followers on twitter is interesting. One strategy is to post interesting stuff for an extended period of time, and letting people find you (generosity, signal/noise and velocity all help with this). The problem with this is that it takes time. A faster way is to be famous. However, that’s not so easy. The easiest way to get followers is to follow a whole lot of people, and count on the web’s tendency towards reciprocity to work in your favour.
The problem with this is that you end up following a lot of people, which for me at least creates some difficulties. For me, twitter is a great information stream – it is part of my aggregating strategy. It helps me find innovation news and viewpoints that I don’t pick up in my rss feed from blogs. The problem with following lots of people is that this makes filtering impossible. There are tools that make it easier to manage large numbers of people that you follow – but they all work on the same principle – ignore most of them. Which means that if I do that, I’m not connecting.
My twitter strategy is to use it as part of my larger aggregate, filter & connect strategy. But if I try to manage the metric – ‘influence’ – I have to collect a lot of followers, which actually makes it harder to execute my strategy. This illustrates the problem with mistaking a metric like influence with what you actually want to accomplish.
The same thing happens when we manage innovation. One of the most common measures of innovativeness is patents. But no one actually needs patents – they need the things that patents somtimes provide – a competitive edge from exclusivity, monopoly profits, or the development of unique products. If we spend too much time managing the metric, we might not achieve the outcomes (profits, market share, etc.) that we really want.
How can we fix this? There are few things we can do:
- Don’t mistake the metrics for the thing we really care about – constantly remind yourself that since we can’t measure innovation directly, the metrics that we use are approximations, not the actual thing that we care about.
- Use multiple metrics – one way around this problem is to use multiple measures for innovation. There are many possible measures – how much we’re investing in generating ideas, how many new products/services/process improvements we actually introduce, profits from new products/services, senior management time devoted to innovation, innovation porftolio balance (both distribution of innovation efforts over time scales, and across the incremental-radical spectrum), and so on. Scott Anthony has done some outstanding work in this area, which he summarises here.
- Make sure that your innovation metrics are tied to your strategy – Think about what you are trying to accomplish, and make sure that your metrics measure actions that will contribute to the outcomes that you are trying to achieve.
Measuring innovation is one of the hardest parts of managing innovation. Avoid the trap of thinking that your innovation metrics measure innovation directly to make the process a little bit easier.
(picture from flickr/aussiegall – CC licensed)
What are Innovation Networks, and Why Should You Care?
Posted by Tim in innovation, networks on 6 December 2009
I did some media training last week, which was interesting. In the course of the morning, I had to think about the main messages I would like to communicate to people about my research. The key one is that the networks that people form within innovating groups have an enormous impact on how successfully the groups innovate. Consequently, if you are trying to manage innovation within an organisation, you will be able to do this more effectively if you understand the networks.
What do I mean by networks? We use social network analysis, which looks at the ways that people connect with each other. People are the nodes within the network, and the connections can be formed in a number of ways. We tend to look at knowledge-sharing connections. So we ask questions like:
- Who do you regularly interact with?
- Who gives you information that you need to do your job?
- Who is a source of new ideas for you?
- Who give you help when you need to solve problem related to your work?
When we get most of the people within a group to answer these questions, then we can map out the connections between people to see what the network looks like. The figure below is a problem-solving network. This is a group of 130 people who answered the last question on this list.
There are several things that you can tell just by looking at the diagram. The first is that there are many people that aren’t in the network at all – they have no connections. The second is that there is a geographical split – the red dots are people in one location, while the green ones represent people in an office on the other side of the country. The split between the two groups is obvious. The third issue is harder to tell just from looking at the diagram, but the people that connect the two regional groups together tend to be managers (bigger circles). All of these issues have the potential to restrict the flow of information within the group as a whole.
All of these observations reflect the structure of the network (which is usually measured stastically, rather than just looking at the pictures – to learn more about that, check out this terrific introduction to networks from Howare Rheingold). The potential problems within this group suggest that it might not be as innovative as a team with a more coherent structure. When we showed these results to the managers of the team, they were anxious to take steps to improve the flow of knowledge. Some of the steps that you can take are described in this review of the latest book by Rob Cross:
Network analysis can also help when you bring employees together on project teams. Too often, certain voices have the leader’s ear, but a network analysis that maps information flow and problem-solving collaborations may reveal certain experts on the team need to be given a greater voice in decision-making.
The more innovation networks that we map, the more we learn about the impact that network structures have on innovation performance. So that’s a quick recap of what innovation networks are, and why analysing them might help make an organisation more innovative.
How to Deal with Complexity
Posted by Tim in aggregate, complex systems, connect, filter on 4 December 2009
Is google making us stupid? No. We keep hearing the argument that relying on technology makes us less smart somehow. Plato was probably the first person to make this argument. His target? Writing – his argument was:
So, too, with written words: you might think they spoke as though they made sense, but if you ask them anything about what they are saying, if you wish an explanation, they go on telling you the same thing, over and over forever. Once a thing is put in writing, it rolls about all over the place, falling into the hands of those who have no concern with it just as easily as under the notice of those who comprehend; it has no notion of whom to address or whom to avoid.
Plato’s suggestion was that we learned best through discourse, and that writing would, well, make us stupid. I’m clearly unqualified to call Plato dumb, but it’s a dumb argument. Here’s the latest version from Steven DeMaio:
Studies have shown that using our memory improves reasoning and creativity. Yet, because of our increased reliance on technology, few of us can even recall phone numbers or appointments anymore. Try using your memory more often by dialing numbers by hand or picturing your weekly calendar in your mind.
This line of argument drives me up the wall. You can see the faulty assumption here – that if we’re not remembering phone numbers, then we’re not using our memory. It’s as though we have one part of brain that is set aside only for remembering phone numbers, and if we’re not memorising phone numbers,then we’re not using that part of our brain. That’s clearly not true. The problem is not whether we’re using our memories or not, the problem is in allocating our attention and memory correctly.
DeMaio actually gets to this point in the longer version of the article – he talks about the benefits of memorising the names of all of his students. I agree that this is a very good use of memory. But there’s a lot of stuff that I’m better off leaving to my distributed memory, much of which is aided by technology. This is how we are able to deal with the rapidly increasing amount of information that we are faced with these days (beautifully documented and discussed in this post by Venessa Miemis).
The key to dealing with all of this information is to outsource as much of the aggregating as you possibly can. My phone can remember phone numbers. Wikipedia can remember when the Magna Carta was signed. My twitter network can remember all the great stuff going on at the Open Innovation Summit right now in Orlando. All I need to know is how to access the information (and how to back it up).
Doing that lets me concentrate on the things that I’m good at – filtering and connecting. We don’t get new ideas by memorising. We get new ideas by making new connections – figuring out what information is important, and synthesising it. One of the reasons that information is increasing exponentially is that we’re getting better at processing it. This is due to the extra brain time that we’re able to free up by outsourcing memorising.
By letting us focus our concentration on making new connections, technology that remembers for us makes us smarter.
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Metrics
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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