Tag: featured

Innovation Requires a Change in Behaviour
What is the most important innovation ever?

I’ve argued before that it is hand-washing in hospitals. This innovation was a major driver in the improved health outcomes that have increased our life expectancies from less than 60 years at birth to nearly 80 in most developed countries.

It’s such a simple idea, and so easy to do, that it must have spread quickly, right?

Well, not really.

Oliver Wendell Holmes in the 1840s was one of the first people to suggest that hand-washing could reduce infections. Not many people paid attention.

One who did was Ignaz Semmelweis. He collected data in maternity wards in the 1850s which showed that hand washing reduced infection rates from nearly 15% to less than 1%. What more proof would you need than that to start doing it yourself?

A lot, apparently. Semmelweis was ridiculed for his suggestion that hand washing reduced infections. Why? Because he couldn’t explain why it worked, he could only prove that it did. He was hounded out of two different jobs for promoting his views on hand washing, and he died in an asylum of, ironically, a serious infection.

It took more than twenty years to clearly identify the mechanism that Semmelweis needed – germs. The work of John Snow, Louis Pasteur and Richard Koch established the germ theory of disease by the late 1860s.

So now that we had a theory to support Semmelweis’ data it should be clear sailing, right? Guess again.

Joseph Lister built on Pasteur’s work in particular to develop techniques for aseptic surgery in the 1870s.

His approach was widely adopted….. fifty years later.

There was about a seventy year gap between Semmelweis proving that hand washing saves lives until the practice was widely accepted. Even today, in many hospitals less than half of the health care practitioners follow the right procedures for hand washing.

What makes it so hard to wash your hands before touching a patient?

The big challenge is that hand washing requires people to change their behaviour. Worse, it requires people to break their habits – something that Charles Duhigg talks about in his excellent new book The Power of Habit: Why We Do What We Do in Life and Business.

Duhigg explains how habits work:

Hand washing was resisted for many reasons. One is that it suggests that doctors and nurses are the cause of infections and harm – and this doesn’t fit with how they view their jobs at all. Another is that it takes time, and time is often in extremely short supply in hospitals. A third is that it just seemed too simple. Medicine is complex, it requires years of training to practice effectively, and significant expertise to practice well. It’s built for complexity, not simplicity.

Consequently, hand washing can be tough sell, even though everyone knows now that it works, and why.

There are some important innovation lessons in the story of the slow adoption of hand washing:
- Innovations are often ideas, not things. The breakthrough here was the idea – the germ theory of disease, and then the application of the idea in practice. We tend to think of breakthroughs as things – the first car, a rocket that takes people to the moon, or an iPhone. But as Hugh MacLeod says:
  
  Products are idea amplifiers. The molecules and/or bytes are secondary.
- Ideas spread much more slowly than we expect. Innovators tend to be pretty smart, and one of the most common mistakes that smart people make is to expect great ideas to be self-evidently good. This is never true. It wasn’t enough to show that hand washing saves lives. It took seventy years of effort to get people to adopt the practice. As Howard Aiken said:
  
  Don’t worry about people stealing an idea. If it’s original, you will have to ram it down their throats.
- Innovation is always about changing behaviour. If you’re selling a new widget, you have to get people to switch from their old widgets. If you have a new way of doing things, you have to get people to abandon the old ways. If you have a new idea, you often have to kill off an old way of thinking. You have to break connections to get your ideas to spread. In all cases, this requires people to change the way that they act.
This is why Duhigg’s work on habits is so important. He has a great set of steps to follow in this pdf excerpt from the book on rules for changing habits. He also has many examples of how firms have gotten their customers to change their habits.

If you are innovating, you must think about this. During an analysis of a recent project, we concluded that the fundamental problem in the organisation was political. We went in thinking our task was to come up with a new business model for one of the units in the firm. But really, it was to come up with a new business model, and also a way to sell it.

Because the innovative idea is worthless if it doesn’t change the way that people act.

We re-learned Ignaz Semmelweis’s big lesson – innovation always requires a change in behaviour.
11 June 2012
Procter & Gamble – Using Open Innovation to Become a World Class Innovator
Note: This is part of a series of posts discussing The Innovation Matrix. See this post for a description of the full model and what can be done with it.

We can use The Innovation Matrix to help us understand how the innovation capability of firms evolves over time. A great case study in this regard is Procter & Gamble. Starting from the late 1990s, this is the path that they’ve travelled:

In the late 1990s, their innovation program had lost its way. Successful product innovation was at the centre of their competitive strategy, but their performance had been slipping. P&G had reviewed their Research & Development strategy and increased their budget for the five years leading up to 1999, even though they already had one of the largest R&D budgets in the world.

The problem was that the increased R&D spend didn’t improve their performance – a classic case of Innovation Commitment increasing without an equivalent increase in Innovation Competence.

By 1999, R&D expenditure as a proportion of sales had increased from around 4% to nearly 7%, new product success rates were stuck at 35%, P&G had developed a huge collection of patents, but fewer than 10% of them were being used in actual products. The last one is the fact that still blows me away. The outcome of this was a drop in the P&G stock price from $118 per share to $52.

At this point, P&G was Bewildered. They were sinking a huge amount of resources into innovation, but they were not getting a very good return at all on this investment.

Their response to this state of affairs is well documented now – they initiated the Connect & Develop program, which was designed to use open innovation to improve their innovation outcomes.

The interesting thing here is that this wasn’t just another increase in Innovation Commitment – P&G’s first move was actually to decrease their innovation infrastructure. They significantly reduced their R&D spend, they changed their innovation metrics and they cut back on activities that weren’t leading to the kinds of outcomes they needed.

The next step was to use Connect & Develop to get more ideas out into the world – one of their major weaknesses previously. As they did this, they moved into the Fit for Purpose category. P&G got better at executing ideas, and they were learning about how to use their resources more effectively within the new open innovation approach.

The outcome of that learning was a diagonal move into the World Class Innovator category. Once they consolidated their learning, they reinvested money into innovation and improved their idea selection process, increasing their Innovation Commitment. They also started to come up with breakthrough ideas again – such as Swiffer. Their Innovation Competence took another jump forward.

In an interview with Stefan Lindegaard, Chris Thoen of P&G outlines some of the outcomes from the Connect & Develop initiative. Some of these stats come from him, while others come from other sources:
- They have extensive research networks (both proprietary and open ones) that that regularly lead to the development of new ideas.
- The percentage of patents in use in products has increased from less than 10% to better than 50%.
- Their new product success rate has increased from 35% to better than 50%.
- The percentage of new P&G products that include elements developed outside of the firm has increased from 15% to over 35%
The end result is that Procter & Gamble is now considered to be one of the most innovative companies around, and is certainly a world leader in using open innovation.

This case illustrates a couple of important points about The Innovation Matrix:
- You can’t become a World Class Innovator in one jump. The dangerous thing about a lot of the case studies that we hear about is that they often make it sound as though highly innovative firms were either born that way, or that they were improved their innovation capability very rapidly to become world class. Neither is true. Becoming a World Class Innovator is always a process – it takes time and effort, and multiple steps.
  If things go well, you can move up diagonally – when an increase in support for innovation is matched by an increase in innovation outputs – the ideal outcome. Nevertheless, it will require the patience and focus to make multiple moves to improve.
- If you’re Bewildered, a step backwards can help. When things aren’t going well, it doesn’t make sense to just increase what you’re currently doing. Instead of spending more again on R&D, Procter & Gamble instead cut it. What they realised was that they were not effective all the way through the idea management process.
  
  They were generating TONS of ideas in 1999 – they had one of the largest patent pools in the world at the time. The places they had difficulty were in selection and diffusion. They would only pursue new product ideas with the potential to be $1 billion businesses – so they cut a lot of promising ideas. Connect & Develop enabled them to bring these ideas to market in collaboration with partners that better equipped to deal with the relatively smaller returns.
  
  This also led to more experimentation – no one expected Swiffer to become as big as it did. This is an idea that almost certainly would have been patented but then killed in the old system. These moves improved both their selection and their diffusion processes.
  
  None of this would have been possible without that first backward step.
I’m going to work on more case studies like this, because it’s critical to gain an understanding of how firms move through The Innovation Matrix. Such an understanding will help us figure out the best routes to take, and the best tools to use in different circumstances.

The value of that first step backwards if you’re Bewildered never occurred to me until I started to think about the Procter & Gamble case in more detail.

Stay tuned for more thoughts about how to use The Innovation Matrix to improve your innovation capability.

Note: most of the facts here came from two papers on P&G – one by my colleagues Mark Dodgson, David Gann and Ammon Salter, and the other by P&G employees Larry Huston and Nabil Sakkab.
30 May 2012
Why You Should Care About Network Structure
When carbon atoms connect, they most commonly form molecules built on rings of six atoms. The things that are built out of these six atom rings of carbon are amazingly diverse.

Here are the structures of two of these things: graphite (A) and diamond (B):

You can see the rings in both. Same material, same basic building block, very different materials. Why? Because of their structure.

I ran across this example in Howard Rheingold’s new book Net Smart: How to Thrive Online.The book is great, and I’ll talk more about it soon.

In the chapter on building network skills, Rheingold includes a quote from Nicholas Christakis, from this talk:

One of the key ideas about human social networks is that in the addition of ties between people and specific patterns of ties that obey particular mathematical rules the whole becomes greater than the sum of its parts. The collection of human beings have properties that do not reside within the individuals, and this collection of human beings is now able to do things that they previously were not able to do. And one of the illustrations or examples that I most like to give about this is something that most people are familiar with from high school or college chemistry and that is the example of carbon. So you can take carbon atoms and you can assemble the carbon atoms into graphite and here we put particular hexagonal pattern of ties and you get sheets of graphite and this graphite is soft and dark. Or we can take the same carbon atoms and assemble them differently into a kind of a perimetral structure with the ties between them, the bonds between the carbon atoms and we get diamond, which is hard and clear and these properties of softness and darkness or hardness and clearness first of all differ dramatically, not because the carbon is different. The carbon is the same in both, but rather because of the ties between the carbon atoms. And second these properties are not properties of the carbon atoms. They’re properties of the group, properties of the collection of carbon atoms. Therefore, when we take constituent elements and assemble them to a larger whole, this larger whole can have properties that we could not have foreseen merely by studying the individual elements and properties which do not reside within the individual elements.

What does this mean for us? It means that our network structure is very important. The value of your network is not just determined by who you’re connected to, or how many people you are connected to, but also (and mainly) by the structure of these connections.

You can have the same number of connections to the same people in two different networks, and one can be like graphite, while the other is like diamonds. This has some practical implications for innovators:
- Your network can be too connected. When I talk with managers about the networks that John and I have mapped, and how their structures are (often) not very good for information sharing, their first inclination it to try to connect everyone up with everyone else.
  This is not a good idea. When they say this, I reply with: “Imagine if you had to read every single email sent and received by every other person in your organisation. That’s what you get when you connect everyone up.”
  
  Most interpersonal networks work best when they have somewhere between 3 and 10% of the total number of connections that they could have if everyone was connected to everyone. This leads to the best structures for sharing information, which is critical in getting your new ideas to spread.
- Strong Networks are Diverse. There’s no point in connecting up only with people that think the same way that you do. If you do this, you’ll just keep getting the same old ideas. You need to build links to people that are interested in different things that you are, to people that know different people than you do, and to people that view the world through a different lens than you do. That’s the best way to generate innovative new ideas.
- Connecting people that aren’t already connected to each other is very powerful. Check out this more detailed discussion of this idea. The basic principle is that if you build the network by connecting people to each other, you make the network stronger. You give up a little bit of power, because if they stay unconnected you can act as a broker. In exchange, you gain reputation and social capital.
  Again, this helps you get your own ideas to spread.
The last point is important – when you start thinking in network terms, you start to realise that reputation and social capital are the main currencies in networks, not power. This is a critical insight. Once you understand this, you can start to build a network that fits your needs.

Graphite and diamond are two very different materials, and they fill different roles. If you’re trying to communicate by leaving marks on a piece of paper, graphite will be much more useful to you than a diamond. One of the things that John and I are learning in our research is that different network structures serve different purposes.

The network that is best for idea generation often isn’t best for idea execution. But just as graphite and diamond are made from the same thing, so are your networks: people and connections. And the structures that they form have a big impact on how you perform.

Note: Here’s a video that Rheingold made to explain the network issues he addresses in the book:
13 May 2012
The Innovation Matrix Reloaded, Again
I’ve continued to test out the ideas behind The Innovation Matrix with senior managers, and it seems to be working its way towards becoming a useful tool. As I do this, it continues to evolve.

Today, I am going to revisit The Innovation Matrix as a broad concept, then over the next couple of weeks I will add posts that talk about each component specifically. This added detail should help to flesh out the tool.

Here is the latest version of The Innovation Matrix:

This is a bit of a distillation of observations over time. I thought of it because I think that a lot of people that are trying to improve innovation within an organisation think that they can go from the bottom left (No Innovation Capability) to the top right (World Class Innovator) in one jump, simply by introducing some sort of innovation program.

This is impossible – you actually have to make the trip in a number of steps, and there are many different paths that you can take.

The table has two increasing dimensions. Across the horizontal axis there is increasing Innovation Commitment. This can include things like talking about how innovation is important, including it as a core value, putting in systems to support and improve innovation, and explicitly earmarking time, money and other resources to innovation. This is measuring innovation inputs. You can also think of this as top-down innovation initiatives.

Going up the vertical axis shows an increase in Innovation Competence – mainly the ability to generate and successfully execute new ideas. This can include things like the actual number and nature of innovations that are implemented, the organisation’s effectiveness across all phases of the idea management process, the breadth of innovations, and outputs across an innovation portfolio. This measures innovation outputs – and it is all about execution.

Here is a brief description of each box:
1. Not Innovating Very Much: these firms don’t innovate. This isn’t necessarily bad – there’s no value judgment being made. They can be successful if they have strong positions in stable industries, or they can be average performers or struggling in other circumstances. I think we can probably all think of examples for this category.
2. Thinking About Innovation: firms in this category are starting to talk about the importance of innovation. They might add it to their list of core values, or have a CEO that is starting to talk it up. Regardless of this increase in awareness and commitment, they are still not very good at it. This is often the first step that organisations take in trying to improve innovation.
3. Bewildered: the primary features of firms here are confusion and frustration. They are talking the talk, with official innovation programs, commitment of time and resources, etc. But they’re still lousy at actually executing ideas. They may have an excessive focus on ideation, a bad selection process, or just not be very good at executing.
4. Accidental Innovators: These would be firms that innovate under some other name – so they might be really good at process innovations through a continuous improvement or lean program. They are able to execute ideas reasonably well, but they don’t have any structure in place to support it, nor do they think that they’re innovative. They innovate through stealth. Many startups operate in this space too.
5. Fit for Purpose: these firms have some structure in place to support innovation, and they are getting better at doing it. In many industries, this is the baseline level of innovation needed to stay in the game over the long run. Several firms that I work with have gotten to this level after moving first to Talking About Innovation.
6. Potential Stars: these firms are good at innovating, and they are putting more resources into getting better at it. They have top-level commitment to innovation, good processes in place, and dedicated resources for innovation. They are reasonably good at executing new ideas and have the potential to become extremely good.
7. Unicorns: the problem with making a matrix is that you have to put something into every box, even if it’s mythical.
8. Stars (at risk): this might seem like the perfect place to be – very good at executing new ideas, but with less structure. These firms aren’t sinking huge amounts of resources into the process, but they are consciously trying to innovate. The risk is that because they lack full commitment to innovation, it might not become systematized, and their performance could drop.
9. World Class Innovators: another self-explanatory category. In these firms innovation is deeply embedded in the culture – everything is oriented around innovation. Think Google, Apple, 3M, Procter & Gamble etc.
How to use this:

Here are some things that I think we can do with this:
- Use it to make a better model of how firms improve at innovation: Many of the people I work with are in firms towards the bottom left, and many of the examples that we use to illustrate points are from firms in the top right (Google, P&G, 3M, etc.). This might be too big a conceptual jump. Not every firm can get to the top right, and neither should every firm aim to. It is more productive to think of this as an incremental process of steps, rather than one big jump.
- Track the evolution of firms: we can learn about how to best manage innovation by tracking how firms progress through this matrix. For example, one firm I work with started by Not Innovating Very Much, then started talking about it and moved to Thinking About Innovation, and now that they are getting better at it they are Fit for Purpose. Tracking these trajectories will give us a better idea of which paths work, and which are riskier. See the case study links at the bottom of this post for examples.
- Realise that there are multiple targets to shoot for: Like I said, not every organisation can be Google. Thinking about innovation with this matrix, you can see that all of the categories in the top row are excellent at innovation. However, the farther you go to the right, the more resources you have to commit to build and maintain this level of excellence. There are many situations where you can try to be an excellent innovator with a more bottom-up, less resource-intensive system in place. Also, there are many cases where it is fine to be Fit for Purpose. Your differentiation doesn’t come directly from innovation, so you just need to be good enough.
- Think About the Best Path to Follow: Almost everyone starts by increasing commitment. The danger with this is that you can end up Bewildered. I wonder if we should be figuring out ways to improve capability rather than commitment. Or is this even possible? It’s an interesting question, and you can certainly make a strong argument in favour of increasing capability before you increase how much you talk about innovating.
The main point with The Innovation Matrix is that improving your innovation performance is a journey of many steps, not simply one big leap. The matrix is designed to help us think about this more accurately, and to be more successful at improving our innovation performance.

If you have any thoughts on this, we’d love to hear them.

Case Studies:
- Comet Coffee – a good example of a startup’s path
- Procter & Gamble – from Bewildered to World Class
1 April 2012
Want Old Ideas? … Then Keep Talking to Your Friends

If you have been reading this blog for a while you will know that a lot of the research work that Tim and I do looks at the link between networks and innovation. When we talk about networks, we mean all sort of ways that people and businesses can connect to each other. For example, we have studied networks of project engineers, a virtual network within a multinational corporation, networks of entrepreneurs in Taiwan, and networks of academics – just to mention a few. Tim’s doctoral thesis was on the evolution of the world trade network and that had some important implications for free trade agreements. We think that networks are a really cool thing to study!

When we do this research we try to link network structures to outcomes. In the innovation context this means that we want to know what network structures are associated with better innovation performance. This morning Tim sent me a link to an article in Slate Magazine that summarizes a famous research study of networks and creativity in the Broadway musical industry. The main finding is that a particular class of network stucture called a ‘small world’ is correlated with the appearance of blockbuster musicals. When this structure appears in the networks of scriptwriters, choreograhpers and librettists, then it is more likely that a highly successful musical will be produced.

Small-world networks have a particular signature of clusters and sparse links between the clusters. They are called small worlds because the handful of links between the clusters create shortcuts between anyone in the network. The general idea is that this makes it easier for new ideas to flow within the network.

One obvious question to ask is why doesn’t a network with more links between the clusters work even better? In the Broadway study they found that improving the connectedness of the network with more shortcuts between clusters resulted in less musical success rather than more, so what is going on here?

My explanation comes from another classic network study that is over forty years old. In a famous paper called “The Strength of Weak Ties” Mark Granovetter highlighted the importance of weak network links. This was demonstrated in his study of how people find a job. It wasn’t the strong connections that were the source of information about a new job, it was actually the weak connections to people you don’t know very well that were the most common source of an opportunity.

When you think about it, this makes perfect sense. Tight networks circulate existing information. It is unlikely that you will get anything new out of your closest friends. You know them and what they know, and vice versa. It’s the bridges into unfamilliar networks that are the better sources of opportunities and ideas. In terms of the broadway musical study, this means that better connected networks don’t necessarily result in more innovative musicals.

This also has a very practical implication for our own networks. Are you in a densely connected cluster? How can you get out and make just a few random connections?

Note: Small-world network image is from Six Degrees by Duncan Watts.

26 March 2012
Eight Models of Business Models, & Why They’re Important
The term Business Model is one that gets thrown around a lot these days. Even though it might sound like a buzzword to you, it’s important to understand what a business model is, and how they are useful.

One of the confusing things about the business model concept is that there are a wide variety of models of business models, and it seems as though everyone that talks about them makes up a new one. This can be frustrating if you are trying to figure out how to use the concept.

At their core, all business models address this questions: how do we sustainably deliver value to our customers? In this instance, the sustainable part refers to your organisation – how can you deliver value so that you’re still around in the future?

In a special issue of the journal Long Range Planning, Charles Baden-Fuller and Mary Morgan say that business models can serve three different purposes. They can describe different kinds and types of businesses. This is critical if we are trying to study them analytically. They can be short-hand descriptions of how firms operate – the primary value here is that you can use the business model to ensure that you have strategic fit across activities. Or they can be role models – you can use them to describe how you want your organisation to function.

More recently, Steve Blank has added another use – he says that business models are hypotheses about how your organisation might be able to create value for customers (see my discussion of this here).

To help illustrate some of the important points about business models, here are some of the models of business models that I’ve run across. The list isn’t comprehensive, so I apologise to anyone that I’ve forgotten – it’s simply due to my ignorance.
1. Value Networks from Verna Allee: Verna was working with some of the basic concepts of business models in the 90s. One of the tools that she developed is Value Network Mapping:
  
  Key points: value creation and exchange is at the core of understanding business models. You need to clearly articulate how you create value, and for whom. The other key point here is that value isn’t just about money. You can also create and exchange intangible value. You can see her latest work here in her book Value Networks and the True Nature of Collaboration.
2. Henry Chesbrough: described business models in an article with Richard Rosenbloom and in his book Open Innovation. Here is what his business model looks like:
  
  Key points: new innovations often require new business models. This is where the idea of business model innovation really started to gain traction. Chesbrough didn’t just describe business models, he also discussed how changing a business model can be an innovation just by itself. I’m beginning to suspect that all new innovations require new business models…
3. Strategy Diamond: this is a strategy tool developed by Hambrick & Fredrickson. They talk about the importance of having an integrated strategy, which looks like this:
  
  Key points: the first key point here is that a good business model is integrated. All of the elements need to be consistent with and support the others. If you change one element, it’s likely that you’ll need to change all of them. Second, this model illustrates how closely linked strategy and business models are. When you design a business model, you can’t do it without clearly articulating a strategy.
4. Patrick Staehler: wrote a PhD called Business Models in the Digital Economy that was published in 2001. His business model looks like this:
  
  Key points: the thing I like best about Staehler’s model are the three bottom boxes: Leadership Style, Relationship Style and Values. Think about that in relation to the point above about integration. If you change the relationship style within your organisation, you’ll likely need to change the rest of your business model as well. Furthermore, this business model innovation could be a source of competitive advantage. This is a very powerful point.
5. Business Model Canvas: around the same time that Staehler was writing his PhD on business models, Alex Osterwalder as also writing a PhD on business model innovation. He developed a tool called the Business Model Canvas. He has subsequently published a book called Business Model Generation, which is all over the place now, along with a number of other analytical tools. Here is his version, as modified by Steve Blank:
  
  Key points: this is where the business model concept has started to go mainstream – it’s astonishing how well this version of business models is doing right now. Osterwalder has done a great job of promoting the idea, and making it genuinely useful. This version of business models proves that it is a practical tool that you can use to figure out where your organisation should be heading.
6. Long Range Planning: the special issue mentioned above makes a couple of important contributions. There is a new model of business models in the paper by David Teece, but it is more of a model to use in description if you are trying to study these academically. It’s not really one that you could use very easily within a firm for analysis.
  Key points: the issue with the Teece model illustrates the point that Baden-Fuller and Morgan make about the different uses of the business model concept. Teece’s model is designed solely for description/classification. So you can run into approaches for business models that aren’t as practical. The second point in the special issue is this: about 2/3 of businesses surveyed in one of the papers can’t articulate what their business model really is. This is alarming. It also raises the point that every organisation has a business model, whether you have consciously thought about it or not. If you’re trying to develop business strategy, it is essential to actually give this some thought.
7. Seizing the White Space: Mark Johnson works with Clayton Christensen, and Johnson’s book from last year has another model of business models. The website has a bunch of useful resources, and the book has some great stories about business model innovation. His model looks like this:
  
  Key points: so now we have models of business models with 4,5,6,9 and 12 components. The same core elements keep turning up. For me, I don’t care which business model version you use, and picking the right one depends on what you’re trying to accomplish. Personally, I like the Chesbrough version because of the emphasis on networks, which I think is critical. On the other hand, the Business Model Canvas is getting easier to use now because of the substantial amount of resources that are building up around it.
  
  People build their own model for different reasons, but it’s important to understand that they are all trying to find ways to get at essentially the same issues. There isn’t one that is absolutely correct. So pick whichever one resonates the most with you to use.
8. Escape Velocity: the latest book by Geoffrey Moore is fantastic. In it he includes a 9-point Market Strategy Framework, which includes elements like Target Customer, Compelling Reason to Buy, Partners and Allies, etc. If you look at it, it’s outlining a business model.
  Key points: like I said earlier, any time you start thinking about strategy, you’re thinking about business models. So even frameworks that aren’t being put forward as business models really are business models.
16 January 2012
Innovation is the Process of Idea Management
Here are a number of actual quotes from this week. On Monday, Mark and I had a research meeting with a colleague in the business school who does business process research. When Mark asked if she has ever looked at innovation, she said:

I haven’t, because innovation isn’t a process, is it?

After a bit of further discussion, we all agreed that it was, and that we could possibly do some productive work together.

Then I had a stimulating talk with Paul Hodgson, the Queensland Director of the Enterprise Connect program (a great initiative). We talked about the series of talks that I gave for the Australian Industry Group last month, where fewer than 2% of the people attending had systems in place to manage innovation. We concluded that this is an area that requires a lot of effort to improve.

Next I got an email from one of my industry contacts who is trying to implement a new innovation program in his organisation, which said:

Our CEO has basically thrown down the challenge: “Why would I want to introduce an innovation and ideas management framework in XXX – How would we benefit?”

This morning Mark told me about a talk he had with one of Australia’s most accomplished executives, who told him about a trip she made recently to Silicon Valley – this is how he summarised their talk:

She visited firms like Google, and Apple and Cisco, and she said that innovation was so deeply embedded in all of them that they never thought twice about it. They spend more time thinking about their place in the value network, and in building their innovation ecosystem. They are very aware of where their great ideas come from.

Finally, John and I talked about recent conversations that he has had with some people that are involved with innovation policies in government, and he said:

The guy’s mindset is from the 1970s – all he could think about when we talk about innovation is IP and commercialisation. Our challenge isn’t to educate the firms, it’s to get the message across to the people that are setting policy.

Conversations like these are both frustrating and exciting. They are frustrating because it would all be a lot easier if people understood what innovation is and how important it is – then we could concentrate on helping them get better at it. On the other hand, they are exciting because it shows how great the opportunity is if we can figure out how to communicate clearly about innovation. This is the message that we need to get out to everyone:

Innovation is the process of idea management.

It looks something like this:

There are three key components to it: idea generation, idea selection & implementation, and idea diffusion. They’re not really sequential either – they feed each other. You need to do all three well to innovate successfully. Central to all of it are people and processes. In particular, one process that we must have is integrating innovation into the organisation’s strategy.

As usual, it sounds very simple to explain it this way, but executing it is a bit harder. Nevertheless, there are several huge advantages to thinking about innovation in this way:
- Idea management is more than just having great ideas. One mistake that people make is to equate innovation with having a great idea. Innovation is not invention. That’s the mistake that our colleague made – fortunately, because she’s very smart, she quickly saw things from a new perspective.
- Thinking about the three steps makes it easier to see innovation as a process – which consequently makes it easier to manage. It’s impossible to manage “having a great idea”. It’s easier (but still challenging) to manage idea generation, idea selection and idea diffusion. Fortunately, there are tools and processes that help with all of these steps. Thinking about innovation in this way is the first step to learning how to manage it.
- It is useful to think about this process taking place within an innovation ecosystem. This gives you another thing to manage – your place in the value network. There are benefits to this though. It makes you realise that you don’t have to come up with all the great ideas yourself, and you also don’t have to spread them all yourself. It becomes much easier to think about collaboration when you see innovation as idea management.
- Seeing innovation as idea management is much more effective than seeing it as just commercialisation. In the commercialisation view, the only way to win is to have a great idea, protect the IP from it, and bring it to market. In the idea management view, you win by identifying and executing great ideas. They don’t have to be new products, the ideas can be for new ways of doing things, or for new business models. Those are all ideas. The innovation process needs to manage ideas – not just create new products.
Innovation is an essential part of economic progress. We can’t make things better without having new ideas. And having them isn’t enough – we also have to be able to execute them, and get them to spread. If we are going to successfully innovate, we need to think of innovation as a process. It might be a fuzzy process, but it is still something that we can manage. So think of it this way:

Innovation is the process of idea management.
14 May 2010
Five Forms of Filtering

We create economic value out of information when we figure out an effective strategy that includes aggregating, filtering and connecting. The three steps interact and reinforce each other – and successful information-based business models have all three. We can undertake business model innovation by changing our methods in these three areas, or by changing where in the value network the processes take place. I’ve run across a few things recently that have gotten me thinking about filtering – and it made me realise that we have another classification issue here. Here is how Clay Shirky frames it:

So, the real question is, how do we design filters that let us find our way through this particular abundance of information? And, you know, my answer to that question has been: the only group that can catalog everything is everybody. One of the reasons you see this enormous move towards social filters, as with Digg, as with del.icio.us, as with Google Reader, in a way, is simply that the scale of the problem has exceeded what professional catalogers can do. But, you know, you never hear twenty-year-olds talking about information overload because they understand the filters they’re given. You only hear, you know, forty- and fifty-year-olds taking about it, sixty-year-olds talking about because we grew up in the world of card catalogs and TV Guide. And now, all the filters we’re used to are broken and we’d like to blame it on the environment instead of admitting that we’re just, you know, we just don’t understand what’s going on.

Filtering is what helps us deal with the vast amount of information available to us. We try to filter information so that we end up with something that is relevant to us – it helps us learn something, it helps us solve a problem, it helps us develop a new hypothesis about the world around us. These are all connections – and this is what really drives value creation. However, we can’t connect without some filtering going on. So filtering is important, and it’s a term that includes several different sub-types. I can think of at least five forms of filtering.

The five forms of filtering break into two categories: judgement-based, or mechanical.

Judgement-based filtering is what people do. At its most basic level, we have naive filtering. This is what we do when we don’t know anything about the information that we are trying to filter. This is a fairly complex internal process, and there are plenty of models available for what is happening in this step. It’s basically everything going on in the ‘Sense’ step in this diagram by Harold Jarche:

As we gain skills and knowledge, the amount of information we can process increases. If we invest enough time in learning something, we can reach filter like an expert. I previously explained how this process can work in bird-watching.

However, even experts can’t deal with all of the information available on the subjects that interest them – that’s why they end up specialising. One way to increase the amount of information that gets filtered is by relying on a network. This can be a network of learners, as in the Connectivism course run by George Siemens and Stephen Downes, it can be a group of people with a similar interest, like all of us talking about #innovation on twitter, it can be large groups of otherwise unconnected people as in Shirky’s examples on places like Digg and Delicious. Networks expand our reach enormously.

There can also be expert networks – in some sense that is what the original search engines were, and what mahalo.com is trying now. The problem that the original search engines encountered is that the amount of information available on the web expanded so quickly that it outstripped the ability of the network to keep up with it. This led to the development of google’s search algorithm – an example of one of the versions of mechanical filtering: algorithmic.

Algorithmic filtering is used by most of the filtering tools available on the web. They can cover the entire web, like google does, or sub-sections of it, like an RSS feed does. Howard Rheingold describes many of these sorts of tools in his post and video on Mindful Infotention.

Rheingold also provides a pretty good description of the other form of mechanical filtering, heuristic, in his piece on crap detection. Heuristic filtering is based on a set of rules or routines that people can follow to help them sort through the information available to them.

Why is filtering important? Understanding the variety of filters available explains why there are often arguments about the discrimination process, and over what role a particular filter plays. If someone writes about filtering, and they mean ‘algorithmic filtering’, a reader thinking of filtering in terms of ‘network filtering’ is likely to misunderstand the discussion. Another source of confusion is that some people talk about filtering not as a search for useful information, but as a way to block information that annoys them.

Filtering by itself is important, but it only creates value when you combine it with aggregating and connecting. As Rheingold puts it:

The important part, as I stressed at the beginning, is in your head. It really doesn’t do any good to multiply the amount of information flowing in, and even filtering that information so that only the best gets to you, if you don’t have a mental cognitive and social strategy for how you’re going to deploy your attention. (emphasis added)

Filtering and connecting is what leads to important skills, like pattern recognition (described well by Venessa Miemis).

Finally, we can use these ideas about filtering to help with business model innovation by changing where it takes place in the value network. One of Shirky’s points is that since Gutenberg, the economic logic of publishing required publishers (of books, music, movies) to act as filters in order to maximise their investment. As publishing and filtering has shifted out to human networks, publishers no longer need to fill this role. Someone (or some network) needs to, and since that creates value, it’s something that can perhaps be monetised.

You can see this in investing. You can put money in Berkshire-Hathaway, where investment choices are run through the personal expert filter of Warren Buffett. Or you can invest in individual stocks recommended by a broker- which is filtering through an expert network. Or you can take advantage of DIY investing, where you do your own filtering, probably aided by some heuristic filters as well. Three different investing business models based on three different filtering methods.

People or networks filling the filtering role now are creating significant value – and people trying to come up with innovative business models in these fields should be thinking about how they can create value through filtering. Of course, the filtering needs to be part of an overall aggregate, filter and connect strategy – which is at the core of successful information-based digital business models.

(Thanks to John, Phil Long & Nancy Pachana for talking to me about these ideas- of course, none of this is their fault. Special thanks to Phil for editorial suggestions.)

8 April 2010
Personal Aggregate, Filter & Connect Strategies
A while back my PhD student Sam and I were talking, and he asked me about my RSS feed. His question was something along the lines of ‘what blogs would I have to read if I wanted to be able to make the connections that you do on your blog?’ As we talked, I realised that it didn’t matter if I gave anyone else my exact RSS feed, they wouldn’t be able to replicate my blog – and the reason for this is aggregate, filter and connect.

When I first thought about aggregate, filter and connect as a framework, it was in an attempt to explain why Amazon’s business model worked better than that of other online bookstores. The first time I talked about it in public, it was to explain how open education might work. I’ve been working on making it in to a general model of how we create something unique when we’re primarily dealing with information.

As such, it can be used to explain business models, like Amazon’s, or blogs, like mine. The more I’ve talked about the model, the more other people are picking it up, which is great. Some of these recent discusssions have gotten me thinking about how aggregate, filter and connect works at a personal level. This was really Sam’s question. I’ve talked about how Charles Darwin basically used an aggregate, filter and connect strategy, Phil Long talks about it as part of personal knowledge management, Harold Jarche has discussed it as both a general model for business and for personal knowledge management (an idea that Jack Vinson picked up, and connected to the concept of enhanced serendipity from Ross Dawson), and Glenn Wiebe used the framework to discuss both Joseph Priestly’s inventions and teaching. So we’re starting to get a bit of discussion Today I’d like to illustrate the concept by discussing how I use it.

Aggregate, filter and connect is a non-linear process, with lots of feedback loops. However, it is unavoidable to talk about it in steps. While I do that, keep in mind that it is all going on at once. Here is how I use the framework to execute ideas in my main area of interest – innovation and networks:

Aggregate: I do a lot of data scanning. The RSS feed that Sam and I were discussing includes 182 blogs. I also follow 306 people on twitter, most of whom usually tweet about things relating to my areas of interest. In addition to that, I finish a book about once every 3 days, and I’ve been doing that for a looooooong time. I also talk to a lot of people, despite being an introvert (see Sacha Chua’s great presentation The Shy Connector to see how that works) – last year had over 80 meetings with people that are practicing innovation management, plus contact with my students, who are nearly all out in the workforce as well. Then there’s the stuff I’ve learned in all the jobs I’ve had. Collectively, this adds up to a fair bit of data.

Filter: This is my weakest area – I don’t outsource nearly enough complexity. I need to get better at taking notes on things I read, in searchable media, so that I don’t do all the filtering in my head. At the moment, I don’t even filter my twitter stream. Ken Gillgren argues that we should be taking in as much data as we can possibly handle, to improve our ability to see patterns and make novel connections. So I’ll say that’s what I’m doing. In addition to my head, I’m also using Evernote, my own tweets, diigo, and my blog as filtering tools. And I’ve used fairly primitive methods like writing reading notes, though that generally hasn’t worked too well for me – I actually find blogging more effective.

Connect: Harold Jarche has been doing some fantastic thinking about this topic recently, and he made this diagram to illustrate the process:

I think this is a nice diagram, which pulls together a lot of the recent discussion on the topic. The one thing that I would like to add to it is this idea: connection works in two related but distinct ways. The first is that we connect ideas to each other. This is the innovative act – as Schumpter said, “(Economic) development in our sense is then defined by the carrying out of new combinations”. This is where I put a lot of effort when I’m coming up with blog posts, with research papers, and even with ideas for consulting jobs. Making novel connections is a skill that I work hard to build.

The second way that connection works is that we connect ideas to people. This is the outbound side of Connection. I use several strategies. When I re-tweet something, I try to make a comment that links the tweet to a broader concept (sometimes a challenge with 140 characters!). I write about the idea connections that I make in my blog – as people read it, they start connecting with the ideas. I give as many public talks as I can – from last September until now I have given more than twice as many public talks as I had in the previous three years combined. In Canberra last week I had a talk with Geoff Garrett, who said “Innovations travel on two legs.” There’s something to be said for that idea – and I have a lot of discussions about my ideas face-to-face – it’s one of the most effective methods of outbound connection.

So that’s a brief summary of how I have been trying to use the Aggregate, Filter and Connect framework over the past few months. In using it, I have learned a few things that might be useful for others too:
- It really helps to think about the three tools explicitly. As I said, I’ve always been reasonably good at making novel connections. But my ability to get my ideas to spread has increased dramatically once I started thinking in this way. Particularly with regard to outbound connections. My use of twitter, and the increase in my public speaking were both ideas that I initiated to increase my connections.
- When people feel overwhelmed by information, it usually means that they aren’t filtering effectively. Like I said, this is my weakest area. But there are some really smart people working on this. In addition to the posts I’ve linked to, check out the rest of Harold Jarche’s blog for some ideas. Venessa Miemis and Ken Gillgren have done some really good thinking in this area too. This is one of the areas in which most of us probably need to improve.
- The other area that we probably need to address is this: we need to get better at connecting ideas. This is where we create value – by making novel connections. And it’s not enough to just make the connections in our head – we have to frame them in a way that others can act upon. That means creating tangible content – a blog, tweets that connect ideas, podcasting, something. My primary recommendation here is to practice making novel connections, and then express them in a way that enables your idea to spread. One good way to do this is to expand the range of areas from which you collect information, and as you read and hear things from outside your area, consciously think about how they connect back to things that you know well. This is the strength of weak ties between ideas.
- Finally, your personal knowledge management scheme isn’t complete until you are doing all three things well. Aggregating is great, but only an initial step. If you don’t filter well, you won’t be able to make sense of the information that you collect. At the same time, even if you aggregate and filter well, you only create real value when you make novel connections between ideas. Information is the fundamental building block of idea connections. Once you make these novel idea connections, you then need outbound people connections to get your ideas to spread. The three skills reinforce each other.
So there’s the answer to Sam – you can replicate my blog by copying my incoming information streams, using the same filtering tools that I do, and then making the same connections between ideas that I do. In other words, you can’t. Aggregate, Filter and Connect is one method you can use to generate unique intellectual value.

NOTE: I’d like to thank everyone I mention in this post, and many others as well for contributing ideas that I’ve been able to use as building blocks in this argument – It’s great that we’ve been able to Connect! George Siemens and Jon Husband have also written things on these topics that have influenced my thinking.

Another NOTE: Venessa has pointed out in the comments that Howard Rheingold has written one of the definitive articles on filtering: Crap Detection 101.

Third NOTE: Follow-up post: Filtering With Your Network
Final NOTE: Here is a practical example of how the process works.
26 January 2010