GUEST POST from Tim Kastelle

How do we decide what actions to take in business? Or in life?

In many cases, we base our actions on our models of the world. We think that things work in a particular way, and this determines the choices we make.

We can make a strong case for trying to make these choices more like a scientist does. Here’s an example.

The Nieman Journalism Lab pulled together a set of opinions on the new pay system that the New York Times has put in place, with thoughts from Steven Brill, Anil Dash and Megan McCarthy among others. Here is one of the points that Steve Buttry makes in his piece:

“My friend and former boss Jim Brady says that you can’t build a business model based on what people should do (and newspaper people believe in their bones that people should pay for their content). You build a business model based on what people will do. This tortured maze of exceptions and trigger points is a laughable effort to collect because people should pay but to find a way not to lose the people who won’t pay.”

This is a great point.

The really good thing about it is that we can actually set these up as hypotheses that can be tested. “People will pay for content” is an idea about which we can collect a fair amount of data.

There are a number of people that are starting to say that we should treat business models as a set of testable hypotheses. Steve Blank outlines how to do this very well in this set of slides:

His thoughts on the broader trends in start-up experience are well worth reading in full, but for our purposes, jump to the case study that starts on slide 72. He uses the Business Model Canvas to discuss the experience of a start-up called OurCrave, an online social shopping platform.

The case shows what the original business model was, and more importantly, how the assumptions underlying it were tested. Then he shows how the business model changed five times in response to data and testing.

This approach doesn’t necessarily guarantee success, but it certainly helps the odds. It demonstrates the reality that Peter Sims describes in a recent post:

“The truth is, most entrepreneurs launch their companies without an brilliant idea and proceed to discover one, or if they do start with what they think is a superb idea, they quickly discover that it’s flawed and then rapidly adapt.”

Thinking about your business model like a scientist can be a good scheme, provided you keep two warnings in mind.

The first is to beware of false precision. You want to test your assumptions with data, but you can’t always get the data you need. And the fact of the matter is that your plan will change yet again once you launch it and people really start interacting with your ideas. Usually, when testing numbers like these, you want to be within an order of magnitude with your numbers, and you want to remember that they are estimates.

The second caution is to avoid making models that say things like “social media always works” or “social media never works.” These absolute cases are never true. What we really want to figure out are the circumstances in which an approach works or doesn’t. Your business model may or may need need social media support, or six sigma in your production, or any other number of things.

Scientists are interested in finding the boundary conditions for rules – when do rules stop working? When testing business model hypotheses, you’re trying to figure out what is right in your particular case. So beware of absolute statements about what will or won’t work.

Experimenting is a critical innovation skill. If you can figure out how to experiment with your business model, you will increase your chances of success. Just remember to test it like a scientist.

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GUEST POST from Tim Kastelle

“The Future is already here, it’s just not evenly distributed.” – William Gibson

That’s the idea that framed last week’s post – Where’s My Flying Car? I argued that as innovators, our job is to invent the future – and that in doing so, instead of trying to come up with something that has never existed before, like a flying car, we’re better off trying to figure out how things that already exist can be redesigned so that they mean something completely new.

It’s about innovating meaning rather than innovating stuff.

Some of the responses to that post triggered some thoughts about how to do this – so here are three steps to take for inventing the future:

1. Make sure that what we’re doing creates genuine value

Last week I talked about how part of the problem was in defining potential solutions to our problems only in terms of ways of thinking that already exist. Umair Haque made a similar point in his post last week:

“What stands in the way of the future, most often, is the past. It’s yesterday’s sluggish institutions. Yet, instead of reimagining and rebooting those institutions, we keep reviving and resurrecting them — zombielike — hoping that by bringing them back from the dead, we can keep the status quo humming along for just a little while longer, that we can eke out the last meager, shriveled morsels of returns from seeds laid down during the industrial age.

So here’s my question: Does what you’re doing have a point — one that matters to people, society, nature, and the future?“

This is the first step in inventing the future.

2. Create value by meeting needs, not wants

Jeffrey Phillips made a very good response to last week’s post. He argues that we don’t have flying cars because while a number of people may want them, no one really needs them. Phillips says that the best way to create value is to focus on needs:

“Wants are interesting and may lead an innovator to potential value, but are often not deeply rooted or key to a person’s life. Additionally, wants often don’t scale, that is, they aren’t shared by a significant number of other people. Needs, on the other hand, are more immediate, more closely felt and more likely to be shared. Innovators must do a better job distinguishing between wants and needs.”

So that’s step two.

3. Redesign things that already exist to create new meaning

Kevin McFarthing gave a great example of doing this in a comment:

“Very interesting post, Tim. A good example of disruptive technology and displacement of existing is the growth of mobile phones in places like India and Africa. People talking to each other is an “old job”. Mobile phone technology leapfrogs landline and cable to allow millions more to talk to each other. The language of the job and the technology exist, economics facilitate, and the innovative change in the market happens.”

That’s step three – innovate meaning. In this case, mobile phones in the west mean “I can talk anywhere”, but in developing countries, they mean “I can finally talk.”

I’ll take Kevin’s example one step further. Mobile phones have been revolutionary in Africa in several ways – for example, they have been used to create banking for many people that don’t have a bank account:

“Disintermediation is also made possible by mobile money. Services to transfer cash by text message have been around for some years. One of the most successful, M-PESA, began in 2007 in Kenya, where it now has more than 13m users. It is now used for salaries, bills, donations: few things cannot be paid for via a handset. Similar services can be found in more than 40 countries. Though not yet on the same scale, this seems to be only a question of time: in most countries in sub-Saharan Africa, more people have a mobile phone than a bank account”

This has it all. The mobile services are making people’s lives materially better. They do this by meeting a genuine need. And the value is created using technology that has been considered a failure in North America and Europe, but which is given a new meaning in the context of developing countries.

This isn’t just an innovation lesson that applies in developing countries though. You can use these same three steps anywhere. In fact, you should use these same three steps any place where you want to use innovation to invent a more meaningful future.

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GUEST POST from Tim Kastelle

When Paul Krugman and Charlie Stross had a chat at WorldCon a couple of years ago, the first question out of Krugman’s mouth was “Where are the flying cars?”

Krugman asked this because he knows that science fiction authors like Stross have been imagining the future for quite a while, and that currently impossible technologies like flying cars have long played a role in their speculations.

Like sci-fi authors, if we’re trying to innovate, our job is to invent the future.

But what does that mean?

Bruce Sterling has some interesting things to say about the future – starting with a paraphrase of William Gibson:

“You see, the future is already here, it’s just not well distributed yet.

The future does feature some brand-new stuff that was technically impossible before, but, more importantly, the future has a different take on matters that are already here. There’s a change of emphasis. The future is like another culture, another country. We have to come to terms with the future’s language.”

The Sterling piece has a lot of interesting ideas about design and inventing the future, and it’s worth a read.

His distinction between brand-new stuff that was technically impossible before, and figuring out the language of the future is a critical one.

It means that inventing the future isn’t simply about making flying cars and other cool stuff you find in sci-fi novels.

We can actually invent the future by figuring out new meanings for the things that are already here.

There’s a great example of what this means in Greg Satell’s recent post:

“As I explained in an earlier post, disruptive innovation is crappy innovation. Crappy, that is, because it tends to do old jobs poorly. A truly disruptive technology changes paradigms by doing a new job entirely. (That’s what makes it so disruptive).

The fact that so many people keep trying to square this circle shows an appalling lack of imagination and good sense. The true impact of digital technology in the marketing arena lies years in the future, possibly more than a decade. What will that impact be? To be honest, I don’t really know and I’m deeply suspicious of anyone who thinks they do.”

People are trying to define the future of digital marketing exclusively in terms of what marketing looks like today. This is wrong. The innovation here isn’t coming up with shiny new web technologies. The innovation opportunity lies in taking the concepts that are already here, and figuring out a new language that will determine how they will work and what they will mean.

We can’t do this by simply extrapolating existing trends. Nor by taking new technologies and new ideas and hammering them so they fit into existing concepts and frameworks.

We won’t invent the future by inventing flying cars. Which in some ways is too bad, because I’d like one. We will invent the future by taking things that are already here, but which are maybe unevenly distributed, and giving them new meanings.

Innovating language by making new novel connections between ideas is the best way to invent the future.

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GUEST POST from Tim Kastelle

Jason Potts said that to me last week when we were talking about the work he’s doing on his house. The house was severely damaged in the flood in January. For the past few weeks, Jason has been working on rebuilding it. He was telling me about how he’s learned to do a lot of the work – simply through experimenting.

Being wrong is actually an essential part of learning – as Jason’s experience with doors shows. Three months ago he didn’t know anything about installing handles on doors, and now he’s good at it. And the cost was much lower than he expected.

This is common error – we nearly always overestimate the cost of experimenting.

Part of this is because we don’t realize how cheap it is test most ideas. But the bigger problem is psychological – one big problem with experimenting is that we might be wrong. In her book Being Wrong, Kathryn Shulz explains why this is such a problem for many people:

“In our collective imagination, error is associated not just with shame and stupidity, but also with ignorance, indolence, psychopathology, and moral degeneracy. This set of associations was nicely summed up by the Italian cognitive scientist Massimo Piattelli-Palmarini, who noted that we err because of (among other things) “inattention, distraction, lack of interest, poor preparation, genuine stupidity, timidity, braggadocio, emotional imbalance,… ideological, racial, social or chauvinistic prejudices, as well as aggressive or prevaricatory instincts.” In this rather despairing view – and it is a common one – our errors are evidence of our gravest social, intellectual, and moral failings.

Of all the things we are wrong about, this idea of error might well top the list. It is our meta-mistake: we are wrong about what it means to be wrong. Far from being a sing of intellectual inferiority, the capacity to err is crucial to human cognition. … Thanks to error, we can revise our understanding of ourselves and amend our ideas about the world.”

In other words, being wrong is the only way to learn.

That’s one of the things I’ve been spectacularly wrong about recently. We’ve had a persistent leak in our basement, which became a torrent during the rains that led to the flood. Ever since I first noticed the leak, I thought that it was from a broken drain pipe. My presumption was that the pipe was underneath the bricks, and that we would have to get plumbers in and dig up the whole front of the house to fix the leak.

During the rain, I took a close look at the leak, and realized that my idea about its source had to be completely wrong. So I developed a new idea: that the water was leaking through the gap between the sidewalk and the bricks.

To test this idea, I tried the experiment you can see in the picture. I bought $20 of sealant, and did the world’s messiest caulking job along the two gaps that I thought might be the source of the leak.

Since then, not one drop of water has gone into the basement. My experiment worked. Now that I know what the problem is, I can work on coming up with a solution more elegant that my two messy lines of sealant.

There are some general lessons in all of this home improvement work:

• If things aren’t working right, examine your basic assumptions: I thought my leak would require a lot of time, money and expertise to fix. Jason thought the same about putting in doors. We were both wrong – but we only knew this once we actually tested those ideas.
• Experiments are a lot cheaper than you think: Who knew that doors only cost $30? People that had already tried to experiment with them, I suppose. My new idea about the leak could have been wrong, but the $20 of sealant was a lot cheaper than calling in a plumber to test out my first hypothesis. Find a way to test out your ideas as quickly and cheaply as possible.
• Being wrong is the only way to learn: Schulz’ point is exactly correct – being wrong is not a moral failing, or a sign of intellectual inferiority. It is the only way we can figure out how to do new things.

Experimenting isn’t just for fixing stuff around the house either – experimenting is a critical step in innovating. Earlier this week Jose Baldaia pointed to an excellent post by Amir Khella called How I launched a profitable product in 3 hours. Khella recounts how he developed a new product called Keynotopia which is, beautifully, a rapid prototyping (experimenting!) tool.

Here is part of what happened:

“It had been less than a month since I wrote about how I’ve been using Apple Keynote to prototype iPad applications. I debated whether or not I should publish the post, thinking there was nothing new or useful about it. Yet, I decided to do it for the fun of it. What I didn’t expect, though, was for the post to be picked up by some of the most respected bloggers, becoming popular among the design and iPhone communities. In less than three weeks, the post generated more than 10,000 visits and 500 downloads of the iPad keynote templates I posted along.”

Khella had an idea, but wasn’t sure if it would work, or even if it was any good or not. Instead of sinking a lot of further thought into it, he ran an experiment. He put together a website just to see if the idea would fly.

And it did. It’s great when your experiments work out this way. But what if it didn’t?

If no one had gone to the page, or if Keynotopia wasn’t useful for people, or if it didn’t work right, or if something else had gone wrong, he would have learned something – and for a pretty small investment, just three hours. If no one tried it out, then he’d know that the idea didn’t create value for people. Then he could move on to his next idea/experiment. If they had complaints about how it worked, then he’d know that the idea creates value, but his execution needs to be better. Then he could fix the problems and make the idea better.

In both of those cases, he would have been wrong about something. And much better off for knowing it.

Being wrong is the only way to learn. If you have a great idea, find a way to test it out. Experimenting is usually a lot cheaper than you think. Just remember Jason and the doors.

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GUEST POST from Tim Kastelle

What do you do if the tools you use to improve your innovation process actually make it worse?

I had a meeting recently with one of our research partners to go over some of the results of our recent survey. The research has two parts – we are mapping the innovation and knowledge-sharing networks within the organization, and we are also looking at how effective they are at managing the innovation process. Finally, we’re trying to figure out how these two things interact. Recently we were talking about their innovation process.

We surveyed about 100 people about the organization’s effectiveness in managing the innovation value chain. The innovation value chain looks at how effective you are at generating ideas, selecting and testing ideas, and getting ideas to spread. I’ve pictured it previously like this:

In order to innovate effectively, you need to be good at all three parts of the process. Firms rarely suffer from a lack of ideas, and our partner is the same. The part of the process that they are the worst at is selecting and testing ideas. These are some of the questions on which they scored particularly poorly:

• It takes too long to develop novel project solutions to the point where they can be used.
• We do not have the time to develop innovative ideas for potential reuse outside of the project they were invented on.
• We have a risk-averse attitude towards trying novel project solutions.

The irony of this situation is that they have invested a fair bit of money into a software package which has the primary purpose of helping them with exactly this step in the process.

As I discussed this with our contact, she looked at the survey results, then she said “You know, none of those things are problems that can be solved with technology.”

I thought that this was a fantastic piece of insight.

A big part of the problem here is that they invested in a tool, and the expected the tool to solve their problem. Unfortunately, the tool doesn’t create more time for people, and it doesn’t increase their innovation skills. Their tool has moved them to the right on the Innovation Matrix, but it hasn’t moved them up it:

This is why the Innovation Matrix is useful – because tools and skills are two separate things. You can increase one without affecting the other. As Jeffrey Phillips wrote in a perceptive post recently, innovation is the last people-centric process:

The fact is that people play a disproportionate role in innovation when compared to any other important function. That’s because, unlike many other processes, the work can’t be divided into simple tasks that can be automated by a computer or accelerated by inanimate processes. So here’s the important question: if people play such a vital role in innovation, why do we starve innovation of the best people in the organization? If people are so vital to innovation, why do we intentionally limit the amount of time we allow for innovators to work?

There are a few key lessons in this:

• Innovation tools and innovation skills are two separate things: people often think that they can solve their innovation problems simply by finding the right tool. This is rarely true. In general, to improve innovation you have to improve skills and capabilities. Tools can be used to facilitate this process, but they can’t do it on their own.
• One of the biggest obstacles to innovation is lack of time: if innovation is important, people need the time and space to work on developing, testing, and spreading new ideas. If you are a manager and you want your people to be more innovative, you have to give them the time needed to do this.
• Tools don’t solve problems, people do: this is why innovation is still people-centric. It’s more important to remove obstacles to innovation than it is to give people tools.

There’s no magic bullet here, no perfect tool. So stop looking for one. Instead, start experimenting. Find the things that work, and scale these up.

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GUEST POST from Tim Kastelle

Here’s a persistent innovation management question: is it better to have a dedicated team responsible for innovation, or should this responsibility be distributed throughout your entire organization? The best answer depends on your circumstances. But if you set up a dedicated team, it’s important to consider what role you want them to play. There are four different roles that a dedicated innovation team can fill.

One of the organizations that John and I do quite a bit of work with has a new internal group that’s been set up to try to help facilitate the identification and execution of innovations that will have a longer-term impact on performance. Prior to this, they had been responsible for facilitating all innovation throughout the organization. In this new configuration, a different group is responsible for helping incremental innovations. The longer-term group, which includes all the people that we’ve been working with over the years is supposed to be looking at “emerging opportunities.”

Over the past few months I’ve been working with them to try to figure out what their business model should be. As we talked things through, we realized that there were really four different roles that they could try to fill. This is what they are, in order of increasing levels of resource commitment:

1. Information Facilitation: this is essentially the role they used to have before the restructure. When you do information facilitation, you find information about innovation, and distribute this to people that are generating ideas. This will help them figure out how to best execute the new ideas. In this role you can also work on developing processes and infrastructure that support all parts of the innovation process. This type of group is most active in supporting idea generation.
2. Opportunity Consultant: a group doing this will do everything that an Information Facilitation team does, but they will take a more active role in selecting ideas. They work to ensure that the ideas that are pursued connect with the organization’s overall strategy. In this role you work on developing the best possible set of criteria for evaluating ideas, particularly for fit with objectives.
3. Opportunity Enabler: this type of group goes one step further – they work to connect ideas with those that have the resources to execute them. Enabling collaboration is a big part of this role – you need a group in this role if you are pursuing an open innovation strategy. This type of team will also work on developing implementation plans, and trying to quantify outcomes and learnings from new initiatives. Opportunity enablers are active in supporting all steps in the innovation process – idea generation, selection, testing and diffusion.
4. Execution Delivery: this is the most active role you can have – this is a group that doesn’t just support the innovation process, they actually undertake all the steps. Most R&D groups fall into this category.

It pays to think about this taxonomy for a few important reasons:

• Upper management often thinks that they are setting up an Execution Delivery group, but only puts together a group with sufficient skills and resources to successfully fill one of the less intensive roles. You can’t set up an innovation group, with responsibility for innovating, without also provided the resources that are required to do this. If you have limited resources (or limited commitment), it is better to acknowledge up front that your new team will be Opportunity Consultants or Enablers. Or even Information Facilitators. The more clear you are about the group’s objectives, the more likely it is that they will be successful. And the objectives have to align with the resources.
• The skills that you need to fulfill each role increase substantially as you move up the list. This is one of the issues that the team we’re working with faces – they started out as Information Facilitators, but in their new role will only deliver value to the organization if they are able to be Opportunity Enablers. This requires a different set of skills. Fortunately, the group is very bright, and quick learners – so they may well be able to build these skills. But again, you have to think about what skills are required up front.
• Because the skill requirements are different, don’t expect one group to fill more than one or at most two of these roles. To some extent the lower-level activities are included as you move up the ladder, but not entirely. If you need to have all four roles filled within your organization, you probably need to have more than one group working to support innovation. Or you at least need to have responsibility for these different roles clearly assigned to different people within one large team.

Using specialist teams to support innovation is a really good idea. However, in order for them to be successful, you need to be clear at the start about which role you want them to fulfill. Each one requires different skills, and different levels of resourcing. If you want a high-performing Execution Delivery team, you need to resource it appropriately.

If you don’t don’t need a full delivery team, or if you don’t have the resources or commitment to supporting one, then you need to scale back expectations. It’s a question of figuring out which role best supports your overall strategy. That’s how you work through your ball of creative mess.

Editor’s Note: If you enjoyed this article you will also enjoy The Nine Innovation Roles by Braden Kelley

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GUEST POST from Tim Kastelle

Social influence is important to innovation. One of the critical steps in innovating is getting our great new ideas to spread – and this is often an issue of social influence. Here is an excellent short talk from network researcher Sinan Aral about how to measure social influence:

Sinan Aral: Social Contagion from PopTech on Vimeo.

Here are some of the key ideas that arise from the talk:

• The economy is a network: in order to understand how innovations diffuse, and how ideas spread, we have to think about the economy as a network. We don’t make decisions in a vacuum – decisions are a social action (see the collected work of Mark Earls on this topic).
• Your network is also important for idea generation: Jorge Barba recently asked whether innovation is primarily an individual or a group activity. It’s a group effort – just as decisions are social actions, so is idea generation.
• If your friends are making you fat, are they also making you innovative?: this is the key issue – if idea generation is a social act, and you want to be more innovative, then you need to spend more time with people and groups that are more innovative.

If we want to innovate more effectively, we have to gain a better understanding of how social influence works.

In the meantime, it’s probably a good idea to start hanging out with people that seem to have a lot of ideas.

Let me know what you think.

Or even better, tell your friends to come and let me know what they think!

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GUEST POST from Tim Kastelle

In this week’s class we talked about Jeff Bezos’ TED talk. When I think about innovation, to me the central part of the process is connecting ideas. As I keep emphasizing, once we’ve done this, we then have to work like crazy to execute them well, and to get them to spread. But we need to start with great ideas, and we get these by making novel connections. I like this talk because there are several great examples of the importance of connecting in innovation.

The first example of connecting works at the meta level. This is a great example of confronting an uncertain business situation (what do we do about the internet?) through the use of analogy (trying to find the most comparable set connections out of several possibilities). In this case, Bezos takes on the idea that the internet was like the gold rushes of the 19th century. This was a common idea after the dot.com bust. He argues that this comparison is not the most accurate one, and that a better analogy to use would be that the internet is like electricity.

Bezos also demonstrates the importance of connecting ideas with all of his examples of re-purposing. As he says, homes weren’t wired so that they’d have electricity, they were wired so that lights could be installed. However, once the houses and businesses were wired for electricity, hackers found many uses for it that had nothing to do with lighting. That’s how electrical appliances got started. It’s yet another example of how innovators often don’t know how their new ideas will ultimately be used.

And Bezos has multiple examples of making innovative things by combining existing ideas. The toaster is a good one. Prior to electric toasters, people made toast over fires, or using a rack on a stove. Once homes were wired, someone figured out that you could use electricity to heat an element stuck in the middle of the same kind of rack. It was a creative recombination of ideas – connecting ideas – that led to the innovation.

Finally, he shows the value of trying many possible combinations of ideas. Not all of them work, and in retrospect the ones that don’t look stupid. Like the electric tie straightener, and the stupid dot.coms. But that’s the essence of innovation – experiment widely to see what works. Find has many new connections between ideas as possible, and try them out. This leads to waste – so we need to find ways to test these new combinations as quickly and cheaply as possible. But since we don’t know in advance which ideas will work, the best way to filter them out is through experimenting.

We often talk about how organizations can place too much emphasis on aggregating ideas. Instead, I think we need to focus on getting better at connecting ideas in novel ways. This is how innovative ideas arise. There are skills that help in this regard – pattern recognition, lateral thinking, and so on. If you’re trying to be more innovative, try to build these skills. Don’t try to compile more ideas, focus instead on making more novel connections, because that’s the fundamental creative act in innovation.

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GUEST POST from Tim Kastelle

Scott Berkun let out the secret of innovation 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 here 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 organizations – huge multinationals, small start-ups, government departments, and educational institutions. Despite these different backgrounds, their findings were remarkably consistent – only 3 of the 60 organizations 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 organizations 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?

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GUEST POST from Tim Kastelle

The word “network” causes a lot of the same problems that “innovation” does – it is used in so many different ways that it is often hard to tell exactly what the user means, it’s in fashion to the point of sounding like hype, and as a consequence a lot of people are ready to stop using it altogether. So when I say that “the economy is a network” it can cause some confusion. Do I mean it is like a network? That is has network-like properties? That it’s something between a hierarchy and a market?

No. I mean that the economy is a network – and that the best way to analyse it as a network. In network analysis, a network consists of nodes (people, firms, countries and so on) and the connections between them (economic exchange, friendship, family relationships, disease vectors and so on). An economic network then is one where people are the nodes, and the economic relationships form the connections between them.

Thinking about economics in this way leads to some useful insights. I was reminded of this when I read Umair Haque’s post – The Real Roots of Recovery. Here is how he sets up the problem that he’s trying to address:

“What is an economy? Is it just rivers of money and stuff, flowing back and forth between consumer and producer, resting on a bed of information? That’s more or less the way we’ve conceptualized it. It’s why economists often say that banks and funds make up the “financial economy,” while industries that make stuff are the “real economy.”

When we conceptualize an economy that way, the implicit goal for both “producers” and “consumers” is merely accumulation of money and stuff. More, more, more. That’s what I call a “thin” economy. That kind of economy is thin in three ways: it’s brittle, easily broken; it’s fragile, crisis-prone; and it’s as shallow as Paris Hilton.”

His suggestion is that to make a stronger economy, a “thick” economy, we need to focus on making real connections with others.

“Yet even that’s just a beginning. The economy is “constructed” by us: built anew every second of every day by each of our billions of tiny decisions, emergently. The real change begins with each of us, and the choices we make.”

This is a network story! The issue with networks is that ties are expensive to maintain. If we think about economic ties, the involve money, attention, time and care. My read of Haque’s argument is that we tend to only think of the ties in terms of exchange. In this view, we choose to buy a loaf of bread, we pay for it, and that’s that. That’s thin. A thick network tie will consider attention, time and trust as well.

What does this mean in practical terms? If we think of our economic relationships as network ties, then the idea that every transaction is a one-off makes no sense at all. Each time we need something, we have to figure out who is cheapest, where they are, and how to make that transaction. On the other hand, if we think of economic relationships as network ties, as something that persists – we value them differently. Now trust becomes more important, as does attention. We want ties that we don’t have to worry about because we know what we’re getting. We want a stable, persistent network. The way to get that is to build relationships with the people in our personal economy. We don’t have to recreate a whole new network each time we need something.

Viewing the economy this way also changes where we want to be in the economy. Take a look at the network diagram from Valdis Krebs above.

The people that I’ve circled are those with high betweenness centrality (learn about that here). In an exchange economy, those are great positions to be in because you can take advantage of your position in between two big clusters. Any goods or information that has to pass between the two groups has to go through you, and this is profitable. However, this also leads to a brittle network. If you lose the people with high betweenness, the network breaks down as the groups become isolated.

If you take a network view of the economy, you become worried about the overall structure of the network. You build links between people so that there is redundancy in the network (network weaving!). This is the strategy that O’Reilly Media has used very successfully. In a network economy, we try to build up the structure of the network to increase resiliance.

Finally, thinking about the economy as a network helps with innovation. In an exchange economy, you just have to get your new ideas out there. If they are better, people will buy from you. Everyone that has ever tried to get a new idea to spread knows that it’s not this easy. We have to get people to disconnect from whatever ideas they’re currently using and adopt ours. If we think of the economy as a network, this process makes sense. Our innovative ideas (new products, newservices or new ways of doing things) have to build new connections. Often this means that we need people to break old connections. This is the central problem in idea diffusion.

The economy is a network. Think about it this way and suddenly we move beyond transactions. The nature of the economic ties between us becomes much more important. These ties involve money, time, attention and trust. If we pay attention to these four things as we build up our economic network, we’ll start building a thicker, more resilient economy.

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