Archive for category aggregate
Make Your Own Map to Make Novel Connections
Connecting ideas is the fundamental creative act in innovation.
If this is the case, how do we get better at it?
I was being interviewed in my office by a student yesterday for a project that she’s doing. As we talked, she kept looking at my bookshelves, with an increasingly confused look on her face. Finally, she said “this is off-topic, but what exactly do you study?” She had stumbled across one of my strategies for connecting ideas creatively – reading very widely.
Here is how I approached this issue in an earlier post:
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.
The reason for this is that the articles in my RSS feed that trigger ideas are completely dependent upon my unique set of experiences, including all of the things that I’ve read and done previously. It reminds me of the idea of psycheography that was developed by Guy Debord and The Situationists (it should be noted that they would be horrified at the use of these ideas in a context that has anything to do with business, but I guess this is part of building novel connections between ideas!).
Consider this map of Paris:
It shows the sections of the city used by a student over a period of several weeks. There are two important points to think about this with this. First, each person’s map of the city they live in will be unique. My version of Brisbane will by fundamentally different from that of everyone else that lives here. The same is true for all cities. Second, most people use only a very small percentage of the city in which they live. The student’s version of Paris is actually quite a small amount of the overall city.
The Situationists’ response to this was the dérive:
One of the basic situationist practices is the dérive [literally: “drifting”], a technique of rapid passage through varied ambiances. Dérives involve playful-constructive behavior and awareness of psychogeographical effects, and are thus quite different from the classic notions of journey or stroll.
In a dérive one or more persons during a certain period drop their relations, their work and leisure activities, and all their other usual motives for movement and action, and let themselves be drawn by the attractions of the terrain and the encounters they find there. Chance is a less important factor in this activity than one might think: from a dérive point of view cities have psychogeographical contours, with constant currents, fixed points and vortexes that strongly discourage entry into or exit from certain zones.
This might seem a bit abstract, but there are some important implications here for innovation, including:
- Identify the paths you normally take through information: the world of information is even bigger than a city. Each of us takes a unique path through this every day. What is yours? What are the limits that this path imposes on the ideas that you have and the connections you make?
- Introduce some new paths through this information: the dérive was a method for finding a way out of the normal paths one takes through a city. How can we do the same with information? Twitter can work as a serendipity engine, but to achieve this, you need to consciously connect and pay attention to people that have backgrounds and interests that are quite different from yours. And again, there is great value in reading widely.
- Make your own map: I’ve been telling my MBA students that their assessments should reflect their own map through the materials that we’re working on together – each person’s will be unique because they are applying the ideas in a unique situation. In other words, they have to make their own map through the material. So do you.
All of this is probably just a way to rephrase what John was saying when he was telling us to Be a Hedgefox!
The bottom line is this – to increase the quality of our innovative ideas, we have to figure out a way to make novel creative connections between ideas. To do this, we have to find a way to access ideas outside of our normal patterns of thinking. We have to make our own map.
Bad Filtering Kills Businesses
If your business model is based on information, and whose isn’t these days, then you need to be able to aggregate, filter and connect. While reflecting on the death of Borders Books, I thought of three stories of filtering in retail.
First Story: Tower Records
In the mid-80s, I went in to the Tower Records in Tacoma, looking for Stop Pretending, the new record by the Pandoras. I figured my odds of finding it were high, since there was a big promo display for the record up on the wall.
I went over to the “Rock – Misc P” and flicked through the records. No luck.
I went up to the counter and asked the clerk if they had it. He said no – they’d gotten one copy, and another guy that worked at Tower had bought it. I asked them why they had the display on the wall, and he told me that the guy that bought the record really liked it, so he made the display.
Then I asked if another copy was coming in. No. Why? Because for records from independent labels, the buying policy was to send one copy to each store. If they needed more than one copy, then it had to be special ordered.
There are five forms of filtering, and this is an example heuristic filtering.
Heuristic filtering is rules-based, and this is a great example of a dumb mechanical process. It’s dumb because there’s no learning (“hey, people in Tacoma seem to like the Pandoras, send them more copies of the record”).
This approach worked fine as long as Tower was still the biggest aggregator around. The boycott of Tower that I started in response to this didn’t really seem to hurt them, even though I bought a LOT of records back then.
However, as soon as a bigger aggregator came along – various internet-based options – the Tower business model was toast.
People say that the internet killed Tower Records, but I think it was killed by bad filtering.
Second Story: Borders Books
In the mid-90s, I bought Science as a Process by David Hull, which became one of my all-time favourite non-fiction books. I bought it at the Borders in Westwood, which at the time had a superb science section. Back then, buying was decentralized to each store. So the Westwood Borders, just down the road from UCLA, had a significantly different selection from the Studio City Borders, and every other Borders in LA at the time.
This was expert filtering. Each buyer knew the kind of people that were shopping in his or her store, and they stocked books appropriate to that market.
Unlike Barnes & Noble, which appeared to use heuristics to stock their stores, each Borders was unique.
When Borders came to Australia and New Zealand around 2000, they had individual store buyers then too, so each store was still unique.
After the chain got sold, the individual buyers disappeared – replaced by a central buyer. This was done in response to the threat of online booksellers. The only way to improve efficiency was to cut down on staffing costs.
So Borders went to dumb heuristic filtering.
And now they’re gone too – also killed by bad filtering.
Third Story: Pulp Fiction Bookshop
I while ago I was browsing through the shelves at Pulp Fiction Bookshop here in Brisbane. They specialize in Science Fiction, Fantasy and Mysteries. Their selection in these areas is among the best I’ve ever seen.
A guy walked into the shop and went straight up to the counter. He said “My wife really likes Iain Rankin and Donna Leone. I want to get her a birthday present – is there a similar author that you can recommend?” The owner of the shop said “Yes, there’s a South African author (whose name I didn’t catch) that’s writing really good mysteries, but no one has heard of him (or her) yet. Try that.” The guy bought two books by that author, and left, looking pretty happy.
That’s expert filtering – both in terms of stocking the store and in terms of helping customers.
Even though people can buy books on the internet, and the Australian dollar is really strong, and the parallel importing laws here making it nearly impossible to sell books successfully, Pulp Fiction seems to be doing pretty well.
They’re doing well, because they filter well.
Conclusions
Simply calling these filtering problems is probably too simplistic. And yet, bad filtering definitely played a role in the death of Tower and Borders. Both of them were pretty good at aggregating. Borders was pretty good at using expert filtering to connect people with books they might like in-store, while Tower was less consistent in this area. For a while, Borders was pretty good at filtering, and Tower was always fairly bad at it.
The problems started when the internet killed their aggregation advantages. This caused Borders to do away with the one thing that actually made them distinctive – their expert filtering. Expert filtering is something that Tower never had.
Neither store ever was able to connect people up with products in the way that Pulp Fiction does. This type of expert filtering & connecting is better even than the algorithmic filtering you get at Amazon or iTunes.
The problem is that it doesn’t scale. So it’s hard to have a Borders-sized bookshop with great expert filtering. It’s easier if you specialize in something, as Pulp Fiction does.
To succeed in an information-based business, you must be good at aggregating, filtering and connecting information. And you have to be able to do all three. The stories of Tower and Borders show you how bad filtering can kill a business.
The Value Proposition in Business Models
Posted by Tim in aggregate, business models, connect, filter on 28 August 2010
Anders Sundelin wrote a post earlier this week about the evolution of the business model concept. He does a great job of showing the various ways in which this idea has been operationalized – it’s still surprisingly fuzzy. For the state of the art thinking on business model innovation, a special issue of Long Range Planning has twenty articles on the topic (all free to download through September).
One element that is consistent across nearly all of the different ways of thinking about business models is that of the Value Proposition. A central part of building a successful business model is creating value for your customers. Innovation plays a role here in two ways: first, innovation is the process of executing new ideas to create value, so it is a central part of any new value proposition; second, we can innovate in the way that we create value, not just in the products, services or know-how that we offer.
In order to innovate the way we create value, it makes sense to look at how we create value from information. In general, we do this by aggregating, filtering and connecting. This works for big firms like Amazon, and smaller firms like O’Reilly Publishing.
I ran across two more examples of how this can work for smaller firms this week. The first comes from Seth Godin’s description of Gerald Roush and his Ferrari Market Newsletter. Here is the description of the newsletter:
The newsletter, it appears, was not just lucrative, it was a bargain. It chronicled the pricing, whereabouts and details of just about every Ferrari ever made. If you were a buyer or a seller, you subscribed. If you wanted to run an ad, you were required to include the car’s VIN, which added to Roush’s voluminous database.
The Roush effect involves extraordinary domain knowledge, a market small enough to understand and diligently earning the role of data middleman. The players in the market want there to be one clearinghouse, one authority who can connect the data, see the trends and publish the conventional wisdom.
Often when people talk about “aggregators”, they are referring to places like Amazon or Google, who try to catalog everything (or close to it). This is a great example of how you can effectively aggregate on a much smaller scale. The Ferrari Market Newsletter isn’t trying to aggregate everything, it’s just trying to aggregate all available information on Ferraris.
In this case, the aggregating is combined with filtering to create an comprehensive aggregation of information in a specific niche. The connections are made between people that are interested in Ferraris – most importantly, between those that want to sell one and those who wish to buy one.
Note that this is not algorithmic filtering, as we see on the comprehensive sites. It is judgment-based filtering. It often sounds as though algorithms are the only way to go these days, and as this case shows, that is not at all the case. There are still opportunities to build effective business models based on personal judgment.
Here’s another example, though it is more speculative. On Techdirt, Michael Masnick talks about the idea of building affinity-based music groups. Techdirt is a consistently interesting blog, and you should definitely check it out. Here is how he describes these groups:
… Topspin’s CEO, Ian Rogers, penned an open letter to Guy Hands, the head of (struggling) EMI, suggesting that rather than think of itself as a “record label” focused on promotion and distribution (two things that are easier and cheaper than ever before), it could instead focus on being the smart filter for music listeners today, struggling to find the music they love amidst so much musical abundance in the world. The suggestion was to take some of the key, iconic, bands under the EMI roof, and put them under affinity-based “mini-labels” with other less well known bands, that would appeal to people who liked the more well known band. It seemed like a great idea, which, of course, EMI has not done.
Here again, the value is created through filtering. And as with the Ferrari Market Newsletter, this model would then try to aggregate all of the bands that relate to each other in a specific way. This is a model that has worked very effectively for many years for Dischord Records – and like Masnick I think it has great potential.
Creating a novel value proposition is an essential part of generating an effective business model. There are great opportunities to do this in creative ways. If you focus on aggregating, filtering and connecting, you can build a good information-based value proposition.
Different Forms of Filtering Create Different Forms of Value
Posted by Tim in aggregate, business models, connect, filter on 8 July 2010
Ethan Zuckerman wrote a very interesting post today called What if Search Drove Newspapers? He talks about several different initiatives designed to gauge readers’ interest in different news stories, particularly those that are currently under-reported, and then devising methods for reporting stories on these topics. He asserts (correctly, I think) that this is basically search-driven content development. In particular, this is a strategy that will work well with Google.
Zuckerman concludes by making an interesting point (but you should go read the full post):
I’d propose another way in which search-driven content creation might be evil – it’s a step towards news outlet as search engine and away from news outlet as source of serendipity.
The front page of a newspaper is a statement not just about what’s happened in the world in the previous 24 hours, but what the editor believes is important for you to know about. There’s always more that happens in the world that can fit on a paper page – or even a much larger web page – and the editorial decisions made shape a vision of what you need to know as a reader and what you can safely ignore. Smart editors use this ability to engineer serendipity, pushing readers towards topics they might not have known they were interested in, featuring more obscure content that’s got good storytelling and a high likelihood of capturing a (previously uninterested) reader’s interest. (I wrote about this idea at more length in a post called The Architecture of Serendipity.)
The way to create value in digital business models is by creating value through aggregating, filtering and connecting ideas. The thing that I think is interesting about Zuckerman’s piece is that it basically looks at Google-style filtering as the only method for driving search. This method is algorithmic filtering – This is what people often end up talking about when they discuss news aggregators and other search-driven journalism.
However, there are at least five forms of filtering, and using each of them can create value differently. I think that we need to explore these other forms of filtering in trying to create online value – in the news industry as well as in other contexts.
The editor deciding what is important is expert filtering. This still is used in several contexts, such as at politico.com (discussed previously here). The expert network could be a very interesting approach to filtering new as well.
The main point here is that there is definitely still opportunity to take advantage of judgment in filtering and connecting news stories. Mechanical filtering methods (the algorithm-based approaches) appear to be dominating right now, in large part because of Google’s current gigantic footprint on the internet.
This does not mean that this is the only way to go, though. In order to create value with one of the different forms of filtering, you have to think through very carefully how you are going to do each of the aggregate, filter and connect steps. I’ve been arguing for a long time that the money in digital business models comes from filtering well, and that the firms that realise this are the ones that will do well. A business model with mechanical aggregating, and judgment-based filtering and connecting should still work. It might not be all things to all people, but then, very few successful business models are.
Grassroots Innovation
Veronica Vera pointed me to a great talk by Anil Gupta from TEDIndia. He talks about grassroots innovation, and methods for getting ideas to spread in poorer regions. It’s a fascinating talk:
Innovation in developing countries is a wildly unappreciated phenomenon – there are incredibly interesting things going on in places like India, China and Brazil. Some of them are built around finding innovative ways to provide goods and services to poorer people at much lower costs. Aravind Eye Care and the Tata Nano car are just two good examples of how this works.
Gupta is talking about something different though. He is not approaching poor people as consumers, but as inventors. This is reflected in one of the slogans of the Honey Bee Network – minds on the margin are not marginal minds.
The Honey Bee Network has done some great work in cataloging thousands of inventive ideas that people have developed. Most of them are things that make their own lives better, but many of them also have much wider potential applications. There are several important things that we can learn from this.
- Innovations diffuse through networks – inventions inventoried by the Honey Bee Network have gone through two steps of the innovation process. Someone had a great idea, and they figured out how to make it work. The next step is to get the idea to spread. The HNB takes a network approach to getting people to share ideas. Their objective is the creation of technology commons – ideas are free for people to people use, but a license is required for firms. By cataloging the ideas in one central registry, it is much easier to help people connect up with the ideas. To get the ideas to spread they are creating a network.
- Use a portfolio approach to take advantage of the long tail of innovation – one of the big issues in diffusing these ideas is that many of them are of use to a relatively small number of people. Guptil argues that this should not discourage attempts to get the ideas to spread. By developing a broad network of people interested in grassroots innovation, it is easier to locate the people in the long tail. The central registry of ideas makes it relatively easy to sort through them and find ones that are appropriate to use in particular circumstances.
- Not all good ideas come from where we are – Guptil says this when trying to encourage Indians to be more willing to adopt ideas from China and Brazil. The idea applies more broadly too. It doesn’t matter where you are – there are plenty of great ideas that come from someplace else. It benefits us to be humble enough to realise this and to learn from others.
This is actually a great example of an aggregate, filter and connect value creation strategy. The Honey Bee Network does all three very effectively. They have aggregated over 10,000 great inventive ideas from around the world. By assessing and describing each one, they enable potential adopters to filter through this huge database to find the ideas that will be most useful. And they have created an extensive, strong network that they can leverage to connect ideas to ideas, and ideas to people. This is how they get the great ideas to spread.
There are great ideas everywhere – the key to innovation is developing systems that allow us to test these ideas and get them to spread. The Honey Bee Network is a great example of how to build a platform that enables the process of innovation to take place – even in locations that many people don’t often think of as innovative. There’s a lot we can learn from this.
Here is a full slide show from Anil Gupta that has more detail on a lot of the examples that he uses in his TED talk:
What’s the Best Idea?
Posted by Tim in aggregate, connect, filter, innovation strategy on 16 April 2010
Over the past couple of weeks I’ve been participating in an innovation jam organised by Kate Morrison from Vulture Street Innovation Services – it’s been a fascinating experience. I’ve talked about jams before, but it’s been great to get deeply involved with one. I’ve been thinking about this one through the aggregate, filter and connect lens, and I’ve learned some interesting things about using these kinds of tools to aid innovation.
- The first point is that the idea generation process was clearly tied to strategy. The group sponsoring the jam has specific strategic objectives that they are trying to meet, and the questions that were asked address these goals. These objectives were identified by looking at the needs of the sponsoring organisation and their customers.
- Second, the jam invited specific people with an interest in these objectives to participate. In other words, as I said yesterday, people and process were considered first. In this case, this was part of the filtering process – ideas weren’t solicited from everyone, there was filtering right from the start.
- After people were filtered, then the jam process itself ran over two weeks – this was the aggregation stage. As is usually the case with this kind of exercise, participation followed a power-law distribution that looked roughly like this:

There is almost always a disproportionate contribution from the most prolific contributors. However, as in the example above, the most popular ideas (shown by the stars) came from people from all across the range of participation. This is normal for most idea generation exercises.
- Throughout the jam, people voted on the ideas that they liked and disliked. This was one form of filtering. However, the really interesting part is what this organisation did after the voting closed. Today they held a workshop where they invited all of the participants to come and select the ideas that were the best ones to take forward. About 40% of the people that contributed to the jam came along to today’s event. So the process looks like the one used by linux, and icanhazcheesburger:
This is the way that crowdsourcing initiatives often work. The ideas are gathered from everyone (aggregating), and then a smaller group selects the ones that are most promising (filtering), leading to the final content that is then distributed widely (connecting).
- The selection process today was very interesting to watch. Our first step was to filter out ideas that did not match up with the strategic objectives that were initially outlined. This knocked out a few ideas that had been quite popular in the voting. Some of these were off-topic, and some were too vague to be executed. Once we had done that, we had discussions within small groups about which ideas were best, and each group picked two or three to develop further. This step is critically important – this is where connecting plays a huge role. We were connecting the ideas to the strategic objectives, and in several of the groups we connected up several of the ideas that had been submitted to form more coherent plans around the best ideas.
- Once all the groups had picked the best ideas, we reconvened with everyone and heard about the eleven best ideas (we started with 48 in the morning). These will be developed further, and then brought to the project sponsors for implementation.
One interesting point – for all my talk about ideas being the least important part of the innovation process, I ended up being pretty strongly attached to the idea that I had contributed. It had done well in the voting, and it also did well in the group discussion today. I’m convinced that it is a good idea, and I’m going to carry out myself regardless of how this process ends up. Typical behavior for innovators – you do have to be stubborn about your ideas sometimes. Still, I do think that idea execution is the most important part of innovation.
Overall, I think this was an excellent process. The jam + workshop method is able to combine crowdsourcing with a good level of strategic thinking and judgement. The ideas that had been most popular in the voting didn’t all end up in the final eleven, and several that did had been less popular during the ideation part of the process. At least one idea that I really liked got discarded, and a couple of lousy ones ended up in the final mix, but despite that, I think the process worked really well.
So that’s how aggregate, filter and connect can work on smaller-scale innovation projects. You can see how all three procedures were used to build a system that is effective overall. The biggest lesson from all of this is that idea generation processes must be supported by selection and implementation processes as well.
What’s the best idea? Hold an innovation jam and find out.
Innovation Vision
Posted by Tim in aggregate, business models, connect, filter, innovation strategy on 30 March 2010
How do we decide what our innovation strategy should be? Jeffrey Phillips says that we don’t need an innovation strategy at all, we just need a strategy, and it should have innovation embedded within it. That’s pretty consistent with what I’ve said here before as well when I talked about four different ways to integrate innovation and strategy. But given that, what do we do?
A good first step is to figure out where you want to be positioned. Tom Fishburne has some very good advice in this:
I blogged a few months ago that companies can be classified either as Rule Makers, Rule Followers, or Rule Breakers. Most companies duke it out amongst themselves as Followers, trying to gain share against the market leader by playing the rules of the market leader. …
Instead of obsessing about market share, think market creation. Become “the only ones who do what you do”.
This is certainly the way to think if you’re working on radical innovations. In an interview that just came out, Roberto Verganti argues that the best way to do this is to work on innovating the meaning of your products and services:
In the blog I mentioned that companies that are focusing on stripped-down “value” products risk making the mistake of assuming consumers care more about utility and low price than meanings. In the current ‘Great Recession’ meanings are becoming even more important, and companies should not think consumers care less about the emotional and social dimensions of products.
Although it is counter-intuitive, utility is not the only thing that matters to consumers. Even when they are hard pressed financially they don’t want to feel poor.
Yes, they do care about prices and want to spend less. If you have a lot of money, who cares? If you have less money, you care a lot about how you spend the money. Every time you spend your money, it is a very emotional and symbolic act.
Another way to think of this is to find a way to do something that people really believe in, as suggested by Hugh MacLeod:

Of course, this approach can be risky. The chances of failure are non-trivial. On the other hand, the one sure way to fail at innovation is to try to avoid failing. Scott Anthony makes this point nicely in an interview that just came out:
Interview Question: A famous innovation story is about Bank of America, which mandated that 30% of ideas had to fail. Google also had a similar working line with 20% of the employee time being spent on side projects. What’s your take on such strategising?
Scott Anthony: Those are actually two different strategies, and generally I like the Bank of America one more. That metric tells people that it is acceptable to take some amount of risk. If you never tolerate failure what you eventually get are very close to the core, incremental ideas. Those are fine, but won’t produce blockbuster results.
The Google approach, which 3M has done for a long period of time, works well in particular cultures. But it works less well in organisations that are still getting their innovation legs. All things being equal I would rather have three people spending all of their time on innovation than 100 people spending 10% of their time on innovation.Part of the issue with replicating Google’s ‘20%’ system is there aren’t many people who have an end to end approach to innovation that is like Google’s. And if you copy one piece without the surrounding elements, it just won’t work.
So we have to be prepared to fail, at least with a few ideas. The key point here is to make the failures happen as quickly and as cheaply as possible. But we have to do it, even if it’s risky. After all:
The ROI on innovation is survival
— Andrew Howlett, CEO en Rain
That all looks pretty familiar, doesn’t it? Or maybe not. All of the quotes were included in my post yesterday, but there I asked you weave your own story around them. That was the least successful post that I’ve written in over three months, at least according to views, retweets, and every other metric that I normally look at. Why?
I think it says something pretty important about the aggregate, filter and connect idea – that to create value you have to do all three things. Yesterday, I only aggregated and filtered. All of the quotes were things that came through my aggregating tools – primarily the RSS feed and twitter. I filtered through all of that, and found four items that created a theme – at least inside my head. So I put them out there to see if they’d resonate with you in the same way. It doesn’t appear as though they did.
Today’s post might not be much better, but at least there’s a coherent story in it. That’s because in addition to aggregating and filtering, I connected up the ideas. In order to create value that people are interested in, you need all three components.
This also illustrates an interesting point that is currently being discussed. It started with Robert Scoble talking about the tools that are needed for curation. I love the way that he describes curation:
This is a guide for how we can build “info molecules” that have a lot more value than the atomic world we live in now. First, what are info atoms? A tweet is an atom. A photo on Flickr is an atom. A conversation item on Google Buzz is an atom. A Facebook status message is an atom. A YouTube video is an atom.
Thousands of these atoms flow across our screens in tools like Seesmic, Google Reader, Tweetdeck, Tweetie, Simply Tweet, Twitroid, etc.
A curator is an information chemist. He or she mixes atoms together in a way to build an info-molecule. Then adds value to that molecule.
This prompted interesting responses from Joanne McNeil and Erica Glasier. They both have some issues with Scoble’s post. But I think that really, both of them are responding to the more widely-held view of what “curation” is – more of an aggregate-filter process, like yesterday’s post. I think that Scoble is pretty clearly talking about an aggregate-filter-connect process. So maybe we need a new word for what he’s talking about?
In any case, I thought it would be fun to experiment with two different approaches to compiling and presenting related information. Which do you think worked better?
Connecting Ideas is the Fundamental Creative Act in Innovation
Posted by Tim in aggregate, connect, filter, innovation on 18 March 2010
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 emphasising, 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 repurposing. 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 organisations 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.
Information Wants to Be Free?
We often hear that “information wants to be free” – but does it really? If it does, why did my research partners and I just pay $13,000 to get a copy of this database?
Now that’s admittedly 13,000 Australian dollars, and once you take exchange rates into account it comes out to — a whole lot, in any currency. Why is it worth that? And why did we get it? Alert readers will be able to guess that the answer to both questions is aggregate, filter and connect.
This is a concrete example of creating value from information in both cases. First off the database. It is a compilation of data about strategic innovation alliances going back over 30 years. The data has been aggregated from public sources. It has also been filtered – out of all of the available news about strategic alliances, the original researchers have filtered out all of the ones that are not innovation-related. They’ve then also aggregated data about the objectives of the alliances, start and end dates, industry, and several other things. And they’ve connected all of that data together into a database. By starting with widely available information, they have used aggregating, filtering and connecting to create a valuable resource for researchers.
The people that have put the database together have already done plenty of analyses of the data, and published many papers on their findings. So why would we pay for data that has already been pretty thoroughly worked over? Because we can aggregate, filter and connect too. In this case, we’re paying them for most of the aggregating and filtering, but we have some unique connecting capabilities that makes it worthwhile for us. I have some skills in longitudinal data analysis that are fairly rare – connecting these with the data will create new information. My primary collaborator has developed some unique economic theory, which we’ll connect with the outcomes of my network analyses. By connecting our unique skills and knowledge to a database that anyone can buy, we’ll create new value.
Our objective is to provide some practical insights that will help organisations manage innovation collaborations more effectively. Studies show that somewhere between 50-80% of all innovation alliances fail to meet their objectives. If we can figure out a way to improve these outcomes it would be quite valuable.
So the next time someone tells you that “information wants to be free”, remind them of the entire quote from Stewart Brand:
On the one hand information wants to be expensive, because it’s so valuable. The right information in the right place just changes your life. On the other hand, information wants to be free, because the cost of getting it out is getting lower and lower all the time. So you have these two fighting against each other.
And then remember that the way to create the expensive information is to aggregate, filter and connect.
Why Your Great Idea Will Fail
There are a few reasons why your great idea will fail. The main one is that it will fail because it isn’t executed, or it isn’t execute well. We’ve talked about the problems with focusing just on ideas many times before. Last week I read an outstanding post by Matt Perez and realised why this is a problem. Here is one of the key parts from Matt’s post:
As I’ve been saying in several posts, I think it is obvious by now that more and more the future will be dominated by companies that can keep up a consistent stream of innovation. Given the system today, patents are a necessary evil for some industries, but woe to those who focus solely on protecting their one (and only) brilliant idea. Better to spend money and effort in creating and sustaining a culture (and processes and metrics) that makes innovation possible, even disruptive innovations.
As I read this, I realised that the issues with ideas and innovation are a stock and flow problem. When we focus just on compiling ideas, we are working on increasing our stock of ideas. Often, when we do this, we think that more ideas are better.
The problem is that better ideas are better, not more ideas. In order for this to make sense, we need to think about the flow of ideas. This is why I think that Matt’s point about the importance of having an innovation culture and process is so critical. We need to be able to translate ideas into action. That is why tools like the Innovation Value Chain are so effective. It’s not that the model is perfect, or the only tool to use. But it works because it gives us a feel for the way that we process ideas – we need to generate good ones, we need to select the most promising ones to try out, and we need to get our great ideas to spread. We miss a lot of these critical steps if we only focus on building our stock of ideas.
In arguing this point, it is easy to discount idea generation too much. As Harold Jarche points out, we need both stock and flow to make things work. But the most common mistake when firms try to become more innovative is to focus entirely on building their stock of ideas, which is why I think it’s important to emphasise the importance of building a process that facilitates idea flow.

Hugh MacLeod makes this point in a different way in his post today:
Products are idea amplifiers. The molecules and/or bytes are secondary.
This gets at the importance of the last part of the Innovation Value Chain – getting ideas to spread. And it also illustrates the importance of good quality ideas – if everything that we are trying to sell is based on ideas, then quality is clearly important. But at the same time, we have to execute them, and we have to get them so spread.
So your great idea will fail if it is only part of an idea stock. If it’s your one great idea, that you hang onto no matter what, the odds of succeeding are low. On the other hand, if your great idea goes into an idea flow process, then your chances are better. We need “consistent streams of innovation” to win – and for that, we need to concentrate on improving our idea flows, not just increasing our stocks.
(Photo from The Stock Solution Photo Agency under a Creative Commons license, and the cartoon is the latest from Hugh MacLeod’s daily newsletter, which you should subscribe to)








