Archive for category filter
Finding the Best Way to Fail
Posted by Tim in filter, innovation strategy, selection on 14 March 2010
Nancy and I were talking about a kind of strange newspaper article that her sister sent her discussing the upcoming release of the DSM-V (the official diagnostic manual for mental illnesses). The author of the article was a psychiatrist advocating going back to the 19th century definition of depression – melancholia. I joked that we might as well go back to using phrenology.
If you’re not familiar with it, phrenology was a diagnostic system [sic] based on the idea that the bumps on your head could tell you something about the brain structures underneath the skull. The theory goes on to suggest that the different brain structures reflect different personality traits. As a science, phrenology was discredited a long time ago – around the same time we stopped talking about “melancholia”.
But Nancy had a fascinating response to my joke about phrenology. She said (approximately):
Phrenology was actually really important because it was the first time that people started thinking about the localisation in the brain. Before that, they thought of it as a pretty undifferentiated organ – like a kidney – where each part did the same thing. So phrenology was actually one of the first steps towards modern neuroscience.
That reminded me of a great post by Randy Haykin about the Apple Navigator (which I talked about earlier here). Haykin talks about how many of the key features of the Apple iPad were first introduced in the Apple Navigator – a prototype from 1987 that never launched as a product.
The stories of phrenology and the Navigator show how both science and the economy are evolutionary processes. They both build on earlier ideas to create new ones – usually through creating new combinations. Phrenology failed as a scientific theory, and the Navigator failed as a product, but both contained ideas that could be combined with others to form new, better ideas. We learned from the failures.
That’s why a lot of people, including me, advocate developing a tolerance of failure when we’re innovating. Failure gives us a chance to learn, and it helps us execute ideas that might form building blocks of better ideas in the future. If at least some of our ideas aren’t failing, we’re not trying out enough new things.
However, failure also has consequences – something that venture capitalist Mark Suster forcefully points out in Why the ‘Fail Fast’ Mantra Needs to Fail. His key point is that when fast failure is encouraged, it can have several major drawbacks for start-ups. It can encourage poor business model development, premature abandonment of start-ups, and a cavalier attitude towards the money that others have sunk into the venture.
All of these are valid points. But I think it shows that we are using ‘fast failure’ to cover many different things. One of the key quotes in Suster’s post is this:
You want to talk about the ultimate “fail fast” – how about if you fail before you’ve spent any money building product because you validate there isn’t a big enough market or you can’t make money?
This got me thinking. I think that what we need is a taxonomy of economic failure. We can actually think of failure as a hierarchy that looks something like this:
- System failure (the collapse of communism)
- System component failure (stock market crashes)
- Major firm failure (Enron going out of business)
- Start-up failure (pets.com going out of business)
- Product failure (New Coke tanking)
- Idea failure (Apple Navigator prototyped but never launched)
As you go down that list, failure gets less expensive. When I talk about tolerating failure, I’m talking about trying to set up systems that encourage cheap fast failure. This is usually at the level of ideas. I agree with Suster that encouraging failure at higher levels can be irresponsible.
Innovation courts failure. Not every great new idea will work – and since it is nearly impossible to tell in advance which ones will work and which ones won’t, we have to find cheap, quick ways to test them out. This can be done through the use of experiment as in rapid prototyping combined with iteration based on feedback, through the use of modelling or other simulations, or through the use of a screening tool like the stage-gate process.
The main point is that we need to try to encourage failure before new ideas get too embedded into the economic network. At the top level of the failure hierarchy, failure causes enormous disruption and pain, because those parts of the system are so deeply interconnected. It is much better for ideas to fail than it is for products, firms or economic systems to do so.
(photo from flickr/evansville under a Creative Commons License)
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)
Three Blogs I Love
Posted by Tim in filter, innovation, networks on 27 February 2010
I’ve spent the past couple of days reading an astonishing number of excellent blog posts. I share nearly all of them on my twitter feed, so if you want a compilation of those, check that out. Today I thought I’d share three different blogs which always seem to have great content.
First up is Innovate on Purpose by Jeffrey Phillips. Phillips writes for managers and others that are involved in the innovation process. The posts here are fairly concise, unadorned, and nearly always exactly correct. I haven’t run across any other innovation blog that I find myself agreeing with so consistently. Here is a clip from a recent post called Just Do Something:
There’s always something you can do, and starting now is much better than starting when you finally get the OK. In many firms, the OK may never happen. Create a small innovation capability and generate ideas about the future, new product and service ideas, and help other teams generate ideas. You’ll attract others who have similar needs and interests and gain incredible credibility. Eventually you’ll be the go-to person for innovation. Don’t laugh, I’ve been in at least two organizations where the head of innovation was simply the person who started doing innovation and was eventually recognized as the expert.
If you’ve been reading our blog regularly, I’m sure you can see the parallels between that message and some of the things that John and I are saying consistently. Phillips is also a regular contributor to Blogging Innovation, another outstanding innovation resource.
Next up is Network Weaving, written by June Holley, Valdis Krebs and Jack Ricchiuto. This is one of my favourite network analysis blogs. They’ve each been doing organisational network analysis for many years, and their experience and depth of knowledge comes through each post. Here’s an excerpt from a recent post by Ricchiuto called The 4 Laws of Networks:
Innovation = learning x diverse connections
I disagree with the argument that innovation is the child of desperation. I wish it was so, because if it was, we would be on a planet devoid of incredible amounts of preventable child deaths, failed economies, and the rest of what would otherwise be tragedies that could be prevented by innovations of all kinds. The pragmatic reality is that innovation happens at the intersection of learning and cultivating diverse connections. When you have diverse connections in a network, learning almost cannot not happen. Networks literally become learning disabled if the connections become too homophilous and without learning, no innovation is possible.
One of the subtle points of this post is that all four laws involve multiplication – not addition. It is an excellent example of the increasing returns that are inherent within networks. All of the posts on Network Weaving are like this – they make good points on the surface, and there are also insights lying underneath as well.
The last blog I’d like to highlight is This Week in Review by Mark Coddington. Coddington started his weekly review of news relating to the current state of journalism on his own site, but it was recently picked up by The Nieman Journalism Lab at Harvard. There are a couple of things that I love about Coddington’s blog. The first is that it is a tremendous resource. Personally, I’m interested in what’s going on in journalism, particularly from a business model standpoint. However, because it isn’t my core area of interest, I don’t have time to read everything on the topic. It is Coddington’s core area of interest, and he does an outstanding job aggregating information, filtering it down the key stories each week, and connecting up all the ideas into a coherent narrative. Here’s an example from this week’s post, discussing a great post by Jay Rosen:
Innocence, objectivity and reality in journalism: Jay Rosen kicked off some conversation in another corner of the future-of-journalism discussion this week, bringing his influential PressThink blog out of a 10-month hiatus with a post on a theme he’s been pushing hard on Twitter over the past year: Political journalists’ efforts to appear innocent in their reporting at the expense of the truth.
Rosen seizes on a line in a lengthy Times Tea Party feature on “a narrative of impending tyranny” and wonders why the Times wouldn’t tell us whether that narrative was grounded in reality. Journalistic behavior like this, Rosen says, is grounded in the desire to appear innocent, “meaning a determination not to be implicated, enlisted, or seen by the public as involved.” That drive for innocence leads savviness to supplant reality in political journalism, Rosen said.
The argument’s been made before, by Rosen and others such as James Fallows, and Joey Baker sums it up well in a post building off of Rosen’s. But Rosen’s post drew a bit of criticism — in his comments, from the left (Mother Jones), from the libertarian right (Reason), and from tech blogger Stephen Baker. The general strain running through these responses was the idea that the Times’ readers are smart enough to determine the veracity of the claims being made in the article. (Rosen calls that a dodge.) The whole discussion is a fresh, thoughtful iteration of the long-running debate over objectivity in news coverage.
That’s the other reason that I love his Reviews – they are a fantastic example of creating value through aggregating, filtering and connecting. If you click through to all the links from just that story, you have about a half hour of rally interesting reading to do. But you get a pretty good feel for what’s happening from Coddington’s summary. That’s not mere aggregation, nor is it simply curation. To create value in this way you need all three parts – aggregating, filtering and connecting.
So those are three of my key resources. I hope you check them out yourself!
How to Fail at Innovation
Posted by Tim in evolving economic entities, filter, selection on 25 February 2010
The way to fail at innovation is to try to avoid failing.
The idea of failure has popped up quite a bit this week for some reason. Innovation is filled with tensions that we have to become comfortable with if we’re going to succeed. One of the big tensions is between success and failure – when you’re innovating, you can’t have one without the other. In a very interesting post, Arne van Oosterom suggests that this is good argument for emphasising adaptability rather than innovation for many firms, as this eliminates the discomfort caused by the tension between the two.
I am in complete agreement with van Oosterom that adaptability is a desirable trait for organisations to develop. But in doing so, I don’t think we can abandon innovation. I think that we need to develop strategies for dealing with failure.
This was the conclusion reached by both Peter Yates (ex-CEO of PBL, among other things) and Patricia Cross (Non-Executive Director of Wesfarmers and numerous other organisations) in their talks at the Leaders’ Edge Luncheon here in Brisbane on Tuesday. The topic of the talks was ‘Tales from the Corporate Battlefield’ – and it sounds like both of them have been in plenty of battles. And one of the common themes that they touched on is that if you’re doing anything worthwhile, you will experience failures. It’s not fun, it’s not something to be embraced, but it’s inevitable.
This theme was also addressed by Hutch Carpenter in a fantastic post this morning. In making the point that innovative firms will fail, he included this picture:
He includes this quote from Jeffrey Phillips – one of the best innovation bloggers around:
As Edison and countless others have demonstrated, you rarely get it right the first time, and if you are stymied by early failure, then you’ll never find and implement the best ideas. Innovation, as has been pointed out by individuals with far more to say about it than me, will create some failures. Your job isn’t to avoid the failures, since you can’t predict them in advance, but to reduce the cost and impact of the inevitable failures. In other words, keep moving.
So there’s the contradiction that we have to deal with – if we’re going to successfully innovate, we have to fail. The key is to figure out how to do it as cheaply as possible. As I’ve said before, if everything that you try works, then you’re not trying enough things. These contradictions are one of the things that makes managing hard, but it’s also one of the things that makes good managers so valuable. Failing isn’t fun, and it’s natural to try to avoid it. However, it is a necessary element of success.
In other words, the one guaranteed way to fail at innovation is to try to avoid failing.
Establish Authority by Creating Value
Posted by Tim in aggregate, book riffs, connect, filter on 22 February 2010
One of the best ways to build connections within the economic network is to be an authority – and since revenue often follows connections, this is a useful strategy to consider. How do you become an authority? I’ve run across a couple of suggestions recently.
First up – this from the JournaMarketing Blog (I’m not trying to pick on the guy, his recent posts are much better, and worth checking out):
Services like Friendfeed make it easy to pull together information from a lot of different sources. So if you’re looking for a way to become an authority in your field, find the 5-10 top sources in that field, and pull their feeds into one location — on your own website. You’ll earn the goodwill of those other sources by linking to their content. And you’ll gradually become the 1-stop shop for anyone looking for information in your field.
Compare that process to the one outlined by Chris Brogan and Julien Smith in Trust Agents:
Say that you’re asked a question by e-mail about a specialty of yours… You could just respond by e-mail, but you don’t. Instead, you write about it on your blog. You point the person who made the original inquiry to what you wrote, so taht person gets what he or she wants; but now, anyone else can see it as well. People who arrive via Google by searching for similar information can visit and post comments weeks, even months, later. Your blog post, which used to offer answers to typical questions asked by a few people, has now become a resource…
Imagine that you do this 500 times. Over time, you’ve probably been asked 500 questions about your specialty; suppose you had answered all of them on your blog. These 500 posts now make up a pretty hefty set of resources, with a lot of insider information and tips, and you’re heping a fair number of people. As you do so, you’re starting to become known for your expertise.
So, our choice: establish your authority by creating value for people, or do it by appearing to create value. Which do you think will work better? Which person are you most likely to believe? Which takes the most work?
Aggregating by itself does not create value – this is a common fallacy doing the rounds these days. To create value, you have to aggregate, filter and connect information. In the Trust Agents example, you are not just aggregating the stuff that you know. You are filtering it so that it addresses specific problems that people have, and you are connecting up ideas to help solve those problems. And you are also connecting your solutions to people, actively through e-mail and telling people about your blog, and more passively through search engine visits.
The difference, of course, is that it takes a lot more effort to create 500 good quality blog posts. It will probably take more than a year, or even longer. And even then, you’re only “starting to become known” as an expert. But that’s what it takes to establish genuine authority. You have to put in the hours – there’s just no way around it. Of course, the payoffs (both emotional and financial) to being a genuine authority are generally higher as well.
These ideas apply whether you are building a personal brand, or whether you are creating an innovative business model. You need all three skills to creat value. You have to be able to aggregate, filter and connect to establish authority by creating value.
(Photo from flickr/Wessex Archaeology under a Creative Commons License)
Aggregate, Filter & Connect for Smaller Firms
Posted by Tim in aggregate, business models, connect, filter on 15 February 2010
We know the story by now: as it becomes less and less expensive to transfer digital content, the price firms can get for information-based products and services is being driven down. This has led to chaos in the music, news and publishing industries. So everyone in these industries is doomed, right? Wrong. I believe that successful business models in information-based industries can be built if you focus on three key skills: aggregating, filtering and connecting.
I’ve talked about how this is true for people, and also for large firms like Amazon. But what about medium-sized companies? Doesn’t all the money go to the big ‘aggregators’ like Amazon and Google? Not necessarily. A great example of a smaller firm that has been very successful in the information-based industry of publishing is O’Reilly Media. Today I’d like to talk a bit about why I think they are doing well. But first, let’s hear why Tim O’Reilly thinks they’re doing well:
Tim O’Reilly makes the argument for Open Publishing @ TOC 2009 from Open Publishing Lab @ RIT on Vimeo.
Right off the bat, he characterises the firm as ‘connectors’. O’Reilly works to spread ideas (connecting ideas to people), and then uses the connections that are made to generate money. It’s a pretty good business model, and one that I think will continue to work. Here are some more examples:
- First off, O’Reilly filters by specialising – they are not a general publisher. Here is Tim O’Reilly’s broad business goal:
Here’s mine: To become the information provider of choice to the people who are shaping the future of our planet, and to enable change by capturing and transmitting the knowledge of innovators and innovative communities.
To do this, they primarily focus on publishing books on the internet and open source technologies, programming, and systems administration are probably their core areas, but they also look at other digital topics like security, digital photography and information design. When building their business models the first thing that smaller firms need to do is to find something in which they can be the absolute best.
- Once they have filtered by specialising, O’Reilly aggregates in a couple of ways. One way is through the conferences that Tim O’Reilly mentions – they draw thousands of people that are interested in the O’Reilly core topics. It’s likely that most of the people that are interested in writing on these topics show up at these conferences. They also aggregate by building a community around their work. In addition to the conferences, they do this by hosting online user groups and forums, providing training, and giving away a lot of relevant content through blogs, white papers, and free downloadable copies of many of their books. All of these activities pull in the people that are most likely to contribute great new content themselves.
- When I talk about connecting at the personal level, there are two directions of connections. People connect up ideas, and this is how they create value. They then connect their ideas to people – this is how they get their ideas to spread. It is roughly the same for firms. First, they create value by connecting up their value network. In the case of O’Reilly, part of how they do this is by taking a highly activist role in the editing process. As John Scalzi humourously points out, publishers provide a number of services that are critically important to authors. Connecting authors to the right resources is a key part of building the value network at the back end – ultimately, it is the value network that creates economic value.
- The second part of connecting for firms is getting your ideas to spread. I think that this entails much more than marketing. It is built upon meeting the needs of your customers and business partners. Again, O’Reilly takes an approach that is different from most publishers. They have been much more willing to provide free downloads of books – sometimes this hurts sales, but sometimes it has driven sales. In both cases, it has helped to build the community. They also get their ideas to spread through the conferences. As this week’s TED conference has shown, when you have limited space available for a desirable event, you can charge people a lot to attend. One of the ways that people get their money’s worth from such events is to tell everyone they know about them – which spreads the ideas.
Tim O’Reilly wrote the book (well, the article) on Web 2.0 – and he uses the principles he has outlined to build his company. There isn’t anything new in any of the individual actions that O’Reilly Media undertakes – the thing that is unique is the way that they have combined things.
This is a great example of business model innovation. They are not delivering books, they are delivering ideas. Once you make that key switch, there are many new opportunities available. The O’Reilly Media business model is based on skills in aggregating, filtering and especially connecting. These are the three critical skills that you need to build successful information-based business models – whether you’re a person, a huge firm like Amazon, or a smaller firm like O’Reilly Media.
Creating Value from Information
How do we create value when we’re busy tweeting, blogging, reading and writing? I have been arguing that to create value from information, we need to have systems in place that allow us to aggregate, filter and connect information – and that this is true for people, and for business models. These are the things that go on all the time behind the scenes when people are creating blog posts, and I thought it would be interesting to see how this actually happens.
Shortly after the unveiling of the iPad, Venessa Miemis wrote a post called ‘iPad: Overhyped Flop or a Case of Great Design Thinking?‘ Venessa has a distinctive talent for writing about complex topics in an integrative and creative manner, so I thought it would be interesting to ask her about how she put this post together.
Tim: What resources did you use to collect & keep track of all of the discussions surrounding the iPad?
Venessa:Ever since I started blogging regularly, starting in November 2009, I’ve used Twitter as my main tool for collecting resources. It’s taken months to build a network, but it’s been well worth it. I’m very satisfied with the quality of tweets that pass through my stream. For the iPad post, it wasn’t too hard to find the articles – the Twittersphere was abuzz with speculation and opinions about the iPad in the week leading up to the keynote and in the days following it.
I saved most of the articles that came from a media outlet (Wired, New York Times, engadget, PopSci), and then paid attention to the articles by individual bloggers that were getting RT’d and referenced the most. I also used the #iPad hashtag to search what people on Twitter outside my immediate network were saying. I saved all of these links into delicious.
Comment: Twitter ended up acting as both an aggregation tool and a filter. The assumption behind Venessa’s strategy here is between the tweets from own network, which is large, and through searching that she would find most of the relevant commentary on the iPad. The filtering aspect is interesting – when we choose to follow someone on twitter, we are in part relying on them to choose the best things that they encounter to tweet about. So our network is doing a lot of the work in terms of filtering.
Tim: Did you use any tools to sort out the themes, or did you just work it out in your head?
Venessa: The themes emerged from the posts themselves. As I read through them, it seemed the implications of the iPad could be framed around three main areas: business, education, and leisure.
Comment: Here’s where I’m a lousy interviewer, because this is the key part of the process and I didn’t ask very good questions here (but see Venessa’s comment for more). But the value in the post is that it did not simply aggregate all the online discussion about the iPad. After collecting all the information that she could find, Venessa selected further by picking what appeared to be the key posts about the iPad.
Then, she connected up ideas by grouping them into themes. For me, this is the entire reason that the post works so well – it is the creative connecting. It’s also interesting that Venessa covers this in one line. It’s very typical that this step is hard to describe. We often talk about themes ‘emerging’ from our collected reading. As I’ve said before, connecting ideas is the fundamental creative act – and when you read the post, you can see how Venessa did this extensively in writing it.
After we connect up ideas, then we have to connect our ideas to people to get them to spread:
Tim: How did you promote the post?
Venessa: At 2:06pm on Feb 1st, I tweeted this:
At 5:03pm on Feb 1st, Tim O’Reilly tweeted:
That day, there were 3,672 visits to the site. The next day had 5,297. The next day had 1,690, and since then there have been between 700 – 1,000 visits daily. The post has received 21 trackbacks, and 121 comments (although I answer almost every comment, so you can subtract that by half). It was one of the top 20 posts on wordpress.com on February 3rd.
At the time of this writing, bit.ly shows the link has been clicked 7,785 times.
I also just found out that the post was mentioned on Leo Laporte’s “This Week in Tech” podcast on February 7th by Baratunde Thurston.
Tim: Can you track how the idea travelled?
Venessa: With over 1.4 million followers, it’s clear that Tim O’Reilly’s influence is far-reaching. Normally when I post a new article, I’ll get around 400-700 visits. Last week I had over 14,000.
I did get a big influx of new followers on Twitter (250+) within those first 48 hours, but I don’t know what percentage of that translated into new blog followers.This has been a really interesting experience, and more a display of the power of influence than anything else. I wouldn’t say the quality of that post was particularly higher than any others I’ve written. It was just super topical and given exposure by a highly influential person within the tech community. In order to get a post go that viral again, I may just have to be patient until Steve Jobs builds hype for whatever comes after the iPad.
Oh, and another thing to mention, which I thought was interesting – though I received 10 times the amount of traffic, I only got about twice as many comments…..so maybe 14,000 visits and only 50 comments. which to me says a lot about the amazing community that has grown around the blog. Though I may not get huge daily numbers with my traffic, a lot of people comment and engage and interact, and that’s what has made the blog so interesting. The posts are primers, but the conversations that follow is where a lot of insights and tips come in. So though I saw the power of influence via traffic, the quality of traffic I already had is unmatched.
Comment: There’s obviously a lot to say about connecting up ideas and people here. First, the traffic spike from Tim O’Reilly came as a result of the quality of idea connections in the post. There were hundreds (probably thousands) of posts about the iPad that week. Venessa’s did well because of the synthesis. That’s what led to the post getting into a traffic network with so many people in it.
I think this sheds some light on the question of the value of transient traffic. Venessa’s blog regularly gets a lot of very good, thoughtful comments as she mentions. This is because of the community that has built up around the blog (connecting ideas to people!). Because that community was already there, I think it made it easier for her to capture some value from the huge spike in traffic that she got. Double the number of comments is actually pretty good – it is sure more than you’d expect if transient traffic is ‘worthless’. Same with an increase of 250 people following on twitter. I suspect that regular RSS feed readers probably took a spike too. If you’re creating good intellectual value, it increases the connective value of transient traffic.
Finally, a lot of people talk about information aggregation as something that creates value in and of itself. Because it is a relatively easy thing to do, this why a lot of people dismiss Amazon and Google as ‘merely aggregators’. But the value doesn’t come from simply aggregating. The real value comes from aggregating information, filtering it effectively, and then creatively connecting ideas.
All three parts of the process must be done well in order to create value from information. I hope that this behind the scenes peek at the making of ‘iPad: Overhyped Flop or a Case of Great Design Thinking’ provides some insight into how the process works.
How to Filter Better
I was at the mall yesterday eating lunch, and I took a moment to listen to the conversations going on around me. They were, without exception, utterly banal. Consequently, I concluded that conversation is a useless tool, and the widespread use of it is nothing more than a symptom of the widespread decline of intellectual discourse, manners, and civilisation as a whole.
Stupid, right? That’s a riff on Clay Shirky’s response to people that complain about the quality of discourse within social networks.
The latest version of that complaint comes from Tom Davenport on the HBR Blog, who asks for us to tweet about something important:
Almost 50 years ago, FCC Commissioner Newton Minow suggested that the then-new medium of television was becoming a “vast wasteland.” One could argue that the same fate is befalling social media.
So here we are again: a promising new medium being used largely for vapid chattering about celebrities. Couldn’t these technologies be used for good?
Here’s an analogy: bird calls. When Nancy and I started birding one of the things that we learned was bird calls. When we’re actively birding, we’re pretty alert to everything, because we want to know where the birds are. However, when we’re working at home, we pay less attention to the calls. And since we’ve got a yard that is pretty attractive to birds, there are a lot of calls.
The vast majority of bird calls don’t contain too much information. The most common form of call is what ornithologists refer to as ‘chip calls’, which are short, brief calls that birds use to let other birds know where they are. It can help them find food (lots of chips in one area), it can keep them from straying from the flock (most of the chips moving further away), and it simply be a Horton-Hears-a-Who type ‘hear I am’ call or an ‘everything’s fine’ call. When we’re working at home, we don’t pay any attention to these calls at all.
However, the birds also have a few calls that contain a whole lot of information. We’re particularly interested in the calls that say ‘predator’. That usually means there’s something pretty interesting in the yard, like a Goshawk, or this Carpet Python:
We only got to see that snake because we heard the bird calls, followed them, and found the snake.
So we’re filtering the bird calls in our heads all the time, waiting to find the interesting bits. We need to do the same thing with conversations (well, maybe not the conversations at the mall food courts…), and social networks like twitter. Most of the words used in these media are like birds’ chips. We need to find the words that contain data. How can we do this?
Howard Rheingold is in the middle of putting together of series of tutorials about Mindful Infotention that explain a few different methods that work.
These are fantastic videos that are well worth your time. In the one above, Rheingold makes several important points. He demonstrates the use of several different tools for aggregating and filtering information on a topic that is of interest to you. He has step-by-step instructions that make it pretty easy to track down specific information from streams that are on average not very useful.
Rheingold also talks about two different forms of filtering – algorithmic and personal. He correctly recommends using a combination of the two. Algorithmic filtering includes tools like PostRank, which uses a formula to determine which posts are most relevant to your search. Personal filtering takes place on sites like delicious and diigo, where you can find the pages that the highest number of people have bookmarked, or the pages that specific people have saved. There’s also using your network to help filter.
Rheingold makes an absolutely critical point at the end of the video – it’s not enough to simply aggregate information, and filter it so that you have only the most relevant information. You also have to do something with it! The information is no good unless you connect it up:
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.
There is a wealth of great information being shared on a constant basis within social media and elsewhere. None of it does us any good at all if we can’t find the bits that are useful to us in solving the problems on which we’re interested in working. But if we filter well, we’ll discover that we actually are tweeting about things that are important.
(I’ll also just note that Howard is currently getting treated for cancer – the prognosis sounds encouraging, and I hope that he pulls through it in good shape.)
Innovation Lessons from The Checklist Manifesto
Posted by Tim in book riffs, filter, innovation on 7 February 2010
How do we deal with complexity? A while ago I suggested that one strategy that we use to handle complexity is that we outsource some of the rote memorisation of facts and routines that we need regularly. This is essentially the strategy that Atul Gawande also advocates in his outstanding book The Checklist Manifesto: How to Get Things Right. The primary theme of the book is that one of the best tools we can use to handle complex situations is a very simple one – the checklist.
The key story in the book is the World Health Organization’s development of a Safe Surgery Checklist. The checklist consists of a series of items to confirm as part of the surgery process. The are all fairly basic things, and they are put into three groups that are asked at three natural pause points in the surgery – before the anaesthetic is administered, before the first cut is made, and before the patient is taken from the operating room. They address very basic issues that you would think wouldn’t be missed, but which often are: confirm that patient’s name, confirm which side of the body the surgery will take place, confirm the procedure, ensure that all surgery team members have introduced themselves by name and role, discuss key concerns for patient recovery, and several others.
After the checklist was developed, WHO tested in eight hosptials around the world – four were in developed countries, and four were in developing countries. They ran the gamut from well-funded hospitals with all mod cons to rural hospitals that had so few resources that they had to sterilize and re-use surgical gloves until they wore out. In the test study, they gathered data from about 4,000 surgery patients across all eight hospitals for three months, then they introduced the checklists into the surgical procedures, and gathered data from another 4,000 operations.
The results are astonishing. Major post-surgical complications fell 36% with the use of the checklist, deaths were down 47%. All eight hospitals saw major reductions in both categories, and from a statistics standpoint, the relationships are statistically significant. The outcomes were published in the New England Journal of Medicine at the start of 2009.
Gawande is a terrific writer, and I recommend the book highly (his earlier books Complications and Better are also great). I think there are several things that we can learn about innovation from this story, including:
- Behavioural innovation is at least as important as product innovation – probably moreso. Drug companies make a pretty big deal out of new discoveries that have statistically significant impacts of 1 or 2% on their target – what would happen if they had a drug that reduced the incidence of an important problem by over 40%? It would be all over the news, and it would be hailed as one of the biggest breakthroughs in medical history. We’ve seen products with even bigger impacts – for example, the polio vaccine. However, the idea that we can reduce the incidence of several major categories of surgical complications simply by acting differently is mind-boggling.
- So why haven’t we heard more about this? People often resist simple solutions to complex problems. Gawande believes that this is because many people think that relying on things like checklists reduces their autonomy, and their ability to act creatively. Gawande’s response:
It’s ludicrous, though, to suppose that checklists are going to do away with the need for courage, wits, and improvisation. The work of medicine is too intricate and individual for that: good clinicians will not be able to dispense with expert audacity. Yet we should also be redy to accept the virtues of regimentation.
Like I said at the start – checklists are great because they allow us to outsource some complexity. This means that we don’t have to think about the basic things, leaving more time and brain resources to deal with the things that genuinely require our skill and judgement.
- We do not interrogate our failures very well. The first step in building an effective checklist is to find the ways that we commonly screw up, look for systematic patterns, and put steps in the checklist to address these common problems. Checklists have been most widely adopted so far in industries where failures are closely examined – flying and construction. When a plane crashes or a building collapses, a lot of effort goes into learning why, and how to prevent the problem in the future. Once we learn the cause of the failure, the checklist helps prevent its reoccurance.
The application of these ideas to innovation is fairly obvious. When you look at innovation as an evolutionary process, it quickly becomes apparent that a number of ideas need to be selected out before we invest too much into them. Most organisations do not spend too much time thinking about the ideas that didn’t work. One of the things that we need to get better at is learning from our ideas that fail. If we do this, then we can build a checklist.
Gawande has a couple of business examples in the book. He talks about three investment firms that have developed checklists to help them evaluate investment opportunities. And he talks about a study by Geoff Smart that looked at what made Venture Capitalists most effective:
Smart specifically studies how such people made their most difficult decision in judging whether to give an entrepreneur money or not. You would think that this would be whether the entrepreneur’s idea is actually a good one. But finding a good idea is apparently not all that hard. Finding an entrepreneur who can execute a good idea is another matter entirely. One needs a person who can take an idea from proposal to reality, work the long hours, build a team, handle the pressures and the setbacks, manage technical and people problems alike, and stick with the effort for years on end without getting distracted or going insane. Such people are rare and extremely hard to spot.
Which VCs are most successful? According to Smart’s study, the ones that use a checklist. Checklists are simple tools that can help us filter ideas and opportunities. They help us outsource complexity, leaving us more mental capacity to connect up ideas in novel ways – which is the key to innovation.
Filtering, Crowdsourcing and Innovation
Posted by Tim in book riffs, filter, innovation strategy, selection on 1 February 2010
How can we take advantage of the ‘wisdom of crowds’ in our innovation efforts? There are some distinct challenges in trying to do this. The basic idea is this: if you get a large number of people to estimate something – the weight of an ox, or the number of jellybeans in a jar, for example – usually the average of all of the estimates is closer to the actual number than any individual’s guess. Consequently, there is a strong argument for taking advantage of this phenomenon if you are trying to get a handle on estimating a particular number. Businesses have used these techniques to improve their sales forecasting for example (Gary Hamel includes a really nice example of how Best Buy used this method in The Future of Management).
Can this work to improve innovation? It’s not as obvious that it will. I’m currently reading You Are Not a Gadget by Jaron Lanier (more on this book in a later post). Lanier has this to say about using crowds:
The reason the collective can be valuable is precisely that its peaks of intelligence and stupidity are not the same as the ones usually displayed by individuals.
What makes a market work, for instance, is the marriage of collective and individual intelligence. A marketplace can’t exist only on the basis of having prices determined by competition. It also needs entrepreneurs to come up with the products that are competing in the first place.
Since the internet makes crowds more accessible, it would be beneficial to have a wide-ranging, clear set of rules explaining when the wisdom of crowds is likely to produce meaningful results… Among other safeguards, I would add that a crowd should never be allowed to frame its own questions, and its answers should never be more complicated than a single number of multiple choice answer.
Crowds can be useful, but also dangerous. Nassim Nicholas Taleb says that crowdsourcing should be avoided in situations where the potential payoffs are very complex, and when we don’t know what the outcome probability distribution looks like. Unfortunately, this is precisely the case for most innovations.
Relying on crowds can lead to innovation problems. Stefan Lindegaard identifies this as one of the common causes of open innovation failure (the comments on that post are worth reading too):
Many companies start off with idea generation platforms hoping that external contributors will contribute with great ideas and/or technologies. Most do not deliver on the expectations as they get more trash than gold.
And in a post that addresses some of the issues with crowdsourcing really nicely, Graham Horton says:
In conclusion, customer idea portals as they are currently popularly advocated will produce limited results; they will only provide suggestions for solutions that are apparent to customers, given their level of expertise and self-knowledge.
All this might suggest that we can’t use crowds to help innovation. However, I think that these two quotes suggest a possible way that we can still take advantage of crowds in our innovation efforts. One of the issues is that we often misunderstand how crowdsourcing actually works. The Lindegaard quote suggests that people think that we can turn to our crowd (customers, stakeholders, etc.) and just wait for the good ideas to roll in. This is in line with a common understanding of crowdsourced systems – people often talk about Linux, for example, as a process where thousands of people write bug fixes for the software, and all of these fixes get put into the program, making it better. This misses a critical step.
That’s a diagram that I made last year to explain to some friends how icanhascheezburger.com works – but it explains Linux just as well as it explains lolcats. The critical step in the process is the middle one. Both systems crowdsource content – Linux crowdsources code, icanhascheezburger crowdsources cat drawings. The problem is, not all of the code works, and not all of the lolcats produce lols. In each case, there is a small group that filters the incoming content. We don’t have crowds creating stuff, and then voting on stuff. We have crowds creating stuff that answers questions posed by the group guiding the process. The answers that work are then selected by that group as well.
This leads to the answer that both Lindegaard and Horton suggest: in order to get useful answers from crowds, we have to have good internal capacity ourselves. Crowdsourcing needs to be guided. To use the crowd in innovation, we need to set the questions. And we need to know enough to be able to figure out when the crowd is giving us good answers.
A while ago I talked about using jams to select ideas. This process follows these principles. The questions being asked are set by the organisation, so the crowd is trying to address a specific problem. And the best answers are not just judged by popularity – there are several evaluation mechanisms that can be used. You can use the votes and go with the most popular. You can use the ideas that were most polarizing. You can take the ideas that are generated and plug them into whatever other system you use (stage/gate, gut feel, whatever).
Crowdsourcing then is another tool that we can use in our aggregate, filter and connect strategies. In this case, the filtering is the critical step. If we don’t filter correctly, crowdsourcing simply aggregates, which by itself doesn’t help us much. And the aggregated crowdsourced answers need to be connected to questions that we know are important. Crowdsourcing is not a panacea, but it can be a useful innovation tool if we use it correctly.
Graham Horton has written a terrific post that looks at which questions we should ask the crowd.
Filtering With Your Network
Posted by Tim in complex systems, filter, innovation strategy, networks on 27 January 2010
In yesterday’s post on Personal Aggregate, Filter & Connect Strategies, I didn’t have room for one key point: one of the key filters to use is your network. When he was in Brisbane last month, George Siemans gave a talk with an example that illustrated this perfectly.
For the past couple of years, he has run a course on Connectivism with Stephen Downes. Here is the definition of connectivism from Downes:
At its heart, connectivism is the thesis that knowledge is distributed across a network of connections, and therefore that learning consists of the ability to construct and traverse those networks.
As I understand it, one of the points of the course is to present students with so much data that they can’t possibly process or understand all of it as individuals. This forces them to create networks to build data-gathering and sense-making networks in order to succeed. There are more details about networks, connectivism and the course in this excellent presentation from Downes (the presentation also discusses Downes’ framework for building knowledge within complex networks, which consists of Aggregate – Remix – Repurpose – Feed Forward).
So as individuals, our network is part of our filtering system. This also points out how the three processes – aggregating, filtering and connecting – interact with each other. In the example of my twitter feed, I discussed this as part of my aggregation strategy. But at the same time, I’m actually counting on people within my network to filter. They’re not sending every little thing that they run across into their twitter streams – they are selecting.
This same process happens as part of the aggregate, filter and connect process for organisations. We can use our networks as aggregating/filtering tools. This is what is happening in customer-led innovation, crowd-sourcing and open innovation. We use our network to increase the flow of ideas into our organisation (aggregate), and we also count on our network to decide which things they run across might be important (filter).
And just as for individuals, these processes don’t work well for firms either unless they are good at all three steps. If you only aggregate, you get overwhelmed with ideas. You need some form of selection process. Both forms of connecting are important too. You must be able to connect ideas in novel ways – this is one of the central skills in innovation. If you’re not generating and executing your own innovative ideas, you run into several problems. Open innovation won’t work, because you’re not bringing anything of value to the table – so why would anyone want to partner with you? Customer-led innovation and crowdsourcing won’t work either, because the skills need to tell which ideas are worth pursuing – your filtering is worse if you’re bad at connecting ideas.
Outbound connecting is also critical – this is how we get ideas to spread. In the case of firms, getting ideas to spread is a critical part of innovatgion diffusion. This is also a network function. Using our networks to help with filtering is essential – both for individuals and for firms.
This is one of the reasons that we are doing research on networks. Knowledge creation within firms is also a network function. John and I recently made a video to use to explain network analysis and our main research project to people that are participating in our studies. Because many of them are in remote locations, we can’t visit everyone. And we figured that showing them a video might help them feel more of a connection with us than simply sending them a document with the same information. Take a look at this, and keep in mind the value of your network in executing aggregate, filter and connect strategies:
UQBS Innovation Networks in Project-Based Firms Information Video from Tim Kastelle on Vimeo.
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.
What Would Google Do? by Jeff Jarvis
Posted by Tim in aggregate, book riffs, connect, filter, innovation strategy on 25 January 2010
The question of how to best adapt to the changes brought about by the internet is of key importance to all organisations that are in information-based industries. According to Jeff Jarvis in What Would Google Do?, the answer is fairly simple: do what Google would. Here is a video in which he outlines the argument in the book (this is from the same session of BRITE ‘09 as Umair Haque’s talk that I discussed yesterday):
Jeff Jarvis at BRITE ‘09 conference from Center Global Brand Leadership on Vimeo.
Jarvis, author of the blog buzzmachine.com, takes an interesting approach in this book. He infers a number of rules for acting more like Google, but he does this without having direct contact with the firm. Because he’s a very entertaining writer, this first half of the book is worth reading. However, in some ways it rehashes ground covered well by Chris Brogan and Julien Smith in Trust Agents (reviewed here), or David Weinberger in Small Pieces, Loosely Joined (reviewed here). The main ideas are that to succeed, you should join network and be a platform (both facilitated by the internet’s linking structure), give control to your customers instead of trying to retain it yourself, and build a business model based on serving niches. There are actually ten rules in the book, but those are the ones that I found most useful. Jarvis reduces these rules down to five in his tips for creating a Googlier you.
For me, it is the second half of the book that recommends it. In this section Jarvis tries to build new business models based on his Google rules in nine different industry categories, including media, retail, manufacturing and public institutions. Each section has two to three examples, and this part of the book is just fantastic. The thing that I like about it is that Jarvis really puts his ideas to the test here – tackling a number of industries that would not obviously lend themselves to following Google rules like car manufacturing, power generation, and restaurants. It is a fascinating intellectual exercise, and I think that a lot of his ideas would be worth trying out. I recommend the book based on this section.
I’ve been thinking about the ideas in What Would Google Do? while talking with an old friend from university who is currently working with a media company in the US. They are grappling with how to best deal with the challenges of online content. One of the things that they are considering is building their own branded media reader, an idea that Jarvis would almost certainly be against (I know that I am!). Valeria Maltoni wrote an interesting post on the topic of mobile news this morning. She included this graphic, which certainly makes the argument for the necessity of some kind of mobile application for media firms:
Maltoni also checks iTunes, and counts more than 3200 iPhone news apps currently available. This definitely means that my friend’s firm must be mobile-enabled – but why build their own e-reader? Why compete directly against Apple and the iPhone (and the upcoming iTablet), and Amazon and the Kindle, and, well, nearly everyone else that is already in this space? Their argument is that having their own branded e-reader will give them control. They can push out their own content to people, and reinforce the brand in that way.
There are all kinds of problems with this argument. First off, why do people need another mobile device that is tethered to one publisher’s content? Are they suggesting that people should carry their e-reader for their content, and a Kindle for books, and a smart phone for everything else? I’ve got too many gadget as it is – there’s no way I need another one. So I’m not convinced that the demand is there for another e-reader. I haven’t seen any of the financials, but I suspect that there is no way to make it pay without fairly massive volume, and I’m skeptical about achieving that too.
Maltoni’s recommendations start with creating great content, and the rest revolve around being more interactive with people – something that is probably easier to achieve with mobile apps than it is with mobile devices.
The e-reader idea also violates at least two of Jarvis’ Google rules. The first is do what you do best and link to the rest. This is very similar to Maltoni’s creat great content idea – that is what the media firm is good at, and that is what they should do. Do they have a competence in manufacturing electronic devices? Well, no. The second Google rule that this goes against is that you have to hand over control to gain peoples’ trust. The idea behind the e-reader is to enhance control, not give it up. Jarvis argues that this is exactly the wrong strategy to be following these days.
The one way to make the e-reader scheme work is to follow another of the Google rules – be a platform. The only way I can it working would be if it were structured to enable people to do a wide variety of things on it. This means having some kind of alliance that will enable book downloading, and an open software architecture so that enthusiasts can build apps for it. It means building in interactivity so that people can rate content, access the other content that they want, and mash it all together. It means directly taking on the iTablet, Kindle and smarphones. If you do all that, the scheme could work.
But I don’t think they want to do all that. So I don’t think the e-reader is the way to go. To find the best way forward, it might be useful for them to ask What Would Google Do?
Changing the Game for News
Posted by Tim in aggregate, business models, connect, evolving economic entities, filter, innovation strategy on 23 January 2010
A lot of people have been talking recently about a Harris Poll that shows that 77% of people in the US say that they won’t pay for online news. Specifically, this is the question they were asked:
How much, if anything, would you be willing to pay per month to read a daily newspaper’s online content?
And 77% chose the answer “Nothing.” These poll results are absolutely useless – primarily because the question ignores innovation. Here are some things that these results do not say:
- They do not say that information wants to be free.
- They do not say that daily newspaper content has zero value, or value approaching zero.
- They do not say that newspaper readers (or people interested in news in general) are a bunch of freeloaders.
The only thing this polls tells us is that whoever set up the poll is incompetent, and probably shouldn’t be listened to on any further matters of importance. This is fundamentally the wrong question to ask.
If you had asked people in 1980 “How much, if anything, would you be willing to pay per month to watch a television channel’s online content?”, the answer would be “Nothing.” And yet, cable television did reasonably well.
If you had asked people in 1987 “How much, if anything, would you be willing to pay per month to have another phone in addition to the one that you already have? And also, it will have a different number.”, the answer would be “Nothing.” And yet, mobile phones did reasonably well.
Of course people say they’ll pay nothing for news online! What idiot would say that they would? They get it for free already, from a number of good sources. We’ve never really paid for news. Cable TV and mobile phones worked because they offered something fundamentally different from what people already had. Cable didn’t take off until people heard about 24 hour sports on ESPN, and 24 hour news on CNN, and 24 hour music on MTV. Prior to this, you had sports on the weekend, and news at 6 pm and 11 pm, and music, well, never. Cable changed the value proposition.
Mobile phones changed the value proposition too – they allowed you to use the phone anywhere. And eventually, they allowed you to send short text messages. Even still, for me at least, this had negative value until smart phones came along and gave me a portable computer with gps.
And the big problem is that you can’t ask customers what they would want with these things in advance, because we don’t know. You can just experiment. With phones, the telcos always thought that the market was for businesspeople. Initially it was. Once they introduced texting, all of sudden there was a huge market for teens, which drove further innovation.
News has to experiment now, and they need to specifically think about how they create value with Aggregating, Filtering and Connecting. I’ve talked before about many different possible approaches to this. We can develop new funding models, like Jeff Jarvis has. We can develop new value creation models, like Dan Gillmore advocates. We can consciously develop an aggregate, filter and connect model, like politico.com’s or Dan Conover’s.
The main point is that this situation requires business model innovation. To figure out how to do that successfully, we have to ask new questions. We need a news version of cable television. Or a news version of mobile phones. The entire model has to look different. Polls that ask if you’d pay for current content online are not just worthless, they’re harmful, because they prevent us from asking the questions that can lead us to genuine innovation.
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