by ThunderChild tm
Seven rules for the KM-lords in their farm of cubes:
[Via Knowledge Jolt with Jack]

David Snowden has expanded his three rules to seven principles. Now I have to wonder if there are nine rules somewhere. And if there is One Rule to Bind them All. Rendering Knowledge (rules excerpted)

• Knowledge can only be volunteered it cannot be conscripted. [original]
• We only know what we know when we need to know it. [original]
• In the context of real need few people will withhold their knowledge.
• Everything is fragmented.
• Tolerated failure imprints learning better than success.
• The way we know things is not the way we report we know things.
• We always know more than we can say, and we will always say more than we can write down. [original]

The four new elements sound familiar from David’s other writing. Taking time to think about these principles and the additional context David gives them, they begin to sound like common sense. Of course people learn from failures. Of course we build things from fragments of other things. But then why do we forget this common sense when building approaches to knowledge management? Maybe not so common?

Yes, these are common sense but so often not observed. Many organizations do not tolerate failure, making their lack of innovation obvious.

When I was in Junior High School, we played a game called bulls and cows. One person tried to guess a 4 digit number the other person had written down. If the guess has a number in the right position, it counts as a bull. If the guess has the right number in the wrong spot, it is a cow. So the correct answer results in 4 bulls.

Now there are about 4500 possible numbers (assume no repeated numbers and you can’t have a zero in the first position) so having some sort of system helps. Like start with ’1234′. But the absolute best answer is ‘no bulls- no cows.’ Complete failure to guess the number.

This results in the removal of 40% of the possibilities in a single guess. No other choice is as helpful in narrowing down the possibilities. Failing actually gets you to the answer sooner than an initial success of 1 cow.

This game taught me that failure can be much more helpful than a slight success. We see that so much today. Failing does not usually cost too much and can get the group to success much more rapidly by reducing the degrees of freedom one has to work with. It is generally corporate culture that hampers this path.

Those organizations that can tolerate failure will learn faster and innovate at a much more rapid pace. Not necessarily because they are smarter. They are just informed by their failures, narrowing down the possibilities that eventually result in success.

Technorati Tags: Knowledge Creation, Social media, Web 2.0

by Dyl86
Failure as an event:
[Via Seth's Blog]

I try hard not to keep a running tally of big-time failures in my head. It gets in the way of creating the next thing. On the other hand, when you see failure as a learning event, not a destination, it makes you smarter, faster.

Some big ones from my past:

The Boston Bar Exam. My two partners and I spent a lot of time and money building this our last year of college. It was a coupon book filled with free drinks from various bars in Cambridge and Boston. The booklet would be sold at the bars, encouraging, I dunno, drunk driving. Lessons: Don’t spend a lot on startup costs, don’t sell to bar owners and don’t have three equal partners, since once person always feels outvoted.

The Internet White Pages. This was a 700 page book filled with nearly a million email addresses. It took months to create and IDG, the publisher, printed 80,000 copies. They shredded 79,000 of them. Lesson: If the Internet Yellow Pages is a huge hit (it was), that doesn’t mean the obvious counterpart will be. A directory that’s incomplete is almost always worthless.

MaxFax. This was the first fax board for the Mac. It would allow any Mac user to hit ‘print’ and send what was on the screen to any fax machine. We raised seed money from a wealthy dentist, built a working prototype and worked to license it to a big computer hardware company. Lessons: Don’t raise money from amateurs, watch out for flaky engineering if you’re selling a prototype, think twice before you enter a market with one huge player (Apple knocked off the idea) and don’t build a business hoping to sell out unless you have a clear path to do that.

One of the important lessons is to fail as soon as possible and learn from it. Then move on. Today, the most rapid path to wisdom and success is to crank the innovation cycle as fast as possible. Here are a couple of other lessons from Seth:

Prepare for the dip. Starting a business is far easier than making it successful. You need to see a path and have the resources to get through it.

Cliff businesses are glamorous but dangerous.

Projects exist in an eco-system. Who are the other players? How do you fit in?

Being the dumbest partner in a room of smart people is exactly where you want to be.

And the biggest of all: persist. Do the next one.

There are lots of things failing around us everyday. Moving beyond that to success requires persistence and a vision. And a high threshold for dealing with failure.

Besides, maybe one of your failures will be epic.

Technorati Tags: Web 2.0

I’ve put together a nice handout regarding the seminar – Transformed! Information, Bioscience and Web 2.0. Take a look:

You can also download a copy.

Technorati Tags: Social media, Web 2.0

by hendriko
All the details have been finalized for a three hour seminar SpreadingScience is sponsoring entitled

Transformed! Information, Bioscience and Web 2.0

October 7, 2008 6-9 PM
Lake Washington Rowing Club
910 N. Northlake Way
Seattle WA

The seminar will be given by Richard Gayle, Ph.D. and Mark Minie, Ph.D. It is geared for a general audience that includes researchers, lawyers, clinicians and anyone else interested in using modern technology to solve today’s problems. It will have three segments:

1. The Transformation of Information into Knowledge
   Knowledge is the ability to make a decision, to perform an action. The knowledge creation cycle begins with data. Human social interactions transform data into knowledge. Social networks evolved to provide primates with diverse solutions to complex problems. However, there appear to be hardwired barriers to the size of these social networks, limiting the scope and complexities of the problems that can be solved. The huge amount of information being generated overwhelms these barriers. The difficult problems facing us today are too complex to be solved only the tools we evolved. We must use new digital tools to amplify our inherent abilities.
2. The Transformation of Bioscience by Information
   Biology is now a branch of Information Science, and important new research, discovery and invention is taking place on the World Wide Web. From computer gaming/education to personal genomics, biological engineering and robotics, bioscience is undergoing a true renaissance with previously unexpected impact and dividends. This segment will explore bioscience’s new life on the Internet. It will focus on specific examples and new tools with potential practical uses for both scientists and non-scientists alike.
3. The Web 2.0 Transformation
   Web 2.0 is about online conversations. These tools often remove the need for people to occupy the same space at the same time in order to transform information into knowledge. They permit the examination and understanding of human social networks many times larger than our hardwired limits. This enhances the ability to create knowledge and to increase the rate of diffusion of information in an organization. Communities that can use Web 2.0 tools to leverage human social networks will solve complex problems more rapidly than those that do not.

There is a glut of data in the world today. Our normal processes to deal with this glut – the interactions in a human social network – are overwhelmed. However, the same technologies that are permitting such huge amount of data to be created can also help us enhance our social network interactions, providing organizations with the possibility of solving much more complex problems than before.

Please join us on October 7 as we provide a foundation for understanding how Bioscience is being transformed by information and how we can use novel tools to leverage this transformation into critical solutions .

Until September 23, the cost is $175. After that date it rises to $225. So register early!

Technorati Tags: Science, Web 2.0

by pheezy
The Pixar Principles. The Art of Collective Creativity:
[Via Creativity Central]

The Previews:

When I freelanced for Disney, they still required creatives to punch a time clock. Women with tight-fitting hair nets roamed the halls with coffee and doughnuts. And the circular dining hall was festooned with pictures of Walt and Roy and executives like Card Walker.

Chances are somewhere in that group of diners was John Lasseter. John was an animator who left Disney to become part of the computer division of Lucasfilm. Steve Jobs bought the fledging company and renamed it Pixar, a fake Spanish word meaning “to make pictures or pixels.”

Jobs, Lasseter and Dr. Ed Catmull overcame a roller-coaster of financial challenges and turned Pixar into a dream company. Ed Catmull isn’t a name most people don’t know outside of the animation world. At Pixar, he not only co-founded the company, he was the key developer of the RenderMan rendering system used in such films as Toy Story and Finding Nemo.

Recently, Catmull wrote a terrific article for the Harvard Business Review called “How Pixar Fosters Collective Creativity.” His insights into developing a culture of collaboration and sustaining that culture are an important lesson for other creative organizations.

The Harvard Business Review article has the audio if you want to hear the whole thing.I wrote previously about Pixar in three posts entitled The Synthetic Organization part 1, part 2 and part 3. They discuss my view that Pixar may be a model for a new type of company, one based on many of the principles of Web 2.0 – openness, transparency, rapid diffusion of innovations.

This audio from the Ed Catmull is very useful. He wanted to create a creativity inspired company that is self-sustaining, that no longer needs the vision of a few people at the top to maintain innovation. Marty Baker at Creativity Central breaks some of this down. He presents the key insights:

Pixar’s Operating Principles can be distilled down to 3 principles.

1. Everyone must have the freedom to communicate with anyone.

2. It must be safe for everyone to offer ideas.

3. We must stay close to innovations happening in the academic community.

In addition, many decisions at Pixar take place in a social setting, with a level playing field. That is, there is no organizational chart when it comes to examining problems, the goal is to fix the problem not to assign blame.

Web 2.0 approaches work well in this sort of setting since it is hard to dominate a conversation simply because you are a VP. Everyone’s voice, their criticism, their suggestions, has a more equal standing than in a normal conference room. The lack of many of the non-verbal communications of status makes it easier for the goal of creativity to reached.

Technorati Tags: Web 2.0

by Marcin Wichary
Electronic notebooks are cool, and so is RDF:
[Via business|bytes|genes|molecules]

Had a conversation earlier today, all about RDF and linked data. I am a big believer, which is why posts like this one by Cameron Neylon on A new way of looking at science? bring a smile.

Andrew Milsted, a PhD student, enabled an RDF dump of the content in the lab notebook used by Cameron’s group (and others I suspect). The result, a graph that shows each post in the notebook as a node and links between posts as edges. It is a universe of the work going on in the lab, and how that work interacts. It would be interesting to see the dynamics of this graph evolve, and various other ways of visualizing the underlying data and relationships. It would also be cool to put this up on the web as linked data and link it to data outside Cameron’s lab. Might even lead to some very interesting observations and relationships.

This is a simple example, but highlights why it is so important to be able to put data into machine readable formats. RDF is a naturally good model, since it highlights relationships within the underlying data.

Technorati Tags: Bioinformatics, Science, Social media, Web 2.0

Kansas, We Owe You One (Updated Election Video):
[Via Common Craft - Explanations In Plain English -]

I’m not sure how this happened, but there is an error in the original version of the “Electing a US President” video. The original version says that there are 3 congressional districts in Kansas. As we discovered today, via a nice email from Gerry Deman of Kansas, there are actually 4 districts.

Here’s what we’re doing about it:

We have created a new, corrected version of the video. It’s embedded below and we have replaced the video on the original blog entry (and embed code) with this new version. We’ve also replaced the downloadable versions in the Store and other places where it is shared.

Unfortunately, this means that two versions will exist on YouTube, because it’s impossible to replace a video. By deleting the original version, we break the connections to the You Tube players on blogs that embedded it. If you embedded the original version, please do replace the video with this new version.

It’s a good thing that folks like you keep us in check so we can limit the potential confusion. We’ll count better next time, I promise.

So the video put up yesterday had an error – The wrong number of congressional districts in Kansas. A trivial fact in the scheme of the presentation but one noticed by someone in the community.

The Four ‘Ayes’ of the Iterative process:

• investigate
• identify
• improve
• implement

The more rapid each turn of the iterative process is, the faster perfection is approached.

Technorati Tags: Social media, Web 2.0

by txd
What Social Media Does Best:
[Via chrisbrogan.com]
Before Chris starts his list he has this to say:

If you’re still looking for the best ways to explain to senior management or your team or your coworkers or your spouse what it is that social media does, why it’s different than the old way people used to use computers and the web, why people are giving two hoots about it, here are some thoughts to start out the conversation. I look at this mostly from a business perspective, but I suspect you’ll find these apply to nonprofits and other organizations as well. Further, as I’m fond of saying, social media isn’t relegated to the marketing and PR teams. It’s a bunch of tools that can be used throughout businesses, in different forms. Think on this.

I’m not going to list all of Chris’ points but here are a few to whet your appetite.

Blogs allow chronological organization of thoughts, status, ideas. This means more permanence than emails.

The organizational aspects of blogs are one of their most overlooked features.

Social networks encourage collaboration, can replace intranets and corporate directories, and can promote non-email conversation channels.

Email is not optimized for the sorts information transfer that it is used for. It also makes it impossible to really know just who should see the information. Social networks open this up and make it highly likely that the right information to get to the right people.

Social networks can amass like-minded people around shared interests with little external force, no organizational center, and a group sense of what is important and what comes next.

Ad hoc group creation is one of the best aspects of social networks. Rapid dispersal of information amongst a small, focussed group can occur independent of the need for everyone occupy similar space at the same time, as is done in meetings.

Blogs and wikis encourage conversations, sharing, creation.

Facilitating conversations increases information flow, speeding up the creativity cycle

Social networks are full of prospecting and lead generation information for sales and marketing.

This applies to a much wider group than just sales and marketing because at some level, everyone at an innovative organization needs to look for leads.

Blogs allow you to speak your mind, and let the rest of the world know your thought processes and mindsets.

The personal nature of many social media tools helps enhance the ability of a group to innovate rapidly, without the feeling of a restricting hierarchy that can diminish creativity.

Tagging and sharing and all the other activities common on the social Web mean that information gets passed around much faster.

Web 2.0 approaches make it much easier to find information, even though there is more of it.

Innovation works much faster in a social software environment, open source or otherwise.

The diffusion of innovation throughout an organization is really dependent on the social network of that group, how well connected it is, how people communicate, etc. Social media allows innovation to spread much more rapidly, decreasing the rate of diffusion and allowing the creativity cycle to crank much faster.

People feel heard.

This is a big one. Studies have shown that if people feel that their viewpoint is not heard and do not understand the rationale for a decision they become the most upset. Having a chance to be a part of the discussion can make a big difference, even if they do not agree with the final decision.

Technorati Tags: Social media, Web 2.0

by …†∆†¡∆µ∆ 
Building scientific communities:
[Via business|bytes|genes|molecules]
Here is an interesting point that should be discussed more, especially with scientific community building (my bolding).

I will start with something I have quoted all too often

Data finds data, then people find people

That quote by Jon Udell, channeling Jeff Jonas is one that, to me at least, defines what the modern web is all about. Too many people tend to put the people first, but in the end without common data to commune around, there can be no communities.

A community needs a purpose to exist, a reason to come together. Some communities arise because of similar political or gardening interests. Most research communities come together for one major reason – to deal with data.

However, standard approaches are too slow and cumbersome. When one group can add 45 billion bases of DNA sequence to the databases a week, the solution cycle has to be shortened.

Science is an intellectual pursuit, whether it is formal academic science or just casual common interest. That’s where all the tools available today come into the picture. The data has always been there. Whether at the backend, or at the front end, we can think about how to get everything together, but being able to discovery and find some utility is very important. One of the reasons the informatics community seems to thrive online, apart from inherent curiosity and interest in such matters, is that we have a general set of interests to talk about, from programming languages, to tools to methods, to just whining about the fact that we spend too much time data munging. Successful life science communities need that common ground. In a blog post, Egon talks about JMOL and CDK. Why would I participate in the CDK community, or the JMOL one? Cause I have some interest in using or modifying JMOL, or finding out more about the CDK toolkit and perhaps using it. Successful communities are the ones that can take this mutual interest around the data and bring together the people.

Part of what is being discussed here is a common language and interest that allows rapid interactions amongst a group. In some ways, this is not different than a bunch of people coalescing around a cult TV show and forming a community. A difference is that the latter is a way to transform information that has purely entertainment value.

The size and depth of these ad hoc communities, as well as their longevity, will depend on the size of the beach, just how much data must be examined. But I guarantee that there will always be more data to examine, even after publication.

So my advice to anyone building a scientific community (the one that jumped out at me during the workshop was the EcoliHub) is to think about what the underlying data that could bring together people is first. Data here is used in a general sense. Not just scientific raw data, but information and interests as well. Then trying and figure out what the goals are that will make these people come together around the data and then figure out what the best mechanism for that might be. Don’t put the cart before the horse. In most such cases, you need a critical mass to make a community successful, to truly benefit from the wealth of networks. In science that’s often hard, so any misstep in step 1, will usually end up in a community that has little or no traction.

EcoliHub is a great example of a website in the wild that is supported almost entirely in an Open Source fashion. This is a nice way to create a very strong community focussed on a single, rich topic. On the wide open Internet, though, it may be harder for smaller communities to come into existence, simply because of how hard it might be for the individual members of the community to find one another.

A grant deadline for example.

Technorati Tags: Social media, Web 2.0

Life Sciences likes this: Friendfeed:
[Via OpenWetWare]

I’m going to assume that only those currently using FriendFeed will understand the self reference in the title but if you didn’t that’s OK. Just keep on reading, you’ll get it, eventually.

If you happen to be interested or work in the life sciences area I’d recommend you take a few minutes to read Cameron Neylon‘s great post about FriendFeed and how it’s been embraced by the life sciences community.

I won’t go into the details of how FriendFeed works, but it’s been rapidly gaining momentum as a medium for groups of users to network and discuss each other’s shared content.

FriendFeed’s about page states:

FriendFeed enables you to keep up-to-date on the web pages, photos, videos and music that your friends and family are sharing. It offers a unique way to discover and discuss information among friends

The life sciences community has picked up on this great website like wildfire. A recently created room called The Life Scientists grew in a very short period (a week?) from just a few active online colleagues to a whooping 100+ users.

FriendFeed rooms offer a way to share on-topic content and further discussion via comments. Commenting can be done on any shared items (yours or others). This has proven to be useful for rapid input and idea sharing amongst the room’s users.

Amongst the 100+ users of the Life Scientists room, both Cameron from Science in the Open and Pedro from Public Rambling have found FriendFeed to be useful and explain why it works. Both great reads.

This is the sort of tool that can very rapidly connect researchers, in ways that Twitter or Facebook do not. Not only can links be put up rapidly but comments are there very fast. It allows one to ask questions, post answers. It is a lot like how the Bionet newsgroup, which you can still access, used to be back in the old days (i.e. 1993-95) when Usenet ruled the Internet.

This is the online equivalent of the water cooler where you can run into someone and strike up a conversation that could lead to innovative thinking. Only instead of two people having to occupy the same space at the same time, this approach decouples both, permitting a much wider circle of people to be involved.

Technorati Tags: Knowledge Creation, Social media, Web 2.0