I wanted to post this video for two reasons: 1) They used the nearly the same headline as I did (and who knows how many others have ;o); and 2) while my post is a dry review of the ways to generate revenue on the Web, Charlene Li and Sarah Lacy have a brief but interesting discussion about why monetizing social networks is different from search and other general Web advertising (the favored monetization model on the Web). I especially liked Charlene’s comment that Twitter may be amassing a more valuable data set than Facebook because they capture what “people are paying attention to” right now. Take five minutes to check it out:
Posts Tagged social networking
This post is based on the keynote I delivered at Knowledge Management Australia this summer (I know – but better late than never). I entitled the talk “Abandon Your Content Management: KM in the Age of GooTube”. When I developed it I was under the questionable influence of two books: Clay Sirky’s Here Comes Everybody and David Weinberger’s Everything is Miscellaneous. But here I want to share the main premise of the talk: that we should focus less on managing our information and focus more on capturing it and then making it discoverable.
(A note before we begin. I will be using the terms “information management” and “content management” in place of what many people would refer to as “knowledge management.” I define knowledge as “information in action” – and that action can only take place in the human mind. Since I’m not fond of the idea of mind management, I believe “information” is actually what we are managing, not knowledge.)
Most traditional information management or content management systems and programs follow a highly centralized model:
Think about those three verbs: Gather, Organize, Publish. Those are the verbs of centralization and governance. It implies one system (or group) is responsible for information management. And often the majority of the resources within that system are devoted to “Organize” – organizing (and controlling) the information in the system. In an age when search makes unorganized information easily discoverable, this is probably a waste of resources.
The focus on organizing grew out of natural human reaction to trying to understand an increasingly complex environment. There was so much information available that we had to develop ways of organizing it in order to cope. Over time, this resulted in what David Weinberger refers to as the “three orders of order”:
- Organizing the objects themselves based on shared traits. This does have some basis in logic and is exemplified by placing flora and fauna into related Kingdom, Phylum, Class, etc. or in organizing a department store into clothing items, kitchen items, electronic items, etc. But even this has its limitations. Does an under-kitchen-counter TV go in the kitchen department or the electronic department? This order of order is based on organizing the physical objects themselves.
- Organizing “pointers” that represent the actual objects based on some arbitrary system. This order of order evolved to address the sheer volume of objects that needed to be discoverable. We could create new smaller objects that “point” to the real object and then organize those “meta-objects”. The arbitrary way these meta-objects were organized (think alphabetization or the Dewey Decimal system) often removed any “natural relations” they might have. And again, their use and discoverability were limited by the fact that they were still physical objects.
- Digitizing the objects (or meta-objects) allows us to return to the “natural state of chaos”. This new order of order reconsiders the reason we organized objects in light of our new digital world. The core driver of our past organization was to make objects easily (and hopefully logically) discoverable. But in the digitized world, we can discover without the need for organization. Search is the key that unlocks the chaos of information. So, Weinberger’s (arguable) proposal is this: In a digital world power by full search, we no longer need to order (organize) our information to be able to find and use it.
If Weinberger is correct and we can return to chaos comfortably, it brings us to a more natural state of knowledge capture and discovery. To illustrate this, let’s first consider a (grossly simplified) picture of an ecosystem:
Within ecosystems, resources (food, energy) are circulated within the environment from producers to consumers and then (again, grossly simplified) back around to producers again. If we apply this ecosystems model to our old information management model, we will see “Organize” drop out entirely, “Gather” become “Capture” and “Publish” become “Discover.”
Think about these new verbs, Capture and Discover. These are not centrally controlled and they abhor governance. Given an open system, anyone can capture information as they create it (or discover it) and then everyone can discover all that has been captured (via search – as well as links, recommendations, etc.). And if the ecosystem (i.e., information management system) is designed properly, every act of discovery is automatically an act of capture that returns value to the ecosystem. Let’s consider the ideal application of the two verbs in more detail:
Capture. All the content (information) in our knowledge ecosystem is generated by people (people who need people – sorry…). We should design our work applications and procedures to capture everything that people produce as they work. There should be no separation between the tools of production and the tools of information capture. And, of course, those tools should have discovery built into them. Imagine if every time information of value to the ecosystem was generated – whether in a spreadsheet, database, e-mail, conference call, IM or Tweet – it was immediately captured, indexed and discoverable through search, cross-linking, and extensions. People working in that that ecosystem would thrive.
Discover. First and foremost, our information ecosystem must have comprehensive search. In addition, it should incorporate every tool or process for improving discoverability such as tagging, syndication, linking, the “database of intentions“, and recommendations. Moreover the system must recognize that the information is being captured and discovered by people (people who need people – damn! sorry…). As we move from the information age into the connected age and the importance of social networks increases, the system must support the socialization of information. Our ideas and information are satellites orbiting us just as the people in our social graph do. The ecosystem must recognize that information and the people who created or discovered it should be inseparable. We gain far greater value from social information than orphan information.
So how does one go about building a knowledge ecosystem? What are the basic requirements of a system to support the continuous cycle of capture and discover? That’s what the buzzword d’jour, “Enterprise 2.0” (aka “Knowledge Management 1.53”) is all about. By applying the social ideals and platforms sweeping the Web to the enterprise, we can approach (carefully) a knowledge ecosystem. One of the best (though techno-centric) models to capture the elements needed within a knowledge ecosystem is the FLATNESSES checklist created by Dion Hinchcliffe (based on the original SLATES checklist created by Andrew McAfee):
I encourage you to review it and the other “Enterprise 2.0” information out there. Applying those ideas can help you begin to shift from knowledge management to knowledge ecosystem.