The Data, Information, Knowledge, Wisdom (DIKW) hierarchy is a uniquely relevant topic as social technologies take hold and challenge not only the relationships between data, information and knowledge within enterprise organizations, but also how information and knowledge are captured and transferred amongst your staff.
Picture this scenario: Your car broke down on the way to work and you need to bring it to a mechanic. You have a choice of three different garages to get your car back on the road and get to work:
- The first has a few monkeys with primitive tools and no experience fixing cars. We've probably all been to this garage at some point.
- The second has some junior mechanics and manuals. They seem competent, but they don't have any experience with your particular car.
- The third has mechanics, manuals, and a master technician who has a lot of experience with your car.
You get the gist of where I'm going with this. Unless you have a soft spot in your heart for monkeys, you'd choose garage #3.
The point of this exercise is to illustrate a topic that's been around for a while, but is taking on new meaning with the rise of social technologies in the knowledge management space. And that is the Data, Information, Knowledge, Wisdom (DIKW) hierarchy, as described by Jonathan Hey and many before him.
Data vs Information vs Knowledge
To paraphrase, data is unprocessed information. As in the case of the first garage, unless you have a lot of monkeys, the likelihood that they will stumble upon the problem and fix it is pretty slim.
Information is data that has been processed. Some thought has been applied, but it is still somewhat limited—like the manuals that the junior mechanics use. If the problem is well described in the manual, you are in luck. Otherwise, you'll probably end up with a bill, but no real solution.
Knowledge, and/or wisdom, is information that has proper context and has been reflected upon. (Note: There are mixed notions about the DIKW model. Not all versions include all four components, with some more recent versions omitting or downplaying wisdom. For our purposes here, we're using knowledge and wisdom interchangeably.)
Social Technologies and the DIKW Model
Consider your own organization and how it connects to the data/information/knowledge hierarchy. What kind of "garage" would you want your employees working in? More importantly, what garage is going to deliver the best possible repair to your customers?
Hey's version of the DIKW hierarchy goes so far as to describe knowledge or wisdom as "generally personal, subjective and inherently local -- it is found '[within] the heads of employees.'" I'm not sure I agree with this.
Certain aspects of knowledge in hands-on professions (e.g., mechanics and surgeons) cannot be captured by a knowledge repository, much less by social media. This is because the learning is very much “mind-body.” You must learn by doing.
However, both of these professions are also very much driven by other forms of knowledge that are disseminated in more traditional ways. For example, manuals, journals and/or continuing education courses for mechanics and doctors.
The point is that social media can speed the capture and dissemination of this type of knowledge. And therefore the lines between information and knowledge begin to blur.
While I readily acknowledge that the further you move up the hierarchy, the less structured information becomes and the harder it is to capture. I believe that some knowledge can be codified and captured, and that organizations need to think about how they can make this happen, and if social tools can play a role in making this happen.
Editor's Note: There is a lot more to learn about Enterprise Collaboration:
- The Effective Intranet – Engaging the Employee as Customer
- Three Things to Consider in Your Enterprise Collaboration Strategy
- Collaboration – If it Were That Easy We Would all Do It – Well