Model 6: data, information, knowledge, wisdom

The Liebowitz (1999) model of DIKW is helpful if you are looking for something with details to give you ideas on what you might do in your own work. I can see how this can be used to evaluate large scale programs. The value aspects are useful reminders of the underlying purpose in moving to each stage, and therefore gives an idea of what might need to be planned for in a large scale program to achieve each step.

Model 5: data, information, knowledge, wisdom

So let's start 2011 by continuing the data-information-knowledge-wisdom series of posts with something creative from Topicscape's Mindmap directory:
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I like the zone of potential - zone of possibility continuum as well as half the "effort" being below the surface which can only really be explained by doing a drawing like this.

Model 4: data, information, knowledge, wisdom

The Model from Infovis below shows just what I always struggle in explaining to tohers as to why the results of one project cannot just be pushed onto other, adopting, groups.

The producers, the pilot projects, create the basic data and some information (patterns) about it. The consumers (the adopters) need to retest this in their own environment.  Any pilot project that can be written up in a way that helps the consumer to bridge the gap from information to knowledge will likely be more successful at spread than others. This can include things like: you can adapt this in the following way, we did the following and it didn't work but it may work in xyz circumstances etc.

Model 3: data, information, knowledge, wisdom

One of the QI refrains is "increase the capability and capacity of employees". While this is a great concept, easy to declare and impossible not to support, for me it lacks any concrete applicability. What exactly is meant by this? There is another one of our data-information-knowledge-wisdom models which may help pin down what might be meant. Next time you hear somebody say the capacity/capability thing then whip this model out and ask them to explain their intentions and expectations along the data to wisdom curve.

 The challenge here is to produce learning experiences that enable someone to move up the curve. In my experience, much of healthcare improvement work is focused on developing data based skills - how to measure change. Some people get to the information stage where they learn to look for patterns, say by using SPC charts. Can they port this knowledge to other projects in a predictable way? Can they make intelligent choices? To what extent do the participants on a QI project become "wise"?

The above curve comes from Designing Knowledge Eco-Systems for Communities of Practice.  The web resources are excellent - especially if you are developing CoP's as part of your QI strategy.