Data : All sorts of events being produced in an environment.
Information : An event resulting in an incident. Set procedure to deal with a specific kind of incident.
Knowledge : How to solve this problem (resulting from the incident, if it is a problem)
Wisdom : Based upon the kind of problem, should it be dealt with or left on its own, basically what to do with it. Wisdom will generally be seen as experiece and knowing the historical operational working of the service.
The DIKW model assumes the following chain of action:
Data comes in the form of raw observations and measurements (used as a basis for reasoning, discussion, or calculation).
Information is created by analyzing relationships and connections between the data. It is capable of answering simple "who/what/where/when/why" style questions. Information is a message, there is an (implied) audience and a purpose.
Knowledge is created by using the information for action. Knowledge answers the question "how". Knowledge is a local practice or relationship that works.
Wisdom is created through use of knowledge, through the communication of knowledge users, and through reflection. Wisdom deals with the future, as it takes implications and lagged effects into account
Data has commonly been seen as simple facts that can be structured to become information. Information, in turn, becomes knowledge when it is interpreted, put into context, or when meaning is added to it. The common idea is that data is something less than information, and information is less than knowledge. Moreover, it is assumed that we first need to have data before information can be created, and only when we have information, can knowledge emerge.
Data are assumed to be simple isolated facts. When such facts are put into a context and combined within a structure, information emerges. When information is given meaning by interpreting it, information becomes knowledge. At this point, facts exist within a mental structure that consciousness can process; for example, to predict future consequences, or to make inferences. As the human mind uses this knowledge to choose between alternatives, behavior becomes intelligent. Finally, when values and commitment guide intelligent behavior, behavior may be said to be based on wisdom.
According to these definitions, data is the basic unit of information, which in turn is the basic unit of knowledge, which itself is the basic unit of wisdom. So, there are four levels in the understanding and decision-making hierarchy. The whole purpose in collecting data, information, and knowledge is to be able to make wise decisions. However, if the data sources are flawed, then in most cases the resulting decisions will also be flawed.
The reason for a knowledge management system are to validate, to direct, to justify and to intervene or put simple "to make decisions".
DIKW model looks well defined. I am interested in knowing if we have actually seen Chief Knowledge Officer in Organization. As per the wiki ,the guy is expected to manage the 'intellectual capital of the organization' that would surely mean intellect and wisdom and not limited to the know-how to how to do. Do we know anyone in real life and how has the organization helped by draining millions on this C guy. By the way, I came across Chief Humor Officer going through Nigel Risner's IMPACT code. I guess its Dan Brown in some beverage company, for the fun @ work purpose. _________________ regards,
"the only statistics you can trust are those you falsified yourself"
You cannot post new topics in this forum You cannot reply to topics in this forum You cannot edit your posts in this forum You cannot delete your posts in this forum You cannot vote in polls in this forum