Earlier this week, Jon Udell (view here) and Dare Obasanjo (view here) both contributed great articles covering the effect of networks. As I read these posts it got me thinking (once again) about the issue of differentiating data, information, and knowledge. I also realized during my musings that this would actually bring some clarity to technology areas that are oftenly completely misunderstood as a result of value proposition misconceptions or misunderstandings.

A quick head to blog dispatch of these thoughts (while they remain fresh):

Data is an expression of feedback; a statement (rightly or wrongly so) about an observation. If you think about it, didn't we used to capture observed data on paper in tabular form (row and columns which are analogous to Relational Database Tables and Columns)?

Information is data in context, or as I would prefer to say: contextualized data. Thus, information provides an understanding of data (provides insight about statements of observation). I also recall a myriad of context oriented hierarchical presentation forms: taxonomies and ontologies or conceptual schemas (nowadays expressed in an hierarchical tree form called XML and persisted for future reference in an XML aware database).

Knowledge isn't contextualized information, and it is certainly distinct from information (contrary to many dictionary definitions as highlighted in this post by Amy Gahran). I prefer to define knowledge as the basis of what you can, will, would, should, or might do with information. And all cases we express our levels knowledge by the way we act on the information (or lack there of) at our disposal. Think about brainstorming for a moment; you are trying to determine a path of action based on information at your disposal, a typical action would be to draw conceptual or topic relationship maps (graphing, with direction driven by the information processing action) on a whiteboard or piece of paper. Expressing, sharing, processing, and persisting these concepts and topics graphs are what the 'Graph Model' based semantic/knowledge database is all about.

Our industry has derived appropriate technology solution realms for Data, Information, and Knowledge Management (although we mix them up more often than not). Thus, there is room for Network, Hierarchical, SQL, XML (Semi-Structured Model), Object, Object-Relational, and Associative Model (graph based modeling of: source, verb, target; analogous to subject, predicate, object as per RDF).

We are spawning data, databases, infobases, knowledgebases, networks, and eventually agents, that will reflect the timeless relationships that exist across; data, information, and knowledge.