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 asa result ofvalue 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: taxonomiesand
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 thispostby
Amy Gahran). I prefer to
define knowledge as the basis of what you can, will,would,
should, or mightdo 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 graphsare what the 'Graph Model' based
semantic/knowledge databaseis 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, andAssociative Model (graph based modeling
of: source, verb, target; analogous to subject, predicate, object
as per RDF).
We are spawningdata, databases, infobases, knowledgebases,
networks, and eventually agents, that will reflect the timeless
relationships that exist across; data, information, and
knowledge.