Most managers, when asked, will say that their most important
asset is their people. Of course, they’re not talking
about flesh and bones... they’re talking about minds.
use the term "knowledge" very loosely with dozens
of definitions for it, many of which make rather vague distinctions
among data, information, knowledge, and intelligence.
I like to cite A.C. Foskett’s distinction between
knowledge and information:
is what I know, Information is what we know."
getting into more detail, I like to establish a common vocabulary
and share my definition of related terms, which may vary
from other publications.
concept of Intelligence is built upon four fundamental principles:
Data, Information, Knowledge,
and Wisdom or Intelligence.
The basic compound for Intelligence is data -- measures
and representations of the world around us, presented as external
signals and picked up by various sensory instruments and organs.
Simplified: raw facts and numbers.
is produced by assigning meaning to data relevant to mental
objects. Simplified: data in context.
is the subjective interpretation of Information and approach
to act upon in the mind of perceiver.
As such, knowledge is hard to conceive as an absolute definition
in human terms.
or wisdom embodies awareness, insight, moral judgments,
and principles to construct new knowledge and improve upon
Bank example would illuminate the definitions:
- Data: The numbers 100 or 5, out
- Information: Principal amount
of money: $100, Interest rate: 5%
- Knowledge: At the end of Year
I get $105 back
- Intelligence: Concept of growth
are living in an era of ever-increasing pace of technological
Beside increasing the performance and reducing the size,
we are left with only one possible progress path . . . a
move toward more “knowledgeable machines” (I
would rather use the term “knowledgeable” instead
of “intelligent” for the next few years!).
more knowledge available to machines, the more automation
can be achieved and the closer we get to true intelligent
is more valuable than putting information to effective use
in an automated manner and that can only be achieved by
deploying more structural knowledge into our technical life.
such the most important area in computer industry in the
coming years would be the means to deal with knowledge as
matter, referred to as "Knowledge Modeling".
Modeling is the concept of representing information and the
logic of putting it to use in a digitally reusable format
for purpose of capturing, sharing and processing knowledge
to simulate intelligence. Among others it can address business
matters such as Agility, Compliance, Consistent Decisioning,
Reasoning and Knowledge retention (baby boomers retirement,
development going offshore,...).
Although unbelievable today, I can clearly envision a day
when we are able to perform a full-blown modeling of a person’s
entire knowledge base. That operation would be referred to
as “SoulByting”, which is outside the scope of
this website. Now back on earth ...
Following diagram (courtesy of VentureChoice.com)
illustrates a sample knowledge model for venture capital decision
The most common applications of knowledge modeling are used
for education, decision support, alerting and automation.
In marketing and consumer research space knowledge modeling
is utilized to model the decision process of an individual
or segment in a particular context (referred to as Choice
There are already established techniques and methods to facilitate
Knowledge Modeling activities, such as data mining for knowledge
discovery, collaboration tools and document management systems
for Knowledge sharing and rule engines, BPMS and Expert Systems
to capture, process, simulate or react the knowledge inquiries.
In general, knowledge can be categorized into two distinguishable
knowledge - Can be articulated into formal language,
including grammatical statements (words and numbers), mathematical
expressions, specifications, manuals, etc. Explicit knowledge
can be readily transmitted to others.
This type of knowledge can be easily "modeled"
using various computer languages, decision trees and rule
knowledge - Personal knowledge embedded in individual
experience and involves intangible factors, such as personal
beliefs, perspective, and the value system. Tacit knowledge
is hard (but not impossible) to articulate with formal language.
network offers the best possible method for modeling tacit
In the simple form, a Knowledge Model would be designed
with the purpose of receiving data produced from various
sources and generate outputs that could trigger actions.
models can be implemented as software applications, hardware
components, object library, web-services, and many other
forms and shapes using various techniques. The closest natural
reassembled technique to human brain for modeling knowledge
is Neural Network.
a bridge between academic and business world, MAKHFI.COM
intends to help people understand and apply this unimaginably
powerful science to their everyday projects.