READIT - 2009 - Indira Gandhi Centre for Atomic Research

new, and its combination of new ideas with ideas that every one has known all along,
should reassure practitioners rather than unnerve them(Prusak,2001). We have already
seen that the ideas and practices of knowledge management are as old as human
civilization.
Individuals, groups and organizations were always in search of ways and means
to improve the productivity. Data, Information and Knowledge Management in the
context of organizations can be traced to various concepts and practices in pursuit of
productivity improvement and organizational effectiveness, efficiency and excellence.
These concepts can be traced to F.W.Taylor’s Scientific Management Principles, and the
resultant ‘time and motion study’ and other industrial engineering practices
The introduction of data and information management systems in the
organizations was another attempt to improve the productivity. Some of the concepts of
KM, which we have discussed earlier like ‘collective’, ‘sharing’, ‘preservation’, ‘make
available’, ‘ improve performance’ etc are equally applicable to data and information
management. The only difference is that in the context KM we talked about ‘knowledge’
and in the context of data and information management, we talk about ‘Data’ and
‘Information’.
According to Ackoff (1989), data is raw. It simply exists and has no significance
beyond its existence. It can exist in any form, usable or not. It does not have meaning by
itself. Information is data that has been given meaning by way of relational connection. In
other words information is processed data. This "meaning" that information gives may or
may not be useful. Knowledge is the appropriate collection of information, such that it's
intent is to be useful. According to this, data is a pre-requisite for information and
information is a pre-requisite for knowledge. Hence information includes data. Also
knowledge includes information and data. In other words, data and information are
subsets of knowledge. According to Davenport and Prusak (1998), when experience and
insight are added to information, it becomes knowledge. Knowledge is broader, deeper
and richer than data or information. Knowledge is a fluid mix of framed experience,
values, contextual information, and expert insight that provides a framework for
evaluating and incorporating new experiences and information. Knowledge originates
and is applied in the minds of people. It is embedded in people, tasks, routines and
networks.
We can see that data and information are relatively easier to manage, because they
are structured. Managing knowledge is more complex because it is highly unstructured
and mostly exists in tacit form.
The term “Knowledge Management” was first introduced in 1986 by Wiig in a
keynote address “Management of Knowledge :Perspectives of a new opportunity” at
European Management Conference; “ KM is to understand , focus on and manage
systematic, explicit, and deliberate knowledge building, renewal, and application- that is
manage effective knowledge processes(EKP)”, (Wiig,1997). The key concepts that
differentiate the KM as visualized by Wiig from that of earlier times are ‘systematic,
explicit, and deliberate’. Also the technology, particularly Information and
Communication Technology(ICT) and Knowledge Engineering(KE) that is used to
achieve the effectiveness in KM, is and it will continue to be an important differentiator
with respect to time. The KM1.0 activities can be portrayed through the following subsystems: