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The Knowledge Management Cycle 35

An expertise location system may have, as knowledge objects, the different categories
of expertise that exist within that organization (e.g., fi nancial analysis) and these
attributes are used to search for, select, and retrieve specifi c knowledgeable individuals
within the company.
A well-designed repository will include schemes for labeling, indexing, linking, and
cross-referencing the information units that together comprise its content. Although
Meyer and Zack (1996) specifi cally address information products, their work is more
broadly applicable to knowledge products as well . Whereas knowledge does indeed
possess unique attributes not found in information (as discussed in chapter 1), this
does not necessitate adopting a tabula rasa approach and reinventing decades of tried,
tested, and true methods. This is especially true when managing explicit knowledge
(formal, codifi ed), which has the greatest similarity to information management. In
the case of tacit knowledge, new management approaches need to be used, but these
should, once. again, build on solid content management processes.
The repository becomes the foundation upon which a fi rm creates its family of
information and knowledge products. This means that the greater the scope, depth,
and complexity, the greater the fl exibility for deriving products and thus the greater
the potential variety within the product family. Such repositories often form the fi rst
kernel of an organizational memory or corporate memory for the company. A sample
repository for a railway administration organization is shown in fi gure 2.1 .
Meyer and Zack analyzed the major developmental stages of a knowledge repository
and these stages were mapped on to a KM cycle consisting of acquisition, refi nement,
storage/retrieval, distribution, and presentation/use. Meyer and Zack refer to this as
the “ refi nery. ” Figures 2.2 and 2.3 summarize the major stages in the Meyer and Zack
cycle.
Acquisition of data or information addresses the issues regarding sources of raw
materials such as scope, breadth, depth, credibility, accuracy, timeliness, relevance,
cost, control, exclusivity, and so on. The guiding principle is the well-known adage
of “ garbage in garbage out, ” that is, source data must be of the highest quality, oth-
erwise the intellectual products produced downstream will be inferior.
Refi nement is the primary source of added value. This refi nement may be physical
(e.g., migrating form one medium to another) or logical (restructuring, relabeling,
indexing, and integrating). Refi ning also refers to cleaning up (e.g., sanitizing content
so as to ensure complete anonymity of sources and key players involved) or standard-
izing (e.g., conforming to templates of best practice or lessons learned as used within
that particular organization). Statistical analyses can be performed on content at this
stage to conduct a meta-analysis (e.g., a high-level summary of key themes, or patterns