People-focused knowledge management

Examples of Knowledge Map Characteristics: Application of
Knowledge — how knowledge is used, sold/licensed, created, shared,
embedded in technology, etc.; Knowledge Representation — how
knowledge is encoded in rules, stories, natural language, embedded,
mental models, etc.; Nature of Knowledge — how knowledge exists
such as topic knowledge, principles, schemata/scripts/metaknowl-
edge, operational models, routines, prescriptive versus descriptive,
conceptual versus concrete, granularity — precise (rule-based, facts,
etc.) versus general, etc.; Source or Destination of Knowledge — how
knowledge flows to and from external or in-house experts, cus-
tomers, suppliers, practitioners, educational programs, customers,
etc.; Medium for Knowledge — how knowledge is conveyed such as
communicated person-to-person, assimilated on-the-job, embedded
in knowledge-based system, included in policies/procedures, written
in books and manuals, and documented in drawings.
Purpose: Identify the needs and availability of expertise (knowl-
edge) within an operating area. KM may be undertaken to establish
existing and expected requirements for knowledge to deliver the
desired operational effectiveness and service paradigm, assess the
present knowledge available to the unit, identify knowledge gaps, and
spot the availability of valuable knowledge that may be underuti-
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lized. Secondary objectives are to arrive at an agreed characterization
of the work to be performed (the service paradigm to be delivered)
and obtain shared understanding of the knowledge situation within
the unit.
Competitive Knowledge Analysis
Competitive knowledge analysis involves information gathering
about and identification of particular areas of expertise and impor-
tant IC assets that are part of competitors’ strengths and successes.
Competitive knowledge analysis is important for all organizations
that aim at having a strong market position.
Purpose: Identify levels of competitive expertise and IC assets for
reasons such as identifying where a competitor may be strong, requir-
ing the enterprise to build comparable knowledge, or if the com-
petitor has weak areas, that may point to opportunities that might
be pursued. Other approaches include obtaining knowledge from
competitors by examining patents and patent applications, perform-
ing reverse engineering, benchmarking, or exchanging knowledge in
scientific articles and professional meetings, and so on.
Knowledge Flowcharting and Analysis (KFA)
A major challenge in KFA is to identify and characterize all the
relevant sources of new knowledge. “Innovation is everywhere, the
problem is learning from it” (Brown 1991). It is both impractical and
undesirable to identify every innovation in an organization, particu-
larly since people at all levels are full of ideas and continually invent
new ways to improve their work. Many of these innovations are per-
sonal and may not apply to others. Nevertheless, valuable insights
and many opportunities for improving knowledge flows are provided
by identifying innovation sources and new knowledge and then chart-
ing paths from innovation to practical use. The goal is to identify
areas of important improvements, and that is possible in spite of the
problems that prevent us from being exhaustive.
Purpose: Find and describe opportunities for improving knowledge
flows around selected areas of the organization in order to do busi-
Examples of Knowledge Management Analysis Approaches
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ness better. As a result, KFA involves investigating, characterizing,
and describing how knowledge is used, held, built, and exchanged
by individuals and entities such as work groups, departments, and
the organization as a whole. Typically, KFA deals more with identi-
fying the paths, means, and utility of aggregate knowledge flows than
the flows of individual knowledge items, ideas, and innovations,
although some of those may be used as examples of more general
activities. KFA also deals with analyzing the strengths and weak-
nesses of existing knowledge flows and with identifying and synthe-
sizing potential improvements.
Knowledge Diagnostics
Root-cause diagnosis of knowledge-related issues may be the least
understood aspect of KM. Only advanced enterprises routinely pur-
sue in-depth analysis and conceptualization — knowledge diagnostics
— of target situations and use this expertise to develop candidates 
for intervention. Most enterprises still pursue conventional symptom-
oriented “industrial engineering” diagnosis and devise remedial solu-
tions accordingly. When such enterprises pursue KM, they may utilize
KM surveys and screenings methods such as knowledge mapping, or
they pursue KM based on what has been successful elsewhere without
deep understanding of the knowledge-related mechanisms in the
target situation.
Generally, knowledge-related problems or opportunities can only
be observed indirectly. By their nature, they are different from tradi-
tional operational, tactical, and strategic issues. Knowledge-related
situations involve how people think instead of what physically
happens. They deal with determining which action is chosen and the
reasons for why it is chosen, rather than just what happens. Enter-
prises often lack the ability to diagnose situations from knowledge
perspectives and instead limit investigation to physical or observable
characteristics, such as process flows, information flows and issues,
and resource availabilities. Considerable understanding of underly-
ing knowledge-related mechanisms and processes is needed to
analyze situations and to conceptualize KM interventions and
actions. It frequently is helpful to consider target situations in the
form of critical knowledge functions (CKFs) that, to be conducted
competently, require application of quality knowledge.
Effective KM diagnostics on the personal level requires under-
standing of how personal knowledge and other intellectual capital
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(IC) assets are applied to produce work deliverables competently and
competitively. On the organizational level, KM diagnostics requires
analysis of structural knowledge-related factors that affect operations
of business functions, the delivery and performance of products and
services, and so on. In general, KM diagnostics requires awareness
of representative knowledge-related issues by having familiarity with
symptoms and underlying processes such as:
Quality problems caused by assembly-line workers who make
minute mistakes when they misunderstand how tolerances affect
the field performance of products — when they do not under-
stand how to identify when parts have problems — or when they
do not know how to repair parts with minor problems and use
them anyway.
Low personal productivity and unnecessary delays caused by
insurance underwriters who are uncertain about how to proceed
in nonroutine cases when they only possess routine and opera-
tional knowledge while lacking broader script and schema
knowledge that would allow them to operationalize such knowl-
edge to apply to different cases and situations.
Wrong customer advice provided by service representatives who
misunderstand customer situations or lack sufficient knowledge
of the enterprise’s products, services, and systems and proce-
dures and therefore address customer situations improperly.
Inappropriate design solutions by engineers who misunderstand
product application requirements because of insufficient 
knowledge of how to apply technology in the target context.
Misdiagnosis and faulty repairs by office machine and instru-
ment technicians who make hasty conclusions based on assump-
tions derived from limited experience.