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A Knowledge Value Chain for Knowledge Management
Jean-Louis Ermine
Journal of Knowledge & Communication Management,
Volume 3 Number 2 pp. 85-101, October 2013
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A Knowledge Value Chain for Knowledge Management
, Jean-Louis Ermine
Telecom Business School, Department of Social Science, 9 Rue Charles Fourier, 91011 Evry, France
ABSTRACT :
The usual value chain is a powerful tool to identify strategic actions to ensure competitive advantages of the firms. A value chain is a chain
of production activities in a firm, starting from the inputs up to the final customer delivery. Products or services pass through all activities
of the chain in order and, at each activity, the product or service gains some value. In a knowledge-based economy, the main strategic
resource of a company is its knowledge capital. Knowledge management (KM) is then critical to bring competitive advantages. There is a
strong need to define what is the value chain for corporate knowledge, in order to manage the knowledge transformation process in the
organisation, up to the best knowledge performance. This article proposes a knowledge value chain (KVC), based on the famous but fuzzy
DIKW hierarchy (Data, Information, Knowledge and Wisdom), as a chain of fundamental intellectual tasks (cognitive activities). The
added value of each task is explained and discussed. It defines the different concepts, based on the current literature, with some definitions
adapted for the KM point of view. Then, the KVC is integrated in a management perspective. Finally, the KVC is interpreted as a
continuum of knowledge processes adding new value at each step.
Keywords: Knowledge Management; Performance management; Value creation; Knowledge assets;
Where is the life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in
information?
T.S. Eliot, ‘Choruses from the Rock’
Information is not knowledge,
Knowledge is not wisdom,
Wisdom is not truth,
Truth is not beauty,
Beauty is not love,
Love is not music,
And music is the best.
Frank Zappa, ‘Packard Goose’
INTRODUCTION
The value chain is a concept from business management that was coined and popularised by
Porter (1985). A value chain is a chain of production activities in a firm, starting from the inputs
up to the final customer delivery. Products or services pass through all activities of the chain in
order and, at each activity; the product or service gains some value. A value chain is a
decomposition of the activity of the firm into value-processing activities. These processing
components and activities are the building blocks by which a corporation creates a product or
provides service valuable to its customers. The chain of activities gives the products or services
more added value than the sum of added values of all activities.
Capturing the value generated along the chain is now the approach taken by top management to
ensure competitiveness. Differences among competitor value chains are a key source of
competitive advantage. In competitive terms, value is the amount customers are willing to pay
for what a firm provides them. Value is measured by total revenue, a reflection of the price a
firm’s product commands and the units it can sell. A firm is profitable if the value it commands
exceeds the costs involved in creating the product. Creating value for customers that exceeds the
cost of doing so is the goal of any competitive strategy. Value, instead of cost, must be used in
analysing competitive position. The value chain categorises the generic value-adding activities of
a firm: the ‘primary activities’ include inbound logistics, operations (production), outbound
logistics, marketing and sales, and services, the ‘support activities’ include administrative
infrastructure management, human resource management, R&D and procurement. The costs and
value drivers are identified for each value activity.
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It is now recognised that we have entered a ‘Knowledge-Based Economy” (Foray, 2004), where
knowledge is seen as the main key factor of business success and as the foundation of
competitive advantage. Knowledge is seen as the most important strategic resource (Davenport
and Prusak, 1998; Drucker, 1993; Hall, 1993; Stalk et al. 1992). The value incorporated in the
products and services is mainly due to the development of organisational knowledge resources
(Quinn, 1992). In fact, the capability of a firm to produce outputs can be considered as
integration and application of the specialised knowledge held by individuals in the organisation
(Grant, 1991). Knowledge management (KM) is the set of strategies, methods and tools to
manage the intangible knowledge asset of a firm, in order to improve its global performance. To
support KM success, there is a need to analyse the chain of knowledge creation in the firm, in
order to assess the added value that leads to performance. If there exists a good knowledge
creation process in the organisation without linking this process to upper capability, it may be
inefficacy. The process must be seen as overall capability.
Knowledge performance can be measured in two categories. One is financial performance.
However, despite the wide acknowledgements of knowledge as a strategic resource, it is still not
well understood how KM impacts business performance, and firms are unable to evaluate the
return on investment in knowledge, though it is a mean of resource optimisation, which impacts
on costs (Chong et al., 2000).
The other way of measurement can be based on the competence-based view theory of the firm.
This theory considers the firm as a portfolio of competencies and its competitiveness is based on
the creation and development of competencies and on the realisation of a strategy able to create a
link between aims, resources and competencies (Prahalad and Hamel, 1990). Those
competencies have a cognitive nature, and it allows the identification of processes to manage
capabilities. Knowledge creation or organisational learning is the main processes for the
development of competencies (Leonard-Barton, 1995; Nelson, 1991; Prahalad and Hamel, 1990).
Carlucci et al. (2004) state ‘the cognitive competence perspective can be summarised in the
interpretation, which defines a company’s competence as a combination of all ‘knowledge
assets’ and ‘knowledge processes’ that allow an organisation to carry out its business processes’.
Thus, there are two ways of considering a knowledge value chain (KVC), the first one is a chain
of knowledge activities acting on the knowledge assets of the firm, and the other one is a chain
of cognitive activities acting on the knowledge processes in the firm.
Following the tremendous development of KM during those last years, the concept of KVC
appeared and has been discussed recently (Carlucci et al., 2004; Lee and Yang, 2000; Wang and
Ahmed, 2005; Eustace, 2003; Powell, 2001; Holsapple and Singh, 2001).
In most of the cases, the proposed KVC is a set of KM processes. KVC is then a KM framework
organising fundamental KM activities as the ‘Knowledge Process Wheel’ given in Carlucci et al.
(2004). The main KM activities included in the different KVCs are mainly:
Knowledge Creation. It is of course the most important, as it accumulates the knowledge
capital, which is the ‘raison de vivre’ of any knowledge-based organisation.
Knowledge Codification. It is about capturing tacit knowledge, which is a very complex
problem, because such knowledge lays in the brain of the knowledge holders without
their conscious awareness about it!
Knowledge Sharing. Once a knowledge corpus is identified and a knowledge repository
is built, sharing that knowledge within a community is not really a standard task. It
requires a lot of efforts from building the community to implement access infrastructures.
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Knowledge Dissemination. Access to knowledge for majority of people (at least the
concerned ones: the right information to the right people) has been coined as ‘the last
mile’ problem, which implies Information Technology IT infrastructures and design
processes.
Knowledge Identification (analysis and structuring). It is a of course a basic question:
what is the relevant knowledge to create a knowledge firm? The question of what are the
knowledge needs is quite often raised.
Knowledge Evaluation. In order to perform good knowledge processes, it is now
necessary to have different grids of evaluation.
The KVC gives a KM framework to analyse the added value brought by each KM process.
Figure 1 shows an example of KVC (Wang and Ahmed, 2005), as a Porter-like model.
Figure 1: An example of knowledge value chain based on knowledge management (KM) processes.
This kind of KVC is then a chain of knowledge activities acting on the knowledge assets of the
firm.
Concerning the second kind of KVC, a chain of cognitive activities acting on the knowledge
processes in the firm, Wikipedia, the free encyclopaedia, defines a KVC as ‘a sequence of
intellectual tasks by which knowledge workers build their employer’s unique competitive
advantage and/or social and environmental benefit’. This definition can be illustrated by the
KVC proposed by Powell (2001). There are very few references for those kinds of KVC;
doubtless, there is a difficulty to chain intellectual tasks in Porter-like model.
In this paper, we propose a KVC that chains fundamental intellectual tasks (cognitive activities)
for knowledge workers, and we justify the value chain by the added value of each activity, in an
overall knowledge creation chain. This work is inspired by the work published by the Moradi
(2009) Moradi et al. (2008), and Brunel et al (2010).
The KVC is built on the DIKW (Data, Information, Knowledge and Wisdom) model, and
interprets that model as a succession of cognitive activities to transform data up to the most
adding value for the firm, which are strategic capabilities. Those transformation activities are
described precisely.
THE DIKW MODEL
The DIKW model is one of the most famous and ‘taken-for-granted’ models in the information
and knowledge literatures. It is widely used in information and knowledge management (KM),
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but this model still remains rather ‘loose’, and has not been deeply discussed or validated. For
the history of this model, and a critical study on it, we refer to the Wikipedia, the free
encyclopaedia entry and to Rowley (2007).
The most popular graphical representation for DIKW is a pyramid, as in the celebrated Maslow’s
pyramid, with data at its base and wisdom at its apex. This representation implicitly explains that
the higher elements in the pyramid need the lower elements to be defined, and that they can be
reached after a transformation process of these lower elements. The DIKW is then a chain where
information is the result of processing data, knowledge is the result of processing information
and wisdom is the result of processing knowledge. The elements of the pyramid can be seen as
having increasing values corresponding to their level. It may then appear as a value chain (Figure
2), according to Chaffey and Wood (2005) quoted in Rowley (2007).
Figure 2: The DIKW pyramid.
Another graphical representation of the DIKW model is a flow diagram where the relationships
between the components are less hierarchical, with feedback loops and control relationships. We
will use that kind of graphical representation, to visualise the value chain (Figure 3).
Figure 3: The DIKW value chain.
There seems to be only a loose consensus in the abundant literature on the DIKW model for the
definition of the different levels, as well as for the transformation processes.
We will give our own definitions of the different levels in order to give a refutable framework
for DIKW, and study the different possible transformations.
Data
Data are defined as raw facts, and learning about data as the process of accumulating facts
(Bierly et al., 2000). Data are raw materials that were accumulated by person- or machine-
based observation. According to Rowley (2007), some authors (Jashapara, 2005; Choo,
2006) introduce a new element in the DIKW chain, which is ‘signal’, that represents the
Data
Information
Knowledge
Wisdom
Data
Information
Knowledge
Wisdom
Low
High
ValueMeaning
High
Low
Information Knowledge WisdomData
Increasing Added Value
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reality perceived, selected and process through our senses to get data. In fact, in the theory of
semiotics (Eco, 1976), founded by Peirce (1934), it is assumed that reality is always
perceived as a ‘system of signs. We define data as the perception of reality by senses (that
can be extended by machine-based observation). Data are then the result of a communication
process, through sign systems.
Information
The only unambiguous definition of information is a mathematical definition given by
Shannon and Weaver (1949). This information theory is a probabilistic point of view on
information produced by a system. During the communication process, the receptor is
waiting for a certain message. Let’s take the case of a traffic light. When a person looks at
this light (the signs system observed), he already has an idea of the set of messages
transmitted by this light. A priori, he is unaware of what message is precisely going to be
transmitted. However, thanks to his experience, he expects to receive some messages with
different probabilities. The information received through a set of messages (the signs system
observed) is calculated as a mean of occurrence probabilities of that set of messages, called
entropy. In information theory, the introduction of the entropy function was a considerable
innovation that was incredibly fruitful, even as a metaphorical tool to understand what
information is. When information is considered as a concept, this information theory is not
often invoked. According to Nonaka (1994), information can be viewed from two
perspectives: syntactic (or volume of) and semantic (or meaning of) information. The
syntactic perspective is ruled by the Shannon’s theory, but the semantic aspect of information
is more important for knowledge creation, as it focuses on conveying meaning. In the
analysis of Floridi (2010), over the past decades, a standard general definition of information
(GDI) as emerged in terms of data+meaning. A straightforward way of formulating GDI is as
a tripartite definition: information is made of data, the data are well formed (remember that
‘information’ comes from Latin ‘in-formare’, i.e., ‘put in form’), the well-formed data are
meaningful (the data must comply with the meanings -semantics- of the chosen system, code
or language in question).
Knowledge
The most common definition for knowledge is a justified true belief (Chisholm, 1982): ‘I
know something, if I believe it, if I have evidence that it is true, and if it is true’. But in the
KM perspective, definitions of knowledge are much more diverse and complex than those for
data or information. Summarising all the definitions in the DIKW literature, Rowley (2007)
states that knowledge might be viewed as a mix of information, understanding, capability,
experience, skills and values. In Ermine and Leblanc (2007), an attempt is made to have a
formal theory of knowledge that is an extension of Shannon’s theory of information. In that
theory, knowledge has three tangled components: information, meaning and context. The
information is rules by the Shannon’s theory, meaning is ruled by semiotics theory, and
context by connected graphs theory. It is possible to define formal entropy that represents
knowledge, based on these three components. Meaning is strongly depending on context that
can be social, professional or operational. Hence, we will define knowledge as information (a
set of messages produced by a system), which have a specific meaning in a specific context.
This theory has been fully developed in Ermine (2000).
In the scope of KM, there is an important distinction between explicit and tacit knowledge. In
general, tacit knowledge is defined as embedded in the individual and explicit knowledge as
residing in documents, databases and other recorded formats. Knowledge is a resource for an
entity’s capacity for effective action, for instance Spender (1996) considers knowledge as
data, meaning and practice.
Wisdom
If the definition of knowledge is complex and not really consensual, the definition of wisdom
is nearly inexistent, and there are very limited discussions of that concept in the DIKW
literature (Rowley, 2007). Wisdom is usually defined as in Wikipedia: ‘a deep understanding
and realising of people, things, events or situations, resulting in the ability to choose or act to
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consistently produce the optimum results with a minimum of time and energy’. Therefore,
we define wisdom as the ability to best use of knowledge for establishing and achieving
desired goals and learning about wisdom as the process of discerning judgments and action
based on knowledge. Considering that we develop our work as a practical and to a certain
extent in the organisational context, for it to be practical, we must to avoid the terminology
that is indistinguishable and ambiguous. We believe that in the engineering and modelling
context the notion of wisdom is not very clear and so it is necessary to clarify that to be
understandable and usable. For this reason, we divided this indistinct and abstract concept as
individual wisdom, which is competence and expertise, and collective wisdom as capability.
Individual Wisdom (Competence/Expertise)
Competence is a standardised requirement for an individual to properly perform a specific
job. It encompasses a combination of knowledge, skills and behaviour utilised to improve
performance. More generally, competence is the state or quality of being adequately or well
qualified, having the ability to perform a specific role. For instance, management
competency includes the traits of systems thinking and emotional intelligence, and skills in
influence and negotiation. A person possesses a competence as long as the skills, abilities and
knowledge, which constitute that competence, are a part of him, enabling the person to
perform effective action within a certain workplace environment. Therefore, one might not
lose knowledge, a skill or an ability, but still lose a competence if what is needed to do a job
well changes. Expertise is a characteristic of individuals and is a consequence of the human
capacity for extensive adaptation to physical and social environments. Prahalad and Hamel
(1990) in their seminal work defined competence as the roots of competitiveness. Then,
competence can be defined as individual mobility, integration and transfer of knowledge and
capacity in order to obtain the results.
Organisational Wisdom (Capability)
Capability is the ability to perform actions. In human terms, capability is the sum of expertise
and capacity. We consider capability as high level of competence in organisation level. Grant
(1996) views organisational capability as the outcome of knowledge integration, complex,
team-based productive activities and dependent upon firm’s ability to harness and integrate
the knowledge of many individual specialists. So make practicable and usability of
competence in organisational wide range will generate some core competency and dynamic
capability for organisation. In this context, organisation wide wisdom is a specific capability
for that organisation. Capability does not represent a single resource in the concert of other
resources such as financial assets, technology or manpower but rather a distinctive and
superior way of allocating resource, complex process as product development, customer
relationship and supply chain management. Furthermore, organisational capability could be
defined as: absorptive capacity (Cohen and Levinthal, 1990) (organisational ability to
assimilate new exterior knowledge,), combinative capability (Kogut and Zander, 1992;
organisational ability to aggregate actual internal knowledge), dynamic capability (Teece et
al., 1997), core competency (Prahalad and Hamel, 1990), organisational learning (Huber,
1991) and agility (Roth, 1996).
KVC AND MANAGEMENT
In terms of management activity, data management has for role to control, protect, deliver and
enhance the value of data assets. It ensures the continued existence and the quality of the
organisational memory. In ‘cognitive’ terms, data management ensures the memorisation
function of the organisation. Usually, information management includes the same types of
functionalities of organisation of and control over the structure, processing and delivery of
information.
Considering the definition we have given of information (data+meaning), information
management has for role to give sense to data, to help workers and managers to take decision in
their tasks at various levels (operational, tactical and strategic). Information processing is crucial
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for decision making, as it is well known for a long time (Simon and March, 1958). Information
management allows conceptualisation and brings understanding as added value to the
organisation.
In Averson (2010), KM is suggested as a strategic management activity in the learning and
growth perspective, in the framework of the Intellectual Capital given by the Balanced Scorecard
(Kaplan and Norton, 1996): ‘A learning and growing organisation is one in which KM activities
are deployed and expanding in order to leverage the creativity of all the people in the
organisation’. The KM needs for competencies development is stressed by many authors. An
internal learning process is necessary for development and maintenance of competencies
(Nelson, 1991; Prahalad and Hamel, 1990). One of the conclusions in the survey by Carlucci et
al. (2004) is that KM sustains the dynamics of organisational learning and improvements of
performances in organisational processes and then enables an organisation to grow and develop
organisational competencies. KM supports different learning capacities: synthesising different
types of information acquiring new knowledge, behaviours and skills. In an organisation, KM
facilitates the learning of its members who are continually learning to learn together and then
provide a continuous transformation of the organisation itself. This is what is called a ‘learning
organisation’ (Pedler et al., 1997; Argyris, 1999). In the KVC, the added value brought by KM is
learning.
Competence is knowledge in action. In the DIKW chain, Rowley (2007) quotes different
definition of ‘wisdom’ that may fit to the concept of competence as efficient knowledge in
action:
accumulation of knowledge, which allows you to understand how to apply concepts from
one domain to new situations or problems;
ability to act critically or practically in any given situation;
use of knowledge and information; and ‘right judgement’;
manner in which knowledge is held, and how that knowledge is put to use;
capacity to put into action the most appropriate behaviour, taking into account what is
known (knowledge) and what does the most good
Competence reflects a broad and deep ability for comprehending the environment and adapting
to it by taking proper decisions and actions. It is the appropriate use of knowledge to improve
performance (usually, it is considered essentially a personal issue but can also have some
collective issue aspects). That ability is generally called ‘intelligence’. In that sense, within the
KVC, the added value brought by competence management is intelligence.
The difference between implementation in enterprise competence and capability management is
in the collective, overall and organisational wide nature of capability. Finally, capability
management leads to better success for organisation and so obtain the upper performance and
overall wealth. The competence-based view, and the knowledge-based view theories (Grant,
1991; Sveiby, 2001), consider knowledge as drivers for the formulation and development of
strategy. Knowledge capacity is then fully integrated in the company’s objectives. The benefit
for the company is an overall capacity of innovation, as a global change (incremental or radical)
in thinking, products, processes or organisations. Aligning the strategy with the competence
portfolio leads the company to global wisdom, although this concept is not yet defined in the
literature. If the individual wisdom is a superior cognitive process involving knowledge,
judgement and awareness, leading to an appropriate behaviour (Rowley, 2007), then we can say
that the capability management corresponds to a high level maturity of the organisation, which
acts properly, with respect to its commitments and its environment.
We can summarise the KVC and its management in (Figure 4).
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Figure 4: Knowledge value chain and management.
TRANSFORMATION PROCESSES IN KVC
According Rowley (2007), if it is difficult to find some consensus on the different definitions of
the concepts in the DIKW chain, there is less agreement as to the nature of the processes that
convert one concept into another concept. The discussion about wisdom is one more time
inexistent.
If the DIKW chain is seen as a value chain, in the context of KM in a firm, then we can be more
explicit and clear. According Moradi (2009, Chap. 4, p. 10), transformation process as
supportive activities in KVC may be divided into two main categories: the first category is more
tangible and objective and could be done by human being and machine-based reasoning. This
category concerns adding value from reality to explicit knowledge. For that category, the role of
information technology as processor is mainly accepted. The second processing category is
going from information and explicit knowledge up to capability. In this category, human being is
a key point and it is intangible, subjective, about beliefs and commitments and action. In this
category, the role of information technology is enabler and not the main element. To describe the
transformation processes, in an effective and understandable way, we will decompose them in
three points of view, related to the definition of knowledge given in the description of KVC:
- Syntactic point of view, which gives the final forms of the outcomes of the
transformation process. This is the visible part of the results in the process.
- Semantic point of view, which gives the enablers to build sense in the process. These
enablers are cognitive filtres allowing interpretation activities in the process to deliver the
outcomes.
- Context point of view, which gives the (cognitive) situation where the process takes
place.
Figure 5: Transformation processes in knowledge value chain
This decomposition is in the style of the one called « triple instrumentation », also used to
describe the KVC in Brunel (2008) and Moradi (2009). We will not discuss deeply the different
Data
Data Management
Intelligence
Maturity
Memorisation
Understanding
Learning
Information
Knowledge Wisdom
Explicit Tacit Competence Capability
Information Management
Knowledge Management
Competence Management
Capability Management
Knowledge
Performance
Knowledge Value Chain
Cognitive
Value Chain
Data Information
Knowledge Wisdom
Explicit / Tacit Competence Capability
Knowledge
Performance
Perceptive
filters Conceptual
filters
Observation Experience
Strategic
filters
Signs Codes
Structuring
Models
Learning Vision
Knowledge
Strategy
Theories
Practices
Action
Context
(Situation)
Syntax
(Form)
Semantics
(Interpretation)
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concepts, but give some standards definitions, that are shared by common sense, essentially
extracted from dictionaries. Hence, the framework for transformation processes in the KVC we
propose is just a starting point for future research, and is oriented towards efficient application
(Figure 5).
The starting point of the transformation chain is reality, as a set of things possessing actuality,
existence or essence, which exists independent of human awareness.
1) Transforming reality into data is acquiring signs (signals) through perceptive filtres via
observation
A sign is something that suggests the presence or existence of a fact, condition or quality. More
precisely, a signal is an indicator that serves as a means of communication. This is the ‘semiotic
hypothesis’ that assumes that reality is communicated to us as a ‘sign system’.
The transformation process is a perception process that is an organisation (in a sign system) of an
unprocessed result of the stimulation of sensory receptors (it may be artificial sensors or sensory
receptors like the eyes, the ears…).
Observation is a detailed examination of phenomena prior to analysis, diagnosis or interpretation.
It implies usually the act of recording something, eventually with instruments.
2) Transforming data into information is coding data trough conceptual filtres via a structuring
activity
A code is a system of symbols, given certain arbitrary meanings, used for transmitting messages.
The transformation process consists in building concepts that are something formed in the mind;
a thought or notion that corresponds to some class of entities and that consists of the
characteristic or essential features of the class.
Building concepts need a structuring posture, which is a frame of mind favourable to make
interrelation or arrangement of parts in a complex entity.
3) Transforming information into knowledge is building models through theories via learning
A model is a schematic description of a system, theory or phenomenon that accounts for its
known or inferred properties and may be used for further study or action.
A model is supported by a theory, which is, in the common sense usage, a well-substantiated
explanation of some aspect of the natural world; an organised system of accepted knowledge that
applies in a variety of circumstances to explain a specific set of phenomena. It is a conceptual
model (explanation) of how the world works.
Using models and theories in KM can be made in a context of learning that is by definition, the
cognitive process of acquiring knowledge (and more generally skills).
4) Transforming knowledge into competences is implementing a set of practices through action
via experience
Practice is a repeated performance of an activity in order to learn or perfect a skill, a habitual or
customary action or act (often used in the plural). Economists speak about routines (Nelson and
Winter, 1982; Lazaric, 2000) as collective competences in the form of prescribed, detailed course
of action to be followed regularly, although they are essentially personal and tacit. They have a
global formulation for achieving a collective task, but they are only collective in their result. This
codified knowledge needs individual experience for appropriation and use by the actors.
These practices are built step by step through action, which denotes as usual an organised
activity to accomplish an objective. Action is seen like a cognitive filtre, enabling the pertinence
of the learned practices.
The adequate posture is then experience : experience is the situation by which a person acquires
knowledge about the world, as contrasted with reason. Experience is an active participation in
events or activities, leading to the accumulation of knowledge or skill.
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5) Transforming competences into capabilities is building a strategy (knowledge strategy)
through strategic filtres (alignment) via a vision
A strategy is a particular long-term plan for success.
Alignment, which is a proper or desirable coordination or relation of components, is the adequate
tool as integration or harmonisation of aims, practices, etc. within an organisation.
The capacity to build a strategy involving the corporate knowledge, aligned with the corporate
strategy requires a vision, as an unusual competence in discernment or perception; an intelligent
foresight. That term of vision, especially for future developments, has a religious connation, but
here stops the KM context!
CONCLUSION
The deliberate and consciously management of KVC will lead to multiple benefits for
organisation. It will result on rapid and more innovation, effectiveness, efficiency and
performance of firms. By improving learning and agility of organisation, it will produce
competitive advantage, expanded economic intelligence and so generating wealth for
organisational stakeholders.
We have studied a KVC that takes in account the nature of individual and organisational
knowledge in a firm. It is a continuous transformation chain from reality perception through data,
to organisational wisdom reflecting the maturity of the firm. If, in that chain, data, information
and, in some aspects, knowledge are concepts that begin to make consensual sense, wisdom,
despite its position at the ultimate end of the chain, is very fuzzy, especially in an organisational
context. In the KM context, it may be interpreted as individual wisdom (competence) and
organisational wisdom (capability).
Management of KVC brings gradually the organisation to superior cognitive capacities, from
memorisation, understanding, learning and intelligence up to maturity. Processing KVC requires
the transformation of data into codified information, knowledge models, portfolio of practices up
to a knowledge strategy. It requires also different postures from observation, structuring,
learning, experience and vision.
This article gives a first simplified framework and seeks to provoke debate about the basic
concepts of information management and KM. It is a first brick to help knowledge strategists in
organisations, and to understand where is the added value in the knowledge capital, that new
wealth of organisations.
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About The author :
Jean-Louis Ermine has a PhD in fundamental mathematics at the University Denis Diderot of Paris, and a
National Research Director Title in computer science at the University of Bordeaux. He worked in the French
Atomic Energy Commission (CEA) as a KM expert for more than 10 years. Since 2003, he is professor at the
national public organisation “Institut TELECOM” where he is now Director for Innovation at TELECOM
Business School. He has written 4 books and more than 100 articles in peer-reviewed journals and conferences.
He is president of the French Knowledge Management Club since 1999, an association rallying a lot of French
speaking companies. He is Expert for UN (UIT, AIEA) since 2003. He is also the chairman of the French
Speaking Congress in Knowledge Management (GeCSO) since 2008, and the president of the French speaking
Knowledge Management Society. He has been project leader or advisor in numerous research or industrial KM
projects in public or private companies in France (Industry, Energy, Transport, Defence, Bank, SMEs …) and
abroad (Sonatrach (Algeria), Hydro-Québec, Public Administration (Canada), IPEN (Brazil), National Nuclear
Safety Authorities (Asia)…)
