From Anarchy to System: A Novel Classification of Visual Knowledge Codification Techniques: Novel Classification of Visual Knowledge Codification Techniques

Abstract

The paper suggests a classification of visual knowledge codification (diagramming) techniques for multi-perspective business systems analysis and design. The classification is based on seven main categories of knowledge: what-knowledge, how-knowledge, who-knowledge, why-knowledge, what for-knowledge, when-knowledge and where-knowledge. The classification defines both knowledge type and the most appropriate kind of diagramming technique. Examples for use of this classification system for marketing function applications are presented. Specific examples of these applications include mind map, concept map, Ishikawa diagram, strategy map, business process models and function trees. It is hoped that the new classification will allow better practical use of diagramming techniques in business and lead to fewer professional misunderstandings and more effective business communication. The aim of this research is thus to improve visual literacy among both business practitioners and educators.

Full text

■Research Article
From Anarchy to System: A Novel
Classification of Visual Knowledge
Codification Techniques
Dmitry Kudryavtsev*and Tatiana Gavrilova
Graduate School of Management, Saint-Petersburg State University, Russia
The paper suggests a classification of visual knowledge codification (diagramming) techniques for multi-perspective
business systems analysis and design. The classification is based on seven main categories of knowledge:
what-knowledge, how-knowledge, who-knowledge, why-knowledge, what for-knowledge, when-knowledge and
where-knowledge. The classification defines both knowledge type and the most appropriate kind of diagramming
technique. Examples for use of this classification system for marketing function applications are presented. Specific
examples of these applications include mind map, concept map, Ishikawa diagram, strategy map, business process
models and function trees. It is hoped that the new classification will allow better practical use of diagramming
techniques in business and lead to fewer professional misunderstandings and more effective business communication.
The aim of this research is thus to improve visual literacy among both business practitioners and educators. Copyright
© 2016 John Wiley & Sons, Ltd.
INTRODUCTION
Knowledge visualization has proved to be an
effective tool for knowledge creation, acquisition
and transfer (Eisenstadt et al., 1990; Eppler and
Burkhard, 2007; Gavrilova and Gulyakina, 2011).
According to Eppler and Burkhard (2007), there
are several formats of visual knowledge representa-
tion: heuristic sketches (e.g. the ad hoc, joint draw-
ings of complex ideas in meetings), conceptual
diagrams (such as Ishikawa diagrams), visual
metaphors (such as an iceberg visualization
distinguishing implicit and explicit forms of knowl-
edge), knowledge maps (such as a landscape of
in-house experts) and interactive visualizations (to
help users to explore complex information in inter-
active form). These graphic formats capture not
only (descriptive) facts or numbers (information
visualization), but also contain prescriptive and
prognostic insights, principles, basic assumptions
and relations. The focus of the current research is
on qualitative conceptual diagrams —schematic
depictions of abstract ideas that use standardized
shapes to structure information and illustrate rela-
tions. Conceptual diagrams help to make abstract
concepts accessible, reduce complexity to the key
issues (Huff, 1990), amplify cognition and facilitate
discussion of relationships. In this paper, we will
often use diagramming and visual knowledge
codification techniques synonymously.
Diagrams (Blackwell and Engelhardt, 2002)
constitute the basis for visual knowledge represen-
tation, and elaborate diagrammatic techniques
typically form visual modelling languages (Harel
and Rumpe, 2000). In computer science, these
techniques are reflected in such languages as UML
(Rumbaugh et al., 2004) and IDEF (Mayer et al.,
1992). They are also integrated in software engineer-
ing methods, for example, the Structured Analysis
and Design Technique, and are organized by archi-
tecture frameworks, such as the Zachman frame-
work (Zachman, 2003).
The focus of this paper is the realm of manage-
ment. Managers frequently use diagrams in their
work (Galloway, 1994; Hodgkinson et al., 2004;
Lengler and Eppler, 2007), but the choice of dia-
grams is often error-prone and inconsistent (Eppler
and Jianxin, 2008).
For the effective choice of visualization method,
at least five perspectives should be considered
(Eppler and Burkhard, 2007). These perspectives
*Correspondence to: Dr Dmitry Kudryavtsev, Graduate School of
Management, Saint-Petersburg State University, Russia.
E-mail: d.v.kudryavtsev@gsom.pu.ru
Knowledge and Process Management
Volume 24 Number 1 pp 3–13 (2017)
Published online 20 May 2016 in Wiley Online Library
(www.wileyonlinelibrary.com) DOI: 10.1002/kpm.1509
Copyright © 2016 John Wiley & Sons, Ltd.

answer five key questions with regard to visualizing
knowledge, namely:
(i) What type of knowledge is visualized (content)?
(ii) Why should that knowledge be visualized
(purpose, knowledge management process)?
(iii) For whom is the knowledge visualized (target
group)?
(iv) In which context should it be visualized (commu-
nicative situation: participants, place/media)?
(v) How can the knowledge be represented
(method, format)?
The focus of this paper is knowledge type, and it
can be used for identifying the type of knowledge
with respect to its content. Any complex entity can
be viewed from several perspectives (aspects,
facets) and in different strata (layers) (Gavrilova
and Voinov, 1998; Kingston and Macintosh, 2000;
Zachman, 2003). The following differentiated
question-based aspects are proposed (Kingston
and Macintosh, 2000; Alavi and Leidner, 2001;
Zachman, 2003; Gavrilova and Gulyakina, 2011):
WHAT-Knowledge: conceptual representation
WHAT_FOR-Knowledge: strategic representation
HOW_TO-Knowledge: functional representation
WHO-Knowledge: organizational representation
WHERE-Knowledge: spatial representation
WHEN-Knowledge: temporal representation
WHY-Knowledge: causal representation.
Today, there is no validated prescriptive frame-
work that links business diagrams with knowledge
types and that offers specific diagrams for particular
knowledge types. This defines the research ques-
tion: What types of conceptual diagrams are most suit-
able for specific types of knowledge (content)?
RELATED WORK
Diagram classifications
A periodic table of visualization methods (Lengler
and Eppler, 2007) provides good overview of dia-
grams for managers. These authors decided that
the classification dimensions should be easy to use
and have some proven benefits. The organization
principles were related to the situation in which
the visualization is used (when?), the type of con-
tent that is represented (what?), the expected visual-
ization benefits (why?) and the actual visualization
format used (how?). They organized these dimen-
sions in a specific table of visualization methods.
However, we conclude that while this is an impres-
sive result, the content (what?) dimension, for the
focus of the current research, is not sufficiently spec-
ified and includes only process (stepwise cyclical in
time and/or continuous sequential) and structure
(hierarchy or network).
Lohse et al. (1994) reported a structural classifica-
tion of visual representations, identifying 11 major
clusters: graphs, tables, graphical tables, time charts,
networks, structure diagrams, process diagrams,
maps, cartograms, icons and pictures. Criteria for
classification were represented using ten anchor-point
phrases: spatial-nonspatial, temporal-nontemporal,
hard to understand-easy to understand, concrete-
abstract, continuous-discrete, attractive-unattractive;
emphasizes whole-emphasizes parts, numeric-
nonnumeric, static structure-dynamic process and
conveys much information-conveys little informa-
tion. We conclude that this classification works
mostly in the structural dimension. The semantic
dimension of diagrams is not covered.
Systemic visual modelling frameworks
Multi-perspective modelling framework (Kingston
et al., 1997; Kingston and Macintosh, 2000) is close
to our research, suggesting analysing information
or knowledge from six perspectives (Who, What,
How, When, Where and Why) at up to six levels of
detail (ranging from “scoping”the problem to an
implemented solution). The authors suggest that
knowledge engineers should apply whatever
modelling techniques they prefer, as long as all the
necessary perspectives are covered. They propose
some modelling techniques that are appropriate
for particular perspectives or levels of abstraction.
However, their description of perspectives can be
extended using diagnostic questions and elements
of conceptual models; furthermore, their recom-
mendations for diagramming techniques should be
more specific and business/management oriented.
Glassey (2008) suggested method and instruments
for visual modelling of integrated knowledge. This
approach provides conceptual diagrams for “know-
how”,“know-what”,“know-who”and “know-
why”knowledge types. Additionally, it supports
interlinks between knowledge types using matrices.
But this approach does not cover all the necessary
knowledge types (e.g. “when”-knowledge) and pro-
vides a narrow set of diagramming techniques with a
focus on UML (e.g. UML collaboration, sequence and
collaboration diagrams for “how”-knowledge).
Knowledge maps (knowledge about knowledge)
are also included in the Glassey visual modelling
kit, but we think that they form another layer with
all types of knowledge and should be considered
independently (Eppler, 2008). Glassey method and
instruments are close to enterprise modelling meth-
odologies, which also provide comprehensive visual
modelling languages such as Archimate (The Open
Group, 2012) and MEMO (Frank, 2002). However,
these methodologies do not include all the popular
types of diagram used by managers, are rigid
compared with the suggested approach and should
be considered as the next maturity level in
4 D. Kudryavtsev and T. Gavrilova
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,3–13 (2017)
DOI: 10.1002/kpm

organizational visual literacy (this topic is also
addressed in the Limitations of the Approach section
and in the conclusion).
RESEARCH METHODOLOGY
This paper is in line with design science research —
a research paradigm in which a researcher answers
questions relevant to human problems via the
creation of innovative artefacts, thereby contribut-
ing new knowledge to the body of scientific
evidence (Hevner and Chatterjee, 2010). Artefact
design implies constructive research methods.
According to Peffers et al. (2008), the design science
research process comprises six steps: problem
identification and motivation; identification of the
objectives for a solution; design and development;
demonstration; evaluation; and communication.
(a) Problem identification and motivation were
provided in the Introduction.
(b) Identification of the objectives involved specifi-
cation of the requirements for the innovative
artefact. R1: ability to choose the category or
specific type of diagram for every type of
knowledge; R2: ability to formulate the user ’s
need for the visual knowledge codification in
his/her own “words”as the “customer voice,”
following the logic of House of Quality (Hauser
and Clausing, 1988); R3: provision of a limited
set of popular and mature diagramming tech-
niques (no requirement for complete coverage).
(c) The design and development step has resulted
in the method for selection of diagraming tech-
niques based on a multi-perspective knowledge
typology. The suggested method is based on
semantic analysis of knowledge types, diagram-
ming techniques and mappings between them.
This analysis is based on the concept of
ontology (Gruber, 1995; Maedche et al., 2003;
Rodríguez and Egenhofer, 2003); more specifi-
cally, we use ontology design patterns
(Gangemi and Presutti, 2009) in our approach.
For further detail, see (Kudryavtsev et al.,
2013). This work describes the theoretical basis
of the novel classification.
Several sources of diagramming techniques were
used in order to reflect both empirical and theoreti-
cal aspects.
One of the authors has participated in manage-
ment consultancy projects since 2003, in the
Business Engineering Group company. He took part
in some 15 projects in the area of enterprise perfor-
mance management, organizational design, busi-
ness process management, business transformation
and IT strategy development. These projects were
in Russia and Ukraine; the clients were medium-
sized industrial companies. Such projects implied
extensive analysis of organizational documents
with different conceptual diagrams, such as analyti-
cal reports, business presentations, procedures and
corporate standards. This documentary secondary
data (Cassell and Symon, 2004; Saunders et al.,
2011) was one of the sources for identification of
diagramming techniques used in business.
Popular diagramming tools suggest predefined
techniques (templates) and their classification.
Because the vendors of these tools have advanced
marketing knowledge of customer needs in diagram-
ming, their selection of techniques is representative
for the current situation. Visio 2010 (https://prod-
ucts.office.com/en-us/visio) provides the following
eight embedded categories: Business, Engineering,
Flowchart, General, Maps and floor plans, Network,
Schedule, Software and Database. Smart Draw
(http://www.smartdraw.com/) provides the follow-
ing 30 categories, including Flowchart, Mind Map,
Organizational Chart, Cause & Effect Diagram, Deci-
sion Tree, Project Management, Education, Engineer-
ing, Learning and Strategic Planning.
In addition to the empirical sources of diagram-
ming techniques, we also reviewed the literature in
order to add some mature techniques to insuffi-
ciently covered knowledge types, for example, the
argument-mapping technique (Kirschner et al.,
2003) was added to the WHY-knowledge category.
Given the vast number of different diagramming
techniques, we applied some criteria: (1) Qualitative
diagrams are included; (2) We consider modelling
business objects at a general systemic level, so we ex-
cluded domain-specific diagrams (e.g. Porter’sfive
forces diagram includes business strategy-specific
concepts, such as Suppliers, Substitutes, Buyers
and New Entrants); (3) We excluded matrix-based
methods, such as Growth-Share Matrix and SIPOC
tables (Rasmusson, 2006) (see (Phaal et al., 2006)
and their matrix-tool catalogue http://www.ifm.
eng.cam.ac.uk/resources/t-cat/ for further detail).
(d) Demonstration. The paper demonstrates the
use of the method to solve practical problems,
so a case study was used (Hevner and
Chatterjee, 2010).
(e) Evaluation. Comparison of the suggested
method with the requirements is presented in
the next section.
METHOD FOR THE SELECTION OF VISUAL
KNOWLEDGE CODIFICATION
TECHNIQUES
We sugge s t s p ec ification of knowledge types (Figure 1
and Table 1) and classification of diagrams/visual
knowledge codification techniques (Figure 2) in order
to choose diagrams for the particular knowledge type.
Informal specification of knowledge types (Figure 1) is
done using the competency questions technique
(Gruninger and Fox, 1995; Ren et al., 2014). More
Novel Classification of Visual Knowledge Codification Techniques 5
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,3–13 (2017)
DOI: 10.1002/kpm

formal specification describes concepts and relation-
ships (Stock, 2010), which are associated with the
knowledgetype(Table1).
The method for the choice of diagram is
represented using the following steps:
(i) Articulate the question (in your own words)
that corresponds to your knowledge codifica-
tion needs (see R2 from requirements speci-
fication in the previous section);
(ii) Align your question with the competency
questions from Figure 1, to find similar one(s).
This will help you to define the relevant
knowledge type (see R1);
(iii) Check your choice of knowledge type using
Table 1. Align the key words and meaning of
your question with the concepts and relation-
ships which are associated with the knowledge
types (see R2);
(iv) Identify the diagrams which are associated
with the necessary knowledge type using
Figure 2 (see R3);
(v) Choose from the short-list the most relevant
diagram type, which is associated with the
necessary knowledge type.
MARKETING CASE STUDY
The Repair Service Company (RSC) is a subsidiary
of one of the biggest oil refineries in Russia. It was
Figure 1 Description of knowledge types using competency questions. [Colour figure can be viewed at wileyonlinelibrary.com]
Table 1 The list of concepts and relationships for the knowledge types
WHAT-knowledge Key concepts: Entity, Concept, Class, Instance, Property;
Relationships: subClassOf, hasPart/isPartOf, type, classifies/isClassifiedBy;
HOW-knowledge Key concepts: Action, Task, Process, Project, Work package, Inputs/Outputs;
Relationships: sequence: precedes/follows; hasInput/hasOutput;
isConsequenceOf/hasConsequenceOf;
Supporting concepts: Gateways, Events, Resource, Inputs/Outputs
WHO-knowledge Key concepts: Agent, Role, Organizational Unit, Organizational Position, Person;
Relationships: playsRole/is Role, is Responsible for, executes/isExecutedBy OR
performs/isPerformedBy, participateIn/has participant, subordinateOf
WHY-knowledge Key concepts: Cause, Effect, Variable, Problem, Premise;
Relationships:influence on/is influence by, has cause, supports.
WHAT FOR-knowledge Key concepts: Goal, Objective, End, Mean, Requirement, Value;
Relationships: help achieve/is achieved by, conflicts with, subGoalOf, influence, has effect;
WHEN-knowledge Key concepts: Start/End time, Duration, Time interval, Time instant (hour, minute, second),
Date (Year, Month);
Relationships: isDurationOf, has Start/End date, has Start/End time, before/after.
WHERE-knowledge Key concepts: Region, Place, Location;
Relationships: located in/is location of
6 D. Kudryavtsev and T. Gavrilova
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,3–13 (2017)
DOI: 10.1002/kpm

founded in the late 1990s by separating the
maintenance and repairs department from the
parent company. Nowadays, RSC is an independent
enterprise, despite its 100% ownership by the oil
refinery, with which it continues to maintain strong
relationships. RSC rents space from the parent
organization and therefore is located in the area of
the refinery. Its main business activities are repair
of dynamic (e.g. pumps) and electric equipment
together with manufacturing spare parts.
At the moment, the primary customer of RSC is
its parent organization (over 90% of revenue). In
turn, RSC is the only company that provides repair
services to the oil refinery. In the long term, the
refinery wants to see RSC as a strategic partner.
There are many issues currently faced by RSC, one
of the most important being widening its range of
customers (external market) and developing its
own distribution network. At the moment, RSC
sells its production and services directly; it has no
marketing department, as it has only just begun to
plan its entry into the external market.
In order to establish a marketing capability, RSC
must clarify “what is marketing?”by specifying
the main marketing concepts, define “what should
the company do in the marketing domain?”,“what
are the current problems and their causes in the
marketing domain?”,“what are the objectives and
performance metrics of the marketing domain?”,
“how should RSC organize marketing processes?”,
“who will be responsible for these marketing
processes?”and finally, “how should it establish
marketing capability?”.
In order to answer all these questions, the
company used both diagrams and standard formats
including text, for example, to describe principles in
the marketing domain; and tables, for example, to
describe business process suppliers, inputs, outputs
and customers (SIPOC tables: Rasmusson, 2006);
roles and responsibilities (RACI matrix: Smith and
Erwin, 2005); and Balanced Scorecards (Kaplan
and Norton, 1996).
Diagram classification and the method for the
choice of diagram helped RSC to identify the most
suitable visual knowledge codification techniques
in order to answer the aforementioned questions.
“What is marketing?”corresponds to the “What is
it?”question (Figure 1) ➔WHAT-Knowledge ➔
Mindmap (Buzan, 2006; Koznov et al., 2011) can be
effective for this knowledge according to Figure 2.
So a marketing mindmap (Figure 3)
1
was developed
as the basis for marketing capability deployment.
Clarification of the main marketing concepts
corresponds to such questions from Figure 1 as
“What is the organization of marketing knowledge
domain?”and “What is the relationship between
entities?”➔WHAT-knowledge ➔Concept map
(Novak and Cañas, 2008) was chosen using
Figure 2 in order to explain the marketing concep-
tual structure; see the example in Figure 4.
“What should the company do in the marketing
domain?”was reworded as “What are the
constituents (or parts) of the marketing function
(capability)?”. This type of question can be found
in the list of competency questions that describes
WHAT-knowledge (Figure 1). WHAT-knowledge
1
All the client-specific data were eliminated from the diagrams
because of the non-disclosure agreement. This therefore consti-
tutes a generalized/reference model of the marketing domain.
Figure 2 Classification of visual knowledge codification techniques. *, these are universal techniques that can be applied to different
knowledge types, but are more frequently used for “what”-knowledge. [Colour figure can be viewed at wileyonlinelibrary.com]
Novel Classification of Visual Knowledge Codification Techniques 7
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,3–13 (2017)
DOI: 10.1002/kpm

can be represented by domain-independent
diagrams: concept map, tree diagram, class
diagram, functions tree and so on (Figure 2). The
functions tree was selected to represent “What
should the company do in the marketing domain?”
(Figure 5).
“What are the current problems and their causes in the
marketing domain?”was reworded as “What are the
causes or reasons for the situation/problem?”from
Figure 1 ➔WHY-knowledge. Table 1 also supports
the choice of this knowledge type, as the “Problem”
and “Cause”concepts and the “has cause”type of
relationship are all associated with WHY-
knowledge. Figure 2 recommends cause-and-effect
or an Ishikawa diagram (Ishikawa, 1963; Kenett,
2007) for WHY-knowledge, and this was chosen
and created for the representation of problems and
their causes in RSC marketing (see Figure 6).
“What are the objectives and performance metrics of
the marketing domain?”focuses on the term
“Objective”, so Table 1 helps to associate this
question with WHAT FOR-knowledge. Figure 2
Figure 3 Mindmap for the marketing domain (WHAT-knowledge example). [Colour figure can be viewed at wileyonlinelibrary.com]
Figure 4 Concept map for the marketing domain (WHAT-knowledge example). [Colour figure can be viewed at wileyonlinelibrary.com]
8 D. Kudryavtsev and T. Gavrilova
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,3–13 (2017)
DOI: 10.1002/kpm

suggests “Strategy map”(Kaplan and Norton,
2004), which was chosen and created (Figure 7).
Performance metrics were integrated into a
marketing Balanced Scorecard and represented in
table format.
A business process diagram was constructed
to help in answering “How should RSC organize
its marketing processes?”(Galloway, 1994), a
HOW-knowledge diagram. The list of RSC market-
ing processes included more than 50 elements, and
Figure 5 Function tree for marketing (WHAT-knowledge example). [Colour figure can be viewed at wileyonlinelibrary.com]
Figure 6 Ishikawa diagram for marketing (WHY-knowledge example). [Colour figure can be viewed at wileyonlinelibrary.com]
Novel Classification of Visual Knowledge Codification Techniques 9
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,3–13 (2017)
DOI: 10.1002/kpm

more than 15 processes were mapped. Figure 8
shows a sample process map.
“How to establish marketing capability?”led to a
project portfolio, which was organized into a
marketing implementation programme. This pro-
gramme was partially represented in tables
(Projects, Priorities, etc.) and graphically using
Gantt charts (Wilson, 2003). This information is not
presented here, because it is client specific.
All these diagrams not only work independently,
but also support each other. For example, Figures 3
and 4 provide a glossary for other diagrams; the
structure of marketing functions (Figure 5) helped
to organize primary marketing activity objectives
(Figure 7); the structure and contents of cause and
effect diagram (Figure 6) helped to describe
marketing-enabling system objectives (Figure 7);
the function tree (Figure 5) includes a “Customer
satisfaction analysis”function, which is supported
by “Customer satisfaction analysis based on the
completed orders”process (Figure 8).
These visual knowledge representations sup-
ported RSC managers during their strategy-to-
execution process, helping them to articulate a
functional marketing strategy and link it with the
operating model; finally, the organizational develop-
ment programme was suggested. The chosen
diagram types were accepted by managers and
formed the corporate standard of RSC.
LIMITATIONS OF THE APPROACH
The use of diagramming techniques, which can be
selected by the proposed method, has its difficulties
and limitations. One of the problematic issues in the
Figure 7 Strategy map for operational marketing (WHAT FOR-knowledge example). [Colour figure can be viewed at
wileyonlinelibrary.com]
Figure 8 Business process diagram for customer satisfaction
analysis based on the completed orders (HOW-knowledge ex-
ample). [Colour figure can be viewed at wileyonlinelibrary.com]
10 D. Kudryavtsev and T. Gavrilova
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,3–13 (2017)
DOI: 10.1002/kpm

use of diagrams is semantic interoperability:
different users may interpret the meanings of the
symbols in the diagram differently. Visual models
may have elements with similar names but with a
different meaning, and conversely, concepts with
similar meaning may have varying names and
signs. In order to resolve this problem, a unified
meta-model (Karagiannis and Höfferer, 2006;
Heidari et al., 2013) or ontology (Andersson et al.,
2006; Guizzardi et al., 2006; Grigoriev and
Kudryavtsev, 2011; Hinkelmann et al., 2015) can be
used. Additionally, consistent diagram examples,
developed in accordance with the chosen notation,
are useful (training dataset). The more, the better.
In this case, users will conceive the meaning of
notation elements through examples (extensional
definitions of concepts).
Business tasks usually need more than a single
diagram, in fact, a system of diagrams that cover
different knowledge types. There are methodologies
that provide visual languages for such purposes,
including the following: IDEF for information
system design and business process re-engineering
(Mayer et al., 1992), Goldratt’s theory of constraints
for continuous improvement (Dettmer, 1997),
CommonKADS for knowledge-based system design
(Schreiber et al., 2000), Archimate for enterprise archi-
tecture management (The Open Group, 2012;
Lankhorst et al., 2013), UML (Rumbaugh et al., 2004)
and MEMO (Frank, 2002) for design of information
systems. For example, IDEF includes a set of visual
sub-languages: IDEF0 is suitable for function model-
ling; IDEF1 is used for specifying entity relationships;
IDEF1X supports the design of relational databases;
IDEF3 captures the process description; IDEF4 spec-
ifies object-oriented design; and IDEF5 captures the
ontology description (Mayer et al., 1992). If the task
of interest is supported by an existing modelling
methodology and there are resources for training,
then it would be reasonable to use the existing meth-
odology. Otherwise, “light-weight”approach to the
selection of a visual knowledge codification tech-
nique will be preferable.
Finally, the proposed classification and method
do not address the choice of techniques for the
representation of relationships between different
knowledge types (e.g. “how”-“who”knowledge
links). These relationships between perspectives
can be successfully supported by matrices or
by comprehensive diagrams (Grigoriev and
Kudryavtsev, 2013).
Nevertheless, diagramming is a useful tool for
knowledge codification, which can be extended in
some situations. In particular, diagramming
techniques must be grounded in ontologies or
meta-models in order to provide semantic interop-
erability. It is also better to use complex task-specific
or domain-specific methodologies, which combine
several diagram types, if they fit the given task
and/or domain.
DISCUSSION AND CONCLUSION
The main novelty of our approach is that a new sys-
temic view of business diagrams is proposed. The
proposed classification describes the mapping
between knowledge types and popular business
diagram types. The classification takes diagram-
ming techniques from the analysis of organizational
documents, templates in popular diagramming
tools and consulting experience. It is grounded in
the semantic analysis of knowledge types and
visual knowledge codification techniques, which
enable to suggest mapping between them. This
mapping, together with the suggested informal
descriptions of knowledge types, can help man-
agers to understand the potential of visual represen-
tations and choose appropriate and comprehensive
models. The suggested diagrams can themselves
be considered as diagram types, which may have
many variations and particular notations. We have
tried to extract the most generic or prototypical
inherent elements of diagramming techniques.
The suggested case study for the marketing
domain of a medium-sized repair service company
demonstrated the relevance and applicability of
our novel method for knowledge diagram
classification and selection.
The overall aim of this research is to improve visual
literacy among both business practitioners and edu-
cators. Its classification-based approach can be con-
sidered as the first step towards visual literacy
among managers, which in its turn helps to achieve
information maturity. Visual literacy corresponds
with recent trends in enterprise modelling, in which
business diagrams describe components of the enter-
prise architecture (Lankhorst et al., 2013); according to
the “Maturity Model”for Enterprise Architecture
Representations (Polikoff and Coyne, 2005), ad hoc
visual models of enterprise architecture correspond
to the first level of maturity.
It is hoped that the proposed new classification will
allow better practical use of visual knowledge maps
in business practice and lead to fewer professional
misunderstandings and more effective business com-
munication. This approach is radically different from
the non-systemic selection of classes and categories
proposed by the main vendors of diagramming soft-
ware. The anarchy of different models increases infor-
mation overload in our era, while the proposed
classification minimizes the selection set of diagrams
and may be used as a visual guide by practitioners.
ACKNOWLEDGEMENTS
The authors wish to thank Lev Grigoriev, Chief
Technology Officer of Business Engineering Group, for
his help in the demonstration of the suggested approach
using the case study.
This research was supported financially by the Russian
Science Foundation grant (project No. 15-18-30048).
Novel Classification of Visual Knowledge Codification Techniques 11
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DOI: 10.1002/kpm

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