=Paper=
{{Paper
|id=Vol-3327/paper05
|storemode=property
|title=Conceptual Modeling in Digital Transformations - Enabling enterprise design dialogues
|pdfUrl=https://ceur-ws.org/Vol-3327/paper05.pdf
|volume=Vol-3327
|authors=Bas van Gils,Stijn Hoppenbrouwers,Henderik A. Proper
|dblpUrl=https://dblp.org/rec/conf/ifip8-1/GilsHP22
}}
==Conceptual Modeling in Digital Transformations - Enabling enterprise design dialogues==
https://ceur-ws.org/Vol-3327/paper05.pdf
Conceptual Modeling in Digital Transformations –
Enabling enterprise design dialogues
Bas van Gils1,2 , Stijn Hoppenbrouwers3,4 and Henderik A. Proper5,6
1
Strategy-Alliance, the Netherlands
2
Antwerp Management School, Belgium
3
HAN University of Applied Sciences, the Netherlands
4
Radboud University, the Netherlands
4
Luxembourg Institute of Science and Technology, Luxembourg
5
TU Wien, Vienna, Austria
Abstract
Conceptual models are both foundational in, and part of, the broader practice of domain modeling. In
general, conceptual models are useful in dealing head-on with many types of knowledge-intensive efforts
to describe and reason about the conceptual structures of a domain of interest.
In the context of digital transformations, conceptual models have an important role to play in
(collaboratively) capturing the understanding of relevant aspects of the existing/desired enterprises and
their context. At the same time, digital transformations also bring about many important challenges for
conceptual modeling. In this paper, we identify two related challenges in this context, concerning the
need to manage the diversity of the involved participants, as well as the need to enable the inclusion of
non-experts in the modeling activities. For each of these challenges we will also suggest directions for
future research. In doing so, we also argue that, in this context, it is important to understand conceptual
modeling as being an integral part of what we call enterprise design dialogues that occur naturally across
the enterprise.
Keywords
Conceptual Modeling, Digital Transformation, Enterprise Design Dialogues
1. Introduction
Whenever we, as humans, have a need to (jointly) reason/reflect about some part of an existing/
imagined domain, we essentially use models to express our understanding of this (part of the)
domain [1]; i.e. domain models. In line with Proper and Guizzardi [2], we consider conceptual
models to be a specific class of domain models, “where the purpose of the model is dominated
by the ambition to remain as-true-as-possible to the conceptualization of the domain by the
collective agent.” The conceptualization of the domain by the collective agent refers to the (shared)
PoEM 2022 Forum, 15th IFIP Working Conference on the Practice of Enterprise Modeling 2022 (PoEM-Forum 2022),
November 23-25, 2022, London, UK
" bas.vangils@strategy-alliance.com (B. van Gils); stijn.hoppenbrouwers@han.nl (S. Hoppenbrouwers);
e.proper@acm.org (H. A. Proper)
~ http://www.erikproper.eu (H. A. Proper)
0000-0002-1137-2999 (S. Hoppenbrouwers); 0000-0002-7318-2496 (H. A. Proper)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
understanding1 of the domain, as harbored in the mind(s) of the participants in a modeling
creation/usage process. Conceptual models can be used to explicitly capture (descriptive and/
or prescriptive) domain knowledge; they allow us to clarify and understand the things we talk
about, and reason about, at a chosen level of abstraction and from some specific perspective. In
the remainder of this paper, we will use the term conceptual modeling to refer to all activities
involved in the creation, management, and usage of conceptual models.
Over the last three decades, the use of various forms of conceptual modeling has widened due
to mutually strengthening developments. The fast growth of the Internet of Things (IoT) and
the Big Data phenomenon have made management and control of meaning in data/information/
knowledge exchange, an equally fast growing challenge. Starting from the field and practice
of database and information system design conceptual modeling has extended into internet-
related system design, prominently involving fields like information retrieval, text mining and
knowledge extraction, and in general it has become an instrument in controlling semantics in
applications of data science, machine learning, and AI [4]. In the rapidly growing application
of sub-symbolic AI and data science techniques, conceptual modeling plays a role not only
in sorting out and controlling data meaning at the source and throughout data processing
pipelines, but also in achieving required explainability of decisions made by algorithms [5],
and in blending human and machine decisions (augmented or hybrid intelligence [6]). This is
particularly true for cases where an AI system makes autonomous decisions that deeply impact
the lives of human beings – e.g. in the case of assessing insurance claims or deciding on legal
matters.
Furthermore, as a result of the increased role of data, leading to the so-called data driven enter-
prises, conceptual modeling has also found its way towards data management and governance
practices and support [7].
Finally, conceptual modeling has to some extent also integrated a number of previously rather
separate directions in semantic modeling, including ‘classic’ entity-relationship modeling, fact-
based modeling, UML-style class modeling, Semantic Web-oriented RDF and OWL modeling
(ontologies), and arguably also linguistic modeling (Wordnet, NLP resources) [4, 8].
In general, conceptual models are useful in dealing head-on with many types of knowledge-
intensive efforts to describe and reason about the conceptual structure of a domain of interest. In
this paper, we focus on the role of conceptual modeling in the context of digital transformations,
where we consider digital transformations as deliberate efforts to transform the architecture of
the organization with a major impact on its digital capabilities [9].
In the context of digital transformations, conceptual models have an important role to play
in (collaboratively) capturing the understanding of relevant aspects of the existing/desired
enterprises and their context. At the same time, digital transformations also bring about many
important challenges for conceptual modeling.
In this paper, we identify two related challenges in this regard, which center on the need to
manage the diversity of the involved stakeholders, as well as the need to enable the inclusion
of non-experts in the modeling activities. In discussing each of these challenges we will also
suggest directions for future research. In doing so, we also argue that, in this context, it is
important to understand conceptual modeling as being an integral part of what we call enterprise
1
Following the semantic triangle [3]; see Proper and Guizzardi [2, 1] for a deeper explanation.
design dialogues that occur naturally across the enterprise, and that such dialogues do not only
take place in the context of top-down driven development/transformation activities.
In line with this, the remainder of this paper is structured as follows. We start in section 2
with a discussion of the role of conceptual modeling in the context of digital transformation.
Based on this, section 3 then discusses the need to embrace and managing the diversity of the
involved participants and their domain understandings. In doing so, we will also discuss the
need to understand conceptual modeling in this context as being an integral part of what we
call enterprise design dialogues. This, then also leads to section 4, where we discuss the challenge
of inclusion of non-experts in modeling.
2. Conceptual Modeling in Digital Transformations
In the context of digital transformations, conceptual models may capture different aspects
of an enterprise; resulting in enterprise models. These enterprise models may be concerned
with the enterprise’s value proposition(s), business processes, information processing, business
rules, application landscapes, etc. These models are used widely in the context of the design,
development and interconnecting of a wide range of information systems, including digital
twins and advanced rule-based systems (for example, for Tax Law Execution, [10, 11]). As
highlighted in [12], in the context of digital transformations, conceptual/domain models can
be used towards a variety of high level purposes, including to aim to understand, to assess, to
diagnose, to design, to develop, to operate or to regulate (parts of) an enterprise.
As discussed above, conceptual models aim to remain as-true-as-possible to the original
conceptualization of the domain that is modeled. However, depending on the context, conceptual
models may be complemented with models that incorporate ‘conceptual compromises’ [2, 13]
which, for instance, enable animation, simulation, execution, gamification, or automated (logic-
based) reasoning. In [13] it is suggested to refer to these models as utility-design models, as these
models include ‘conceptual compromises’ in favor of some intended utilization of the model.
We should also beware that, in practice, the use of conceptual models often goes unnoticed
since the involved models do not always take the form of traditional ‘boxes and lines’ diagrams
or some other dedicated notation. They may also take the form of, or be embodied in, control
panels of e.g. industrial processes, (structured) text documents/wikis, or even spreadsheets.
People might not even refer to these artifacts as being/reflecting (conceptual) models; to them
they are tools to get the job done. This also resonates well with the observation of Junginger
[14]: “Naturally, [engineers] are looking for forms and practices of design they are familiar
with.”
Though the involved conceptual modeling techniques and methods and their specific uses
differ substantially, they all play a role in pragmatic activities for managing and controlling
concept semantics as part of digital transformation. Also, they are all based, to some extent,
on natural language semantics, but harnessed through more or less strict, formal or semi-
formal structures and constraints stemming from logic, set theory, knowledge engineering, and
computer science in general. In principle, this is good news for conceptual modeling and will
propel it and its use forward, even if this involves an increasing variety of forms and uses.
At the same time, from a practical point of view, we see two important, interrelated challenges
that need answering. There are many more, but these are the ones we choose to focus on in the
remainder, also in view of their direct relevance to the evolving practice of conceptual modeling.
The two challenges we focus on center on the need to manage the diversity of the involved
participants, as well as the need to enable the inclusion of non-experts in the modeling activities.
3. Embracing and Managing Diversity
Digital transformations typically involve many stakeholders [15, 16] with differing stakes and
interests, who (should) influence the direction and/or speed of the transformation. As a result,
conceptual modeling in the context of digital transformation needs to embrace this diversity of
perspectives and stakeholders. In addition, managing and embracing this diversity is paramount
because conceptual models traditionally form the base for the creation/configuration of ‘digital
actors’ (such as automated information systems).
Even more, digital transformations will not only involve top-down driven decision making
and projects. They will also involve numerous change made in the operational processes of the
enterprise, resulting in bottom-up changes as well.
As such, we take the view that the design of the structure (processes, hierarchies, (IT)
infrastructures) of an enterprise is (re)shaped by a continuous flow of (top-down and bottom-
up) enterprise design dialogues between the different involved human actors. Or in the words
of Junginger [14]: “Design literally shapes organizational reality.” This may sound abstract, but
in practice, such design dialogues occur all across enterprises. Each time co-workers discuss
‘how to’ divide work, or conduct a (new) task, they essentially engage in an enterprise design
dialogue. When process engineers discuss with senior business management how to shape a
business process, they are having a design dialogue. When database engineers discuss with
domain experts what information needs to be captured in the database, they are having a design
dialogue. When an enterprise architect interacts with different stakeholders to arrive at a new
version of the enterprise architecture, they are having a design dialogue. These examples show
how design dialogues occur across an enterprise, meanwhile (re)shaping the design of the
enterprise.
The notion of enterprise design dialogue also intends to reflect notions such as authoring
“authoring of organizations” [17], as well as views from organizational design [18]. It also
acknowledges the fact that an enterprise is certainly not a ‘machine’ (in the sense of [19] Images
of Organization) that can be ‘engineered’ as such. We do, however, assume that these dialogues
use and/or result in some artifact that represents some abstraction of some aspect(s) of the
design of the enterprise; i.e. some (conceptual) enterprise model.
Enterprise design dialogue may occur bottom-up, but they may also take place as part of
an orchestrated enterprise development/transformation process. In the latter case, one may
explicitly develop a conversation strategy [20], spanning multiple design dialogues. As suggested
in [20], the different steps (i.e. distinct design dialogues) involved in a conversation strategy
can serve more specific goals with regards to ‘enterprise knowledge’, such as: share (or create)
knowledge, agree to the shared knowledge, commit to the consequences/actions resulting from
the shared/created knowledge.
Underlying the goals to share, create, agree to and commit to knowledge, there is a direct
communicative need to ensure a shared understanding, among the different involved participants
(and their background [21, 22]). In the context of enterprise design dialogues with a high variety
of participants, these conceptual models play the role of boundary objects [23, 24], and are as
such the cornerstones for the creation of joint understanding.
As a consequence, describing and controlling the semantics (and to some extent also the
syntax) of key concepts used in conceptual models, in specific situations/contexts, while wielding
one or more specific perspectives, thus also plays an unavoidable and foundational role in digital
transformation [25]. This includes the need to achieving agreement on (if not negotiation
of [26]) concepts and models, and making them traceable (Where and who do they come from?
What are they based on? What use and perspective are they intended for?).
One of the promises of conceptual modeling that scholars and professionals frequently list
is standardization of language. This is harder than it sounds [27]. Language is very close to
the core of who we are, and often not easy to influence or change. Think of local dialects in
geographical regions and figures of speech in different organizations/communities.
Many approaches to conceptual modeling emphasize the need to ‘standardize’ but fail to
mention what the scope of standardization efforts should be. It appears that it is often assumed
that language (i.e. concepts and their definitions) should be standardized across the organization,
much to the frustration of professionals who simply “want to do their work with effective ICT
support” [28]. An exception to the ‘the whole organization’ rule can be found in domain driven
design [29]. This (design) theory is becoming increasingly popular again [30]. A core thought
behind these theories is to use conceptualization in a much smaller local ‘domain’ and worry
about the interfaces (translations) later.
In summary, in managing the diversity of the actors involved in enterprise design dialogues,
and associated modeling activities in particular, we suggest the following three main avenues
for further research:
1. Conversation strategies – The concept of conversation strategies, as coined in [20], can be
extended. Both in terms of more explicit heuristics on how to deal with diversity, as well
as also positioning bottom-up conversations to deal with bottom-up/emergent changes
in enterprises.
2. Models as boundary objects – The further elaboration of the role of models as bound-
ary objects [23, 24], in particular in terms of the understandings of the used modeling
concepts/constructs by the different actors [21, 22].
3. Negotiation and evolution of modeling concepts – Enabling more explicit negotiation [26],
and evolution [27, 28], of (domain specific) modeling concepts/constructs used to express
models, especially in the case of models that bridge between different communities and
domains [25].
4. Inclusion of Non-experts in Modeling
Due to the diversity of the actors that need to be involved in conceptual modeling in the context
of digital transformation, there is an immediate need to also engage non-experts in modeling.
Echoing our own earlier work on more natural forms of modeling [31, 32], Sandkuhl et al. [33]
also observed how modeling increasingly becomes embedded in everyday work.
In the ‘digital era’, the range of different participants involved grows even further, since
‘digital actors’ will increasingly become active participants in the modeling processes as well.
The increase of the number, and diversity, of the participants involved in conceptual modeling
causes a ‘modeling bottleneck’ in the sense that there is growing lack of modeling skills.
At the same time, we should also acknowledge that, in principle, modeling is quite natural
for humans. We started this paper with the observation that “Whenever we, as humans, have a
need to (jointly) reason/reflect about some part of an existing/imagined domain, we essentially
use models to express our understanding of this (part of the) domain”. However, even though
modeling is a natural thing for humans to do, actually doing so in terms of e.g. UML, BPMN
or ArchiMate is not so natural for most humans. In a real world context of enterprises, in
particular when dealing with ideation, re-designing, sense-making, etc., we also see many other
forms of modeling being used, including the proverbial ‘sketch on the back of a napkin’. This,
once more, stresses the point that when considering (conceptual) models, we should not be
framed by ‘boxes-and-lines’ thinking. For instance, the aforementioned strategies for more
natural modeling [31, 32] suggest to use tangible objects/shapes (spoons, sugar cubes, plates,
etc) during modeling.
It is important to note that the question of how natural modeling is for a human in general vs.
how natural it is to do so in terms of languages such as UML or ArchiMate, is largely orthogonal
to the distinction of a conceptual models and utility design models.
Underlying this, there is a trade-off related to the Return on Modeling Effort (RoME) [34]
and the value of modeling [35]. The creation, administration, and use of (conceptual) domain
models requires investments in terms of time, money, cognitive effort, learning, etc. Such
investments should be met by a (potential) return, which is ultimately rooted in the purpose of
the model [36, 37].
Depending on the purpose of a conceptual model, there may be a need to go beyond a ‘sketch
on the back of a napkin’, and e.g. produce a precise BPMN model. This need should then also
warrant the effort for the actors involved (and budget owners) to (a) understand the domain
to be modeled, (b) possibly learn the BPMN language and an associated modeling tool, and (c)
express the domain understanding in terms of a BPMN model with the chosen modeling tool.
To deal with the ‘modeling bottleneck’, and to reduce the effort (the E of RoME), we suggest
to digitally transform modeling itself [38, 39, 40]. In other words, capitalize on advances and
technologies, such as machine learning, data mining, recommender systems, chat-bots, etc, to
empower people without particular modeling background to produce higher quality domain
models. Our hypothesis is that this will create better conceptual models and that the impact
on the time it takes to create these models is reduced. Results suggest that assisted modeling
can indeed be useful, at least for novice modelers: assisted, in this context, means guiding the
modeler through a series of steps that helps reduce the cognitive load in each step. The authors
of [33] suggest to use the term assistive technologies to refer to the use of such technologies to
support (domain) modeling, resulting in the notion of assisted domain modeling [40].
A concrete example of assisted domain modeling, is the strategy as proposed in [41], which
suggest to treat (part of) the modeling task(s) as the selection of interpretation [41], starting
from unspecific concepts and relations and then step-wise interpreting them in terms of the
meta-model of the ‘target’ modeling language. In [42] an illustration of this strategy was
provided, involving a case where 3rd year bachelor students are taught ArchiMate. In doing so,
the students were asked to first create a conceptualization using sticky notes on brown paper,
then adorn these sticky notes with icons referring to the ArchiMate symbols, and then convert
this to an actual ArchiMate model using the Archi modeling tool2 .
In general, a transition to assisted (domain) modeling suggests to employ a continuum of ap-
proaches stretching from automated ‘concept mining’ in a domain to ‘assisted domain modeling’
actively involving (non-modeling-expert) domain representatives. This includes approaches
combining advanced automated techniques with assisted (guided) domain modeling by domain
representatives [40].
Making the modeling process more engaging, by means of gamification [43, 44], the use of
tangible objects in modeling activities [31, 32] and/or making use of concepts from crowdsourc-
ing [8]. Currently, a major Dutch bank is involved in experimenting with the latter. Tangible
objects also have a potential role to play here, as experience shows them e.g. to have an engaging
effect on stakeholders during decision making [45, 46].
In summary, in including non-experts in modeling, we suggest the following three main
avenues for further research:
1. Strategies for natural modeling – Making modeling more natural by e.g. allowing differ-
ent forms of model representations (beyond boxes-and-lines), including the use of the
aforementioned tangible objects [31, 32].
2. Assisted modeling – Provide more guidance for modeling tasks, essentially turning model-
ing processes into a hybrid mix of human, symbolic-AI and sub-symbolic AI [40].
3. Engaging modelers – Making the modeling process more engaging, by e.g. using gam-
ification [43, 44], making use of concepts from crowdsourcing [8], or using tangible
objects [45, 46].
5. Conclusion
This paper started with the observation that conceptual models are both foundational in, and
part of, the broader practice of domain modeling. We then zoomed in on their role in the
context of digital transformations, where they have an important role to play in (collaboratively)
capturing the understanding of relevant aspects of the existing/desired enterprises and their
context.
At the same time, digital transformations also bring about many important challenges for
conceptual modeling. In this paper, we highlighted two related challenges for conceptual
modeling in the context of digital transformations: the need to manage the diversity of the
involved participants, as well as the need to enable the inclusion of non-experts in the modeling
activities. For each of these challenges, we explored some of the background, while also listing
the main avenues for future research to meet these challenges. Important in this is to understand
the conceptual modeling activities in this context as being an integral part of what we call
enterprise design dialogues, and that such dialogues do not only take place in the context of
top-down driven development activities.
2
In our current teaching practices, we actually use Miro for the initial steps
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