Boost the Potential of EA: Essential Practices
Hong Guo
1,2 a
, Jingyue Li
, Shang Gao
and Darja Smite
2,4 d
School of Business, Anhui University, No. 111 Jiulong Road, Hefei, People's Republic of China
Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
School of Business, Örebro University, Örebro, Sweden
Department of Software Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
Keywords: Enterprise Architecture, Enterprise Architecture Tools, Enterprise Architecture Management, Meta-model,
EA Artifacts.
Abstract: Enterprise Architecture (EA) has been applied widely in industry as it brings important benefits to ease
communication and improve business-IT alignment. However, various challenges were also reported due to
the difficulty and complexity of applying it. Some empirical studies showed that EA stilled played a limited
role in many organizations. In this research, we showed other findings where the potential of EA could be
better used. They are derived from our analysis of advanced EA tool recommendations. Based on these
findings, we proposed four essential EA practices and the rationales behind them in order to improve the
understanding of current practices and bring insights for future studies to boost the potential of EA.
Enterprise Architecture (EA), defined as
“fundamental concepts or properties of an enterprise
in its environment and governing principles for the
realization and evolution of this entity and its related
life cycle processes” (ISO/IEC/IEEE, 2019), has been
applied widely in industry to address communication
issues and align business and IT. Despite the high
expectations and some reported benefits (Korhonen,
Lapalme, McDavid, & Gill, 2016; Winter, Buckl,
Matthes, & Schweda, 2010), challenges often arise
that block the efficient application of EA (Engelsman
& Wieringa, 2012; Isomäki & Liimatainen, 2008;
Olsen & Trelsgård, 2016). As a result, in many
organizations, EA still plays a limited role (Guo, Li,
& Gao, 2019; Kotusev, 2019). But what is the reason
for that? To the best of our knowledge, the root causes
are still unclear, and there is no general agreement
about the best practices to improve the EA application
The objective of this study is to enhance the
understanding of how to boost the potential of EA in
practice. In this paper, we present our reflections on
how EA could be used (and might be already used) in
organizations in a more efficient manner. We
analysed the differences between the tool vendor
guidelines and the state of the practice of applying EA
based on a comprehensive study of 27 organizations
as reported by (Kotusev, 2019). Based on the data
analysis results, we proposed four essential practices
to raise the potential of EA accordingly. These
practices are: “use EA in a business outcome-driven
way,” “develop EA gradually when using it,”
“maintain a complete digital EA repository,” and
“base EA on an integrated meta-model.”
The rest of this article is structured as follows.
Section 2 introduces relevant background
information. Section 3 briefly introduces the method
and sources where we collected evidence. In Section
4, we present our data analysis and results. We then
discuss how to boost the potential of EA in Section 5.
And in Section 6, we put forward and motivate the
essential practices identified. Lastly, Section 7
discusses the limitations of this research, points out
future directions, and concludes this paper.
Guo, H., Li, J., Gao, S. and Smite, D.
Boost the Potential of EA: Essential Practices.
DOI: 10.5220/0010473007350742
In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 735-742
ISBN: 978-989-758-509-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
2.1 EA Basics
EA is often referred to as a blueprint for enterprise
composition and enterprise operating systems.
Despite many kinds of benefits EA might bring
(Winter et al., 2010), one main role of EA is to
provide the service of understanding and
communicating enterprise interaction patterns
through abstract and graphical expressions, and to
facilitate the alignment of business and information
systems (Korhonen et al., 2016).
EA usually exists in the form of a set of abstract
graphics which cover the high-level content of the
enterprise across areas such as strategy, business,
information, and technology. We call these
abstractions EA artefacts, EA documents (usually
in more textual form), or EA models (usually in
graphical form).
EA is traditionally and usually developed based
on one or more EA Frameworks (EAFs). These
EAFs provide a common foundation for EA
practitioners. For example, TOGAF (The Open
Group, 2020) is maintained by a standardization
organization (The Open Group) and is one of the most
widely used EAFs. An EAF usually consists of two
parts. One part is a content framework, which mainly
describes what concepts should be included and what
are the relationships among them. The other part is a
development method, which provides guidelines for
developing related EA documents.
For a content framework, a meta-model is often
used to accurately define (about both syntax and
semantics) the concepts as well as the relationships
between them. Since the basic form of EA is usually
a set of graphical models, content frameworks are
also often associated with a set of graphical notations.
For example, the ArchiMate standard that is hosted
by the Open Group includes a set of symbols that are
fully compatible with the TOGAF meta-model. The
meta-model is usually one of the most important
components of an EAF.
EA tools are defined as “software applications
designed to support enterprise architects and other
business and IT stakeholders with strategically driven
planning, analysis, design, and execution (Gartner,
2021).” EA tools should be selected to keep
compatible with enterprises’ approaches to
transformation, modernization, and innovation to
avoid failure of such efforts. EA tools store, structure,
analyse, and present EA information to aid in the
investment, development, and delivery of IT
solutions that enable business success. EA captures
and connects context information across business,
information, solution, and technology domains to
support strategic and tactical decision making. EA
tools also help with planning and executing a business
strategy and focus on diagnostic, actionable,
operational, and enabling deliverables (Gartner,
2.2 EA Benefits and Challenges
EA is often mentioned as a means to provide a holistic
view of an enterprise. Such a holistic view is
elaborated as a coherent whole of principles,
methods, and models that are used in the design and
realization of an enterprise’s organizational
structure, business processes, information systems,
and infrastructure(Lankhorst, 2009). The main idea
here is that EA captures the essentials of an
enterprise. While essentials are thought to be more
stable than specific solutions for currently at-hand
problems, EA is therefore regarded as helpful to
guarding the essentials of the business while keeping
maximal flexibility and adaptivity. Furthermore, it
was thought that without good architecture, it is
difficult to achieve business success (Lankhorst,
Despite the high expectation for EA becoming
the determining factor that separates the winners
from the losers (Zachman, 1997), over the last
twenty years and through many successful examples
(Zachman, 1997), EA application has also met with
numerous challenges (Engelsman & Wieringa, 2012;
Isomäki & Liimatainen, 2008; Olsen & Trelsgård,
2016). In one of the latest empirical research studies
(Kotusev, 2019), representatives from 27 diverse (in
size, industry, and EA experience) organizations were
interviewed about how they have been using EA
(artefacts). Results from (Kotusev, 2019) indicated
that EA in general still played a limited role in many
organizations. The findings suggested that some
important EA documents such as roadmaps were
empirically invalid, and that overall EA was discrete
instead of coherent.
To the best of our knowledge, there is no clear
answer as to why there is such a difference in the
perceived usefulness of EA for organizations and the
limited usage of EA in industry. This inspired the
present research. Our goal is to investigate what
caused insufficient exploration and usage of EA as
reported by (Kotusev, 2019). We also want to identify
essential practices in order to boost more potentials of
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With the aim to shed some light on ways of better
adopting EA, we studied how methods and tools are
used to explore EA potentials for organizational
performance from the tool vendor perspective.
To do so, we have collected evidence from
publicly available content on official EA tool vendor
websites and websites providing third-party reviews
of the EA tools. We primarily used the grey
literature review approach (Garousi, Felderer, &
Mäntylä, 2016) to collect and analyse the data. Grey
literature reviews have been acknowledged as a valid
alternative to academic literature reviews when the
state of the practice is concerned, as they can give
substantial benefits (Garousi et al., 2016).
The evidence mainly comes from the website
contents of 16 leading EA tools. There are three
reasons for us to review such website contents. First,
tools are both instrumental and very important in EA
discipline (Korhonen et al., 2016). Second, tools in
general make it easier for users to accept one
technology. For EA, user acceptance was perceived
as one of the critical challenges. Thus, we assume that
tool support could facilitate EA application. Third,
according to our preliminary observation, the content
offered in the tool vendor websites is rich and
informative. Many white papers, use cases, and
feature descriptions were provided on the vendors’
websites to provide knowledge to their potential
customers and show vendors’ expertise.
We collected data primarily from 16 websites of
EA tool vendors. The vendors were selected from the
list of vendors administered in Gartner’s (Forbes
Media LLC., 2021) annual report named Gartner
Magic Quadrant for Enterprise Architecture Tools
(Gartner, 2020), which includes long-established
manufacturers as well as insightful new challengers.
We believe that how these leading vendors apply EA
represents the current trend of first-line EA
applications. To complement the opinion and
information declaimed by the vendors themselves, we
have also referred to user reviews available in
(Gartner, 2021). The user reviews were verified as
explained by (I. Gartner, 2020) to ensure their quality
and reliability according to some criteria, such as not
containing plagiarized content and highlighting
experiences related to vendors/products. Some
reflections were also triangulated with the analysis of
user reviews in IT Central Station (IT Central Station,
2020), which unfortunately have not been explicitly
presented in this paper due to the space limitations.
Our data analysis aimed to compare the state of
the art as reported in a recent comprehensive study of
organizations applying EA (Kotusev, 2019) (further
referred to as results-of-survey-study”), with the
recommendations suggested by the tool vendors
(further referred to as vendor recommendations”).
We used (Kotusev, 2019) as a representative of the
state of the practice because it proposed clear
statements about the comprehensive EA application
which makes it easier for us to make the comparison.
Notably, it was not easy to compare evidence
extracted from the empirical study and the tool
vendor websites, as the concepts and structures often
differed. In fact, terminology misalignment in
scientific papers is a known issue (Korhonen et al.,
2016). In order to compare and map different aspects
of EA implementation that differ, we focused on four
essential aspects of EA application: how to use, how
to create, how to organize, and how to regulate EA
artefacts. Our analysis started by reading through the
contents of the websites and gaining an initial
understanding of the overall breadth and depth of the
information and supporting evidence. Next, we
extracted evidence relevant to the four chosen aspects
of EA application. As similar evidence was presented
on multiple websites and for multiple products, we
chose the most representative formulations (clear and
complete statements). As a result, evidence presented
in this paper mainly came from the websites of six
vendors: Avolution (Avolution, 2021a), Sparx (Sparx
Systems Pty Ltd., 2021), Ardoq (Ardoq AS., 2021),
ValueBlue (ValueBlue B.V., 2021), Mega (MEGA
International, 2021), LeanIX (LeanIX, 2021).
In this section, we present four aspects of how to use,
create, organize, and regulate EA as critically
evaluated in a recent empirical study (Kotusev, 2019)
presented as results-of-survey-study versus as
suggested by the tool vendors accomplished with
some user reviews as vendor recommendations.”
Reflectionsare derived based on the analysis of the
differences between the extracted evidences.
The four reflections related to EA are summarized
Roadmap (EA usage): empirically invalid versus
feasible and useful.
EA (organizations): not a single description for all
stakeholders versus a single, comprehensive, and
valuable repository.
EAFs/meta-models (EA regulations): purely
declarative versus fundamental.
Boost the Potential of EA: Essential Practices
EA creation/development: for specific purposes
versus for specific business output with limited
extra costs.
4.1 Roadmap: Empirically Invalid
vs. Feasible and Useful
Results-of-survey-study: According to (Kotusev,
2019), the conceptualization of EA as the current
state, future state and transition roadmap is
empirically invalid.” The author presented two main
reasons for this. First, many useful EA artefacts do
not distinguish current and future states. Second, none
of the organizations had comprehensive descriptions
of their current and future states.
Vendor Recommendations: Leading vendors such
as Avolution (Avolution, 2021a) promote a roadmap
as one of the key features of their EA tool products.
The roadmap can be based on state gaps and is
recognized by some verified users according to
(Gartner, 2021). The summary of the evidence is
shown in Table 1.
Reflection 1 (R1): Roadmap based on gap analysis
might be feasible and useful.
Table 1: Evidence to R1.
Avolution promotes roadmap as one key
Might be
based on
Avolution: “The architectures under
consideration will include a current state
plus at least one ‘target’ or ‘future state’
the value of
User reviews to Abakus (Avolution’s EA
tool product):
Among two out of eleven verified
reviews, “product roadmap and future
vision” was enumerated as one of “the
key factors that drove your decision.”
In another verified review, it was
commented that “(Abakus) helps to
create the entire roadmap” in overall
4.2 EA: Not a Single Description for
All Stakeholders vs. a Single,
Comprehensive, and Valuable
Results-of-survey-study: “EA is a complex set of
very diverse descriptions intended for different
decision makers and purposes rather than a single
comprehensive description that is developed and then
used by all stakeholders” (Kotusev, 2019).
Vendor Recommendations: Several vendors such as
Sparx, Avolution, and Ardoq advocate that their
products provide a complete repository and name it
as a key feature of “a single source of truth.” The
vendors also highlight the value of this feature and
think it is essential for data integration, providing a
holistic view and solving complex decision-making
problems. This evidence is presented in Table 2.
Reflection 2 (R2): EA as a single comprehensive
repository is very valuable.
Table 2: Evidence to R2.
Reflections Evidence
having a
Sparx: “A single source of truth.”
Avolution: “A single source of truth.”
Ardoq: “A complete repository for all
highlight the
value of
having a
Sparx: (A single source of truth/
repository) solves the problem of
managing networks of decisions.
Avolution: “The value is in being able
to wire our data together using
integrations” and “create a single
source of truth, pulling in your
chosen master data.”
Ardoq: “Understand your integration
architecture from a holistic point of
view to better manage life cycles,
outages, and the impact of change.”
4.3 EAFs/Meta-Models: Purely
Declarative Vs. Fundamental
Results-of-survey-study: The use of EA
frameworks is purely declarative and does not define
resulting EA practices in any real sense(Kotusev,
2019). Notably, the term meta-model” is not
mentioned in the study (Kotusev, 2019), so we
assume that meta-models are either overlooked or
used interchangeably with EAFs.
Vendor Recommendations: Eight out of 16 vendors
claim their support for more than one standard EAF
such as TOGAF and ArchiMate in prominent
positions on their websites. The top three vendors
even support a big number of EAFs. One vendor
reveals how EAFs relate to their own meta-models
and notations. The evidence is presented in Table 3.
Half of the leading EA tool vendors (eight out of
16) clearly advocate that their tools are
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compatible with at least one notable EAF such as
TOGAF and ArchiMate in prominent positions
on their official websites. These tool vendors are
Avolution, BiZZdesign, MEGA International,
QualiWare, UNICOM Systems, Sparx Systems
Pty Ltd., BOC Products & Services AG., and
The top three vendors (Avolution, BiZZdesign,
and MEGA) are keen to widely support industry
EAFs. For instance, Avolution supports over 100
frameworks. Such EAFs include high-level and
low-level ones. High-level ones are referred to
typical EAFs such as TOGAF which covers full
domains for organizations. Lower-level ones are
referred to more local frameworks such as
BPMN, which covers more specific domains for
organizations (Avolution, 2021b).
Avolution also explains the mechanism about
how they use EAFs/meta-models. The central
idea is to use one single meta model to decouple
underlying frameworks/meta-models and upper-
level notations. Therefore, they can benefit from
both standard EAF/notation compliance and one
single inventory (structured by the meta-model).
This principle seems to also explain why
ValueBlue, which supports one EAF only,
namely ArchiMate, provides multiple formats to
visualize the data, while Erwin, which is a
leading meta-data management vendor, does not
advocate their support to any standard EAFs.
Table 3: Evidence to R3.
their support
to standard
Overall: 8/16 leading vendors support at
least one standard EAF.
Avolution: “Ships with over 100 industry
leading frameworks, metamodels and
recognize the
value of
“Selecting a framework is often one of
the first steps to delivering enterprise
architecture success,” “provide a set of
assets and templates which allow
architects to get started quickly,” “set
best-practice and standards for
governance,” “helpful for collaboration
and communication between architects.”
explain the
EAFs, meta-
models, and
“Practitioners can configure, adapt or
combine frameworks and metamodels.”
ValueBlue: “Models can be presented in
a variety of ways, but under the hood it
is a consistent model within
ArchiMate® 3.1.”
Ardoq: “20+ out of box visualizations.”
It turns out that the majority of the reviewed EA tools
is employing such an integrated meta-model for
integrating EA data. On one hand, such meta-models
can be created in-house based on one standard EAF
or by integrating meta-models from multiple standard
EAFs. On the other hand, one or multiple
notations/formats/visualizations can be supported to
visualize the data. Such notations may or may not
come from one or multiple standard EAFs also.
Therefore, we think the use of EAFs/meta-models
provides a fundamental function (a rigorous
definition for EA data structure).
Reflection 3 (R3): The use of EAFs including meta-
models, is fundamental to EA application.
4.4 EA Creation: For Specific Purposes
vs. for Specific Business Output
with Limited Extra Cost
Results-of-survey-study: No EA artefacts are
created merely for the sake of having some
descriptions” (Kotusev, 2019). “All EA artefacts are
created for specific purposes, rather than simply to
describe some aspects of organizations” (Kotusev,
Vendor Recommendations: Many vendors such as
Avolution and Mega (MEGA International, 2021)
advocate for their solutions to support “outcome-
driven EA” and be “out-of-the-box.” This indicates
that EA is developed/used when a specific purpose
arises. It also means limited cost is incurred ahead of
actual EA usage. The evidence is summarized in
Table 4.
EA can be used/developed for a specific outcome
with limited additional costs. This reveals
organizations' eternal expectations of Return on
Investment (ROI) for various tasks including EA
development/usage. Traditional framework-based
usage of EA usually involves a big amount of
development work ahead, and it is difficult to
evaluate the benefits and costs of using EA.
This might explain why organizations reviewed in
(Kotusev, 2019) discarded/simplified complex cases
such as roadmaps (many EA artefacts needed are not
available and huge and/or unpredictable workloads
are required). In the picture described by vendors, it
is claimed that users are only suggested to use EA for
specific purposes, and they do not have to spend
much extra effort ahead of that. Considering how
EAF/meta-models and EA inventories are generally
used as previously presented, we assume that EA
Boost the Potential of EA: Essential Practices
inventory is gradually accumulated while being used
on purpose.
Reflection 4 (R4): Creating EA for specific business
outputs and accumulatively might present appealing
Table 4: Evidence to R4.
Reflections Evidence
advocate the
purposeful use
of EA.
Avolution: “Business-Outcome
Driven Enterprise Architecture
Mega: “Business-outcome-driven
Enterprise Architecture,” “outcome-
driven approach,” “based on value-
added use cases”
LeanIX: “Outcome-driven approach”
advocate the
box” feature.
Ardoq: “Out-of-the-box integrations
with leading tools,” “20+ out of box
LeanIX: “30 Minutes to Lift Off”
explain the
expectation of
ROI behind.
Mega: “Achieve faster time-to-value
and generate demonstrable ROI
through an outcome-driven
We intended to collect and compare four pairs of facts
described in (Kotusev, 2019) and “vendor
recommendations” above. Subjects in the four
aspects, namely the EA use case (R1), EA repository
(R2), EA frameworks (R3), and expectations on EA
costs (R4), are related. First, the premise of
maintaining a complete EA repository is to use a
common meta-model. Second, the roadmap is not a
simple document describing a certain aspect of an
enterprise, but a complex use case based on the
understanding of multiple aspects of the enterprise.
Effective support for such complex cases therefore
requires multiple related EA documents. Third, the
purposeful use of EA represents a rigid requirement
or restriction in practical environments, which means
that the development of EA must demonstrate
acceptable ROI, although this ROI may be perceptual
and qualitative rather than rational and quantitative.
We further analyse where EA is applied in
(Kotusev, 2019) and “vendor recommendations.”
According to the original claims in (Kotusev, 2019),
in these involved organizations, there is no complete
and unified EA repository. EA is simply a collection
of discrete artefacts. These artefacts are not well
structured and digitized. Therefore, it is impossible to
accumulate and reuse artefacts that were developed
by different people at different times. The benefits of
EA using a certain framework are limited to the
conceptual unification of these artefacts. Some simple
use cases can be satisfied by constructing specified
artefacts, while complicated cases are difficult to be
satisfied due to the consideration of cost performance.
Figure 1: Two situations where EA potentials are applied
While based on our analysis of the vision
described by leading tool vendors, EA is constructed
and maintained as a complete digital repository, this
repository is well structured based on a common
meta-model. Therefore, benefiting from reusing the
data accumulated in the previous simple case
application, even if it is necessary to provide ideal
cost performance, complex cases may still be
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As shown in Figure 1, appropriate ROIs are
required/expected in both situations. From the vendor
perspectives, with the support of a digital and
comprehensive inventory (structured with a common
meta-model), complex use cases are still possible to
be satisfied. But in (Kotusev, 2019), instead of one
comprehensive inventory, there is only a complex
sets of EA artefacts. Only simple use cases can be
satisfied under significant pressure of expected ROI,
therefore. Thus, (Kotusev, 2019) and vendors show
different exploitation of EA potentials. Here, EA
potential has at least two meanings. One is the
satisfaction of complex use cases. The other is to
extract more value from data in digital EA inventory
such as automated data capturing, data integration,
data analysis, and data visualisation.
Based on the above discussion, we can see that in
order to boost the potential of EA, the key is to
maintain a unified digital EA inventory. Such an
inventory needs to be based on a common meta-
model (i.e., by integrating multiple existing EAFs). If
EA is always used for a specific purpose, and at the
same time is gradually accumulated in the inventory,
it is promising to demonstrate a satisfactory or at least
acceptable cost performance. These essential
practices were summarized in Table 5.
In Table 5, we also listed the main rationale.
While essential EA practicesmainly address the
technical part of how to develop and use EA, the
rationale behindmainly addresses the motivations
from EA users’ perspectives. It should be noted that
these practices are not simply juxtaposed, but there
are dependency relationships among levels from top
(P1) to bottom (P4).
Table 5: Essential practices and possible rationale behind
boosting the potential of EA.
Essential EA Practice Rationale Behind
P1: Use EA in a business
outcome-driven way.
To generate
demonstrable ROI.
P2: Develop EA gradually
when using it.
To minimize
unnecessary costs.
P3: Maintain a single digital
EA repository.
To benefit from the
single source of truth.
P4: Base EA on an integrated
To normalize a
common vocabulary.
These practices seem to be in line with spirits
advocated by modern tool vendors. For instance,
LeanIX (LeanIX, 2021), the vendor which tops the
rank according to (Gartner, 2021), proposes five
guidelines to satisfy all stakeholders and therefore
continuously explore the most EA value. First, the
language should be easy to understand. Technical
jargon should be avoided, and important information
should be conveyed. Second, the data should be
available to everyone at any time. Third, the quality
of the data should be maintained actively so that
reliable information can be used for decision making.
Fourth, useless models should be avoided, and
practical benefits should be pursued when solving
real problems. Fifth, it is recommended to focus on a
few areas and use cases so that repeatable success can
be proven. Then, such processes could be
incorporated gradually, and more opportunities and
disruptions can be addressed. Among these five
guidelines, we can distinguish that the first is about
meta models. The second and third is about EA data.
The fourth and fifth are about how to use EA data and
gradually develop/accumulate it.
In this research, we present our reflections on the
comparison of EA applications reported in a recent
comprehensive empirical study (Kotusev, 2019) and
advanced EA vendors’ recommendations and their
users’ reviews. As is evident in our results, several
aspects of EA application differed in the evidence
obtained from the two sources. Based on our results,
we put forward suggestions on how to boost EA
potential the most. One thing to notice is that although
we derived essential EA practices by extracting
behaviour traits from leading EA tools, the resulting
recommendation is not to promote the simple use of
such tools, but to learn from their practices.
One limitation of the present research is that most
evidence comes from the description of vendors. We
compensate for this by reviewing some verified user
comments. We plan to use the tools in real scenarios
ourselves and follow other tool users through
interviews or surveys to further validate and enhance
our proposal.
With proposed practices and the rationale, we
expect that more techniques and methods can be
aligned in this strategically important area effectively.
By doing so, more potential of EA could be employed
in order to address critical issues such as lack of
communication and misalignment between business
and IT in a more reliable way.
Boost the Potential of EA: Essential Practices
This research is financially supported by the
European Research Consortium for Informatics and
Mathematics (ERCIM) ( This
work has been partially supported by NFR 295920
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