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.

1 INTRODUCTION

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

yet.

The objective of this study is to enhance the

understanding of how to boost the potential of EA in

https://orcid.org/0000-0003-3608-0981

https://orcid.org/0000-0002-7958-391X

https://orcid.org/0000-0002-3722-6797

https://orcid.org/0000-0003-1744-3118

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

735

2 BACKGROUND

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,

2021).

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,

2009).

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

EA.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

736

3 DATA COLLECTION AND

ANALYSIS METHOD

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).

4 DATA ANALYSIS RESULTS

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.”

“Reflections” are derived based on the analysis of the

differences between the extracted evidences.

The four reflections related to EA are summarized

as:

• 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

737

• 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.

Reflections

Evidence

Vendors

provide

roadmap.

Avolution promotes roadmap as one key

feature.

Might be

based on

gap

analysis.

Avolution: “The architectures under

consideration will include a current state

plus at least one ‘target’ or ‘future state’

architecture.”

Users

recognize

the value of

roadmap.

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

comment.

4.2 EA: Not a Single Description for

All Stakeholders vs. a Single,

Comprehensive, and Valuable

Repository

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

Vendors

advocate

having a

single

repository.

Sparx: “A single source of truth.”

Avolution: “A single source of truth.”

Ardoq: “A complete repository for all

integrations.”

Vendors

highlight the

value of

having a

single

comprehensive

repository.

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

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

738

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

ValueBlue.

• 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.

Reflections

Evidence

Vendors

advocate

their support

to standard

EAFs.

Overall: 8/16 leading vendors support at

least one standard EAF.

Avolution: “Ships with over 100 industry

leading frameworks, metamodels and

notations.”

Vendors

recognize the

value of

EAFs.

Avolution:

“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.”

Vendors

explain the

relations

among

EAFs, meta-

models, and

notations.

Avolution:

“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,

2019).

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

739

inventory is gradually accumulated while being used

on purpose.

Reflection 4 (R4): Creating EA for specific business

outputs and accumulatively might present appealing

ROI.

Table 4: Evidence to R4.

Reflections Evidence

Vendors

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”

Vendors

advocate the

“out-of-the-

box” feature.

Ardoq: “Out-of-the-box integrations

with leading tools,” “20+ out of box

visualizations”

LeanIX: “30 Minutes to Lift Off”

Vendors

explain the

expectation of

ROI behind.

Mega: “Achieve faster time-to-value

and generate demonstrable ROI

through an outcome-driven

approach.”

5 DISCUSSIONS

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

differently.

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

supported.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

740

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.

6 ESSENTIAL PRACTICES TO

BOOST THE POTENTIAL OF

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 practices” mainly address the

technical part of how to develop and use EA, the

“rationale behind” mainly 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

meta-model.

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.

7 CONCLUSIONS

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

741

ACKNOWLEDGEMENTS

This research is financially supported by the

European Research Consortium for Informatics and

Mathematics (ERCIM) (https://www.ercim.eu/). This

work has been partially supported by NFR 295920

IDUN.

REFERENCES

Ardoq AS. (2021). Ardoq. Retrieved from

https://www.ardoq.com/

Avolution. (2021a). Avolution. Retrieved from

https://www.avolutionsoftware.com/

Avolution. (2021b). Discover some of the leading EA

frameworks ABACUS supports. Retrieved from https://

www.avolutionsoftware.com/abacus/frameworks/

Engelsman, W., & Wieringa, R. (2012). Goal-oriented

requirements engineering and enterprise architecture:

Two case studies and some lessons learned. Paper

presented at the International Working Conference on

Requirements Engineering: Foundation for Software

Quality.

Forbes Media LLC. (2021). Gartner (IT). Retrieved from

https://www.forbes.com/companies/gartner/

Garousi, V., Felderer, M., & Mäntylä, M. V. (2016). The

need for multivocal literature reviews in software

engineering: complementing systematic literature

reviews with grey literature. Paper presented at the

Proceedings of the 20th international conference on

evaluation and assessment in software engineering.

Gartner. (2020). Gartner Magic Quadrant for Enterprise

Architecture Tools. Retrieved from

https://www.gartner.com/en/documents/3970555/magi

c-quadrant-for-enterprise-architecture-tools

Gartner. (2021). Enterprise Architecture (EA) Tools

Reviews and Ratings. Retrieved from

https://www.gartner.com/reviews/market/enterprise-

architecture-tools

Gartner, I. (2020). User FAQ. Retrieved from

https://www.gartner.com/reviews/faq#q4

Guo, H., Li, J., & Gao, S. (2019). Understanding challenges

of applying enterprise architecture in public sectors: A

technology acceptance perspective. Paper presented at

the 2019 IEEE 23rd International Enterprise

Distributed Object Computing Workshop (EDOCW).

ISO/IEC/IEEE. (2019). ISO/IEC/IEEE 42020:2019

Software, systems and enterprise — Architecture

processes. In: ISO.

Isomäki, H., & Liimatainen, K. (2008). Challenges of

government enterprise architecture work–

stakeholders’ views. Paper presented at the

International Conference on Electronic Government.

IT Central Station. (2020). Most Popular Products and

Services. Retrieved from https://www.itcentral

station.com/products

Korhonen, J. J., Lapalme, J., McDavid, D., & Gill, A. Q.

(2016). Adaptive enterprise architecture for the future:

Towards a reconceptualization of EA. Paper presented

at the 2016 IEEE 18th Conference on Business

Informatics (CBI).

Kotusev, S. (2019). Enterprise architecture and enterprise

architecture artifacts: Questioning the old concept in

light of new findings. Journal of Information

Technology, 34(2), 102-128.

Lankhorst, M. (2009). Enterprise architecture at work

(Vol. 352): Springer.

LeanIX. (2021). LeanIX. Retrieved from

https://www.leanix.net/en/

MEGA International. (2021). MEGA. Retrieved from

https://www.mega.com/

Olsen, D. H., & Trelsgård, K. (2016). Enterprise

Architecture adoption challenges: An exploratory case

study of the Norwegian higher education sector.

Procedia Computer Science, 100

, 804-811.

Sparx Systems Pty Ltd. (2021). Sparx Systems. Retrieved

from https://sparxsystems.com/

The Open Group. (2020). The TOGAF® Standard. In.

ValueBlue B.V. (2021). ValueBlue. Retrieved from

https://valueblue.com/

Winter, K., Buckl, S., Matthes, F., & Schweda, C. M.

(2010). Investigating the State-of-the-Art in Enterprise

Architecture Management Methods in literature and

Practice. MCIS, 90.

Zachman, J. A. (1997). Enterprise architecture: The issue of

the century. Database Programming and Design, 10(3),

44-53.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

742