=Paper=
{{Paper
|id=Vol-1859/emisa-05-paper
|storemode=property
|title=Misalignment Symptom Detection with XML-based Enterprise Architecture Model Analysis
|pdfUrl=https://ceur-ws.org/Vol-1859/emisa-05-paper.pdf
|volume=Vol-1859
|authors=Dóra Őri
|dblpUrl=https://dblp.org/rec/conf/emisa/Ori17
}}
==Misalignment Symptom Detection with XML-based Enterprise Architecture Model Analysis==
https://ceur-ws.org/Vol-1859/emisa-05-paper.pdf
153
Misalignment Symptom Detection with XML-based
Enterprise Architecture Model Analysis
Dóra Őri1
Abstract: This novel directions talk deals with the concept of misalignment, with special attention
on enterprise architecture (EA)-based analytical potential. In the following study, the problem of
business-IT alignment will be translated into the aspects and concepts of enterprise architecture.
The main purpose of the proposed research is to analyse strategic misalignment between the
business dimension and the information systems dimension. The contribution of the novel
directions talk is to connect typical misalignment symptoms to relevant EA analysis types. The
significance of the proposed research is the clear and accurate compound of research methods and
implementation instruments to approach EA-based misalignment symptom detection. The results
of the proposed research will contribute to alignment assessment by expanding the ways of
addressing alignment problems. Specifically, it contributes to analysing the state of misalignment
in a complex EA model structure.
Keywords: Strategic Alignment Perspectives, Enterprise Architecture Alignment, Misalignment
Symptoms, Enterprise Architecture Analysis.
1 Introduction
Several difficulties (the misalignments) encumber the achievement of strategic
alignment. Misalignment assessment means a momentous step in achieving strategic
alignment since it helps to reveal the barriers of alignment. Understanding the
underlying causes of misalignments, as well as trying to correct the existing
misalignments are one of the possible ways to achieve alignment [CS08]. The majority
of alignment studies deal with achieving alignment. On the contrary, misalignment
assessment (detecting, analyzing, correcting and preventing misalignment) is
considerably underemphasised in existing works. The state of (mis)alignment can be
examined with several methods. Most of the methodologies approach (mis)alignment
from management, organisational culture, and communication perspectives. In contrast
to popular approaches, one of the main research methods for (mis)alignment evaluation
is enterprise architecture-based assessment. This study deals with the concept of
misalignment, with special attention to enterprise architecture (EA)-based analytical
potential. In the following study, the problem of business-IT alignment will be translated
into the aspects and concepts of enterprise architecture. The main purpose of the
proposed research is to analyse strategic misalignment between the business dimension
and the information systems dimension.
1
Corvinus University of Budapest, Department of Information Systems, Fővám tér 13-15, H-1093 Budapest,
DOri@informatika.uni-corvinus.hu
154 Dóra Őri
The proposed research relates to the concept of strategic alignment. This research aims
to approach strategic alignment from the perspective of misalignment. In this research,
the problem of revealing the typical symptoms of misalignment will be addressed in
order to assess the state of alignment in an organisation. The research builds on the
traditional Strategic Alignment Model (SAM) by [HV93]. The research aims to provide
suitable tools and instruments to detect the symptoms of misalignment [CS08].
Misalignment assessment will be based on the analysis of the underlying enterprise
architecture models [Za87]. EA-based analysis is based on rule generation and testing.
2 Research Foundation
The study discusses the strategic misalignment between the business dimension and the
information systems dimension. The aim of the study is to contribute to the above-
mentioned concerns and gaps by introducing a novel direction that addresses these
issues. Expected outcomes from the proposed research include: 1) Classification of
different misalignment symptoms: EA indicators on misalignment, EA detection
techniques. 2) A framework which can support EA-based misalignment assessment. 3)
Case studies on the operation and correctness of the framework. The main research
objective lies in identifying general ways for detecting the symptoms of misalignment in
the underlying EA models. The sub-objectives of the above-introduced research
objective consist in the breakdown of the main research objective into smaller, logically
connected parts, viz.: 1) What are the typical symptoms of misalignment according to
the operation of the SAM model? 2) How to transform misalignment symptoms into
formally analysable statements? 3) What are the formal analysis methods of detecting
misalignment symptoms in EA models? Based on expected outcomes and research
objectives, the proposed research focuses on the following research questions: 1) Which
misalignment symptoms can be detected via EA assessment? 2) Which dimensions and
domains are needed to examine in an EA model to detect misalignment symptoms? 3)
How do EA models manifest different misalignment symptoms? 4) With which methods
can we explore the different misalignment symptoms in EA models?
The proposed research aims to address the above-introduced research objectives and
research questions by introducing a novel direction for EA-based misalignment symptom
analysis. Figure 1 introduces the conceptual research model of the study. The proposed
direction introduces an approach for EA-based alignment assessment, i.e. a solution for
assessing alignment phenomenon in EA models.
Misalignment Symptom Detection 155
Fig. 1: Conceptual Research Model
3 Proposed Research Methodology
The research shall take a rule-based approach to reveal the symptoms of malfunctioning
alignment areas. The research steps shall be aggregated into three layers: 1)
Misalignment Layer, 2) Enterprise Architecture Model Layer and 3) Analysis Layer.
Misalignment Layer is concerned with the construction and formal description of
misalignment symptoms. Misalignment symptom construction is based on the matching
of the SAM alignment domains. A formal description of misalignment symptoms
consists of pattern generation. EA Model Layer aims at preparing the underlying
enterprise architecture models for the misalignment symptom detection. The phase
consists of model transformation, artefact decomposition, and export file generation.
Analysis Layer is concerned with the implementation details of the proposed research.
EA-based misalignment symptom detection shall be performed by means of formal rule
testing, i.e. the analytical potential of rule generation and rule testing shall be exploited.
Misalignment symptoms shall be defined as formal rules. After rule construction, rule-
testing approaches shall be introduced. Implementation details include the following:
Queries for EA-based misalignment symptom detection shall be written by in XPath
language and Schematron language. Schematron language shall be used for reporting
assertions about patterns (i.e. misalignment symptoms) found in the XML exports of the
EA models under review. XPath language serves as a supportive language for defining
the context of the queries. Schematron-based queries shall be written and validated in an
XML validation tool. The tool includes an editor for composing Schematron queries and
an inbuilt validator engine for validating XML-based models against Schematron
queries.
156 Dóra Őri
4 Challenges and Concerns of EA-based Misalignment Assessment
Misalignment symptom analysis and detection provides insights about query types. The
proposed research direction shall be applicable for detecting the following types of
misalignment symptoms: 1) Symptoms in which the presence or lack of the certain types
of attributes has to be investigated. 2) Symptoms in which the cardinality of certain
connection types has to be analysed. This type is applicable to three cases: Firstly, one
particular model is analysed in terms of connection cardinality. Secondly, sole model
variants are analysed in terms of connection cardinality and the query is processed for
every available model variant. Thirdly, model variants under review are analysed with
another type of static or dynamic EA model in terms of connection cardinality. 3)
Symptoms in which more models have to be compared. This type is applicable to two
cases: Firstly, model variants have to be compared with another group of model variants
according to the project phases. Secondly, model variants have to be compared with a
static catalogue. 4) Symptoms in which more model variants have to be analysed and
compared during the progression of the project.
The proposed research provides highlights significant analytical potential compared to
the inbuilt query power of sole EA modeling tools. The study also gives an account for
symptom validation, e.g. by follow-up interviews at case organisations after operating a
research framework. The topic of validation raises two concerns which have to be
clarified. First, the proposed research does not provide the potential for matching the EA
models under review with an ideal model. This approach would imply the existence of
an ideal, aligned model which can be used for benchmark. The presence of a fully
aligned model base at case organisations would elicit the need for further alignment
steps. Thus, this kind of matching cannot be accomplished, and the proposed research
does not deal with the analysis of this kind of ideal alignment model base. Second, the
preliminary validation of misalignment symptoms cannot be done due to the specific
follow-up interpretations of misalignment phenomena at test organisations. There is no
need for the in vitro testing of misalignment symptoms, i.e. the preliminary
interpretation and evaluation of misalignment symptoms. This kind of validation also
involves a reference model about the ideal state of a case organisation, against that an
organisation can evaluate the presence of misalignment symptoms in advance. In
contrast to the need for in vitro testing, the proposed research shall use a soft, follow-up
validation based on post factum interviews and the interpretation of specific
organisational characteristics and organisational context.
The proposed research direction has limitations on the following areas. The first is that
the framework examines only the model environment, i.e. the details that are modeled.
In fact, the real operation of an organisations cannot be investigated, only the part which
is presented at the modeling level. This observation recalls the need for investigating the
state of models and the difference between models and reality in form of further follow-
up interviews. Future work should concentrate on solving this issue. The second
limitation is the problem of modeling tool lock-in and document format lock-in. The
same misalignment symptoms in different modeling tools and in different document
Misalignment Symptom Detection 157
formats have to be defined in a different way, which undermines the portability of the
proposed model. This limitation could be solved by an intermediate transformation layer
between the layer of documents under review and the layer of misalignment rule
generation. This topic is also deferred to future work. Another way to solve the problem
of lock-ins is to use XSLT transformation language to filter the relevant analysis details
from documents in different formats. This approach would make the models in different
document formats comparable for processing detection of the same misalignment
symptom. Further work needs to be carried out to implement the standardisation of
different document formats.
5 Conclusion
The paper dealt with the concept of enterprise architecture-based misalignment analysis.
It presented a research approach for EA-based misalignment assessment. The novelty of
the study lies in: 1) approaching the phenomenon of alignment from misalignment
perspective, 2) using a symptom-based approach to detect the state of misalignment in an
organisation, 3) using the concept of EAM to perform misalignment symptom detection
and 4) applying rule testing and XML validation techniques in EA environment. The
research produces structured data on the symptoms of misalignment. In a broad sense,
the usage of the proposed direction facilitates and eases the planning and evaluation of
IT service portfolio in large, complex and heterogeneous organisations. In addition, it
supports the development of strategic directions. The results of developing the proposed
model addresses two concerns: On the one hand, it confirms the compliance and
relevance of misalignment patterns described from existing, real-world misalignment
symptoms. On the other hand, it verifies the proper construction and operation of the
analysis methods provided.
Acknowledgement. The financial support of KÖFOP-2.1.2.-VEKOP-15-2016-00001
Public Service Development Establishing Good Governance is gratefully acknowledged.
References
[CS08] Carvalho, G., Sousa, P.: Business and Information Systems MisAlignment Model
(BISMAM): An Holistic Model leveraged on Misalignment and Medical Sciences
Approaches. In Johannesson, P., Gordijn, J. (eds.) Proceedings of the Third
International Workshop on Business/IT Alignment and Interoperability
(BUSITAL’08). CEUR, vol. 336, CEUR-WS, Aachen, pp. 104-119, 2008
[HV93] Henderson, J.C., Venkatraman, N.: Strategic Alignment: Leveraging information
technology for transforming organizations. IBM Systems Journal 32(1), pp. 4-16, 1993
[Za87] Zachman, J.A.: A Framework for Information Systems Architecture. IBM Systems
Journal 26(3), pp. 276-292, 1987