Assessing the Complexity of Dynamics in Enterprise Architecture Planning – Lessons from Chaos Theory

Enterprise Architecture (EA) models capture the fundamental elements of organizations and their relationships to serve documentation, analysis and planning purposes. As the elements and their relationships change over time, EA planning becomes increasingly complex. An analysis of existing methods shows that the complexity of dynamics is not sufficiently addressed. We argue that a sophisticated understanding of the complexity matter is prerequisite for EA planning method construction. As Chaos Theory (CT) is deployed in natural and social sciences—as well as in different contexts of IS research—to describe and understand the behavior of complex systems over time, we use properties of CT to assess the complexity of dynamics in EA planning and to derive requirements for EA planning methods. Our findings emphasize the importance of initial conditions of the architecture for EA planning and the need to harmonize planning granularities in order to achieve predictable results

EMPIRISCHE VALIDIERUNG VON INTEGRATIONSTYPEN AM BEISPIEL UNTERNEHMENSÜBERGREIFENDER INTEGRATION

Integration wird oft als das Ur- und Hauptthema der Wirtschaftsinformatik angesehen [23, 29]. Mergers & Acquisitions [15], Enterprise Application Integration (EAI) [13], Datenintegration [10], unternehmensübergreifende B2B-Prozessintegration [3] oder die Integration von fachlichen Strukturen und deren informationstechnische Unterstützung im Rahmen des Business/IT Alignment [19] sind nur einige Beispiele für die sehr unterschiedlichen Erscheinungsformen von Integrationsprojekten. In einer phänomenologischen Betrachtung lassen sich daraus beispielsweise Integrationsdimensionen wie Integrationsform (verschmelzende vs. verknüpfende Integration), Integrationsreichweite (bereichsweite, unternehmensweite vs. unternehmensübergreifende Integration), Integrationsobjekt (Daten-, Funktions- vs. Prozessintegration) oder Integrationsrichtung (vertikale vs. horizontale Integration) unterscheiden [11, 21]. Eine solche phänomenologische Betrachtung bleibt jedoch immer unvollständig und eignet sich darum nur unzureichend, eine handhabbare Anzahl fundamentaler Integrationstypen zu definieren und sie methodisch zu unterstützen. Die Identifikation von abgrenzbaren Integrationstypen ist jedoch von Bedeutung, da es auf Grund der Heterogenität von Integrationsprojekten keine „one-size-fits-all“-Methode gibt, sondern im Sinne des Situational Method Engineering [2, 9, 16, 28] jeweils geeigneter situativer Methoden bedarf. Die fundamentalen Integrationstypen können die Basis für Methodenfragmente bilden, aus welchen situative Methoden erstellt werden können. Winter [31] hat darum vorgeschlagen, Integrationstypen nicht phänomenologisch, sondern auf Basis abstrakter Zusammenhänge in Unternehmensarchitektur- Metamodellen zu definieren. Ziel dieses Beitrags ist es, anhand verschiedener Integrationsauf-gaben die vorgeschlagenen Integrationstypen am Beispiel der unternehmensübergreifenden Integration empirisch zu überprüfen. Im Folgenden werden dazu bestehende Arbeiten im Bereich Integration analysiert, um daraus die hier adressierte Forschungslücke abzuleiten und die von Winter vorgeschlagenen Integrationstypen vorzustellen (Abschnitt 2). Anschließend werden in Abschnitt 3 das Vorgehen sowie die Befunde der empirischen Untersuchung vorgestellt. Abschnitt 4 diskutiert die Ergebnisse der Untersuchung sowie die sich daraus ergebenden Implikationen. In Abschnitt 5 erfolgen eine Zusammenfassung sowie der Ausblick auf den weiteren Forschungsbedarf

Complexity Levels of Representing Dynamics in EA Planning

Abstract. Enterprise Architecture (EA) models provide information on the fundamental as-is structure of a company or governmental agency and thus serve as an informational basis for informed decisions in enterprise transformation projects. At the same time EA models provide a means to develop and visualize to-be states in the EA planning process. Results of a literature review and implications from industry practices show that existing EA planning processes do not sufficiently cover dynamic aspects in EA planning. This paper conceptualizes seven levels of complexity for structuring EA planning dynamics by a system of interrelated as-is and to-be models. While level 1 represents the lowest complexity with non-connected as-is and to-be models, level 7 covers a multiperiod planning process also taking plan deviations during transformation phases into account. Based on these complexity levels, a multi-stage evolution of EA planning processes is proposed which develops non-dynamic as-is EA modeling into full-scale EA planning

Understanding Enterprise Architecture Management Design – An Empirical Analysis

Enterprise architecture management (EAM) is expected to pro-vide business value by guiding the continuous development and transformation of an enterprise. Based on the approach we strive for constructing useful artifacts that guide the successful and situational design of EAM. In order to do so we argue for a tho-rough analysis of the design problem in advance. This is realized by a two-step survey conducted on EAM practices. The empirical analysis reveals eight determining design factors of EAM, a de-lineation of three different types of EAM design in the form of clusters as well as insight about the successfulness of the different types

A survival analysis of application life spans based on enterprise architecture models

Abstract: Modern enterprises face the challenge to survive in an ever changing environment. One commonly accepted means to address this challenge and further enhance survivability is enterprise architecture (EA) management, which provides a holistic model-based approach to business/IT alignment. Thereby, the decisions taken in the context of EA management are based on accurate documentation of IT systems and business processes. The maintenance of such documentation causes high investments for enterprises, especially in the absence of information on the change rates of different systems and processes. In this paper we propose a method for gathering and analyzing such information. The method is used to analyze the life spans of the application portfolio of three companies from different industry sectors. Based on the results of the three case studies implications and limitations of the method are discussed.