Analyzing the Evolution and Usage of Enterprise Architecture Management Patterns

Enterprise Architecture Management (EAM) evolved to a powerful function to support organizations in strategic decision making and in the fulfillment of business requirements. The organization of an EAM function is not an easy task and demands for best practices, suited to the respective needs of an organization. The pattern-based EAM approach constitutes a collection of such best practices, including detailed information on how to approach EAM activities in organizations. Recent research extended the original EAM pattern structure from 2008 with further concepts to provide more extensive patterns. We conducted an online survey with 31 EAM experts to identify current trends in this domain. We compare documented EAM patterns from 2008 with those identified in the conducted survey to evaluate changes of EAM best practices over the last years. Our research results reveal major changes of EAM needs and best practices, which evolved across several industries and point out clear trends

DRIVERS AND EFFECTS OF INFORMATION SYSTEMS ARCHITECTURE COMPLEXITY: A MIXED-METHODS STUDY

Today’s organizations deal with a significant complexity of their information systems (IS) architec-ture—a complex cobweb of heterogeneous IS with tight, mutual interrelations. With the constantly in-creasing number of IS along with the inherent complexity of the organizational context in which IS are embedded, organizations lose control of their IS architecture’s evolution. Through employing a se-quential mixed-methods research design, this study investigates the drivers and effects of IS architec-ture complexity. Based on the extant literature and on focus groups data, at the outset we develop a research model and derive its constitutive hypotheses. We subsequently test the research model follow-ing a partial least squares (PLS) approach to structural equation modelling (SEM) with survey re-sponses from 249 IT managers and architects. While differentiating structural and dynamic complexi-ty, this study confirms a high degree of integration, large size, high diversity, strong dynamics, and, in particular, inadequate planning as the main drivers of IS architecture complexity. Further, this study affirms the negative effect of IS architecture complexity on the efficiency, agility, comprehensibility, and predictability of the IS

Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications

Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques, that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning model to detect anomalies within an enterprise application, based upon data from multiple APM systems. The research was conducted in collaboration with a European automotive company, using two months of live application data. We show that our model detects abnormal system behavior more reliably than a commonly used outlier detection technique and provides information for detecting root causes

Business Capability Maps: Current Practices and Use Cases for Enterprise Architecture Management

This paper provides a state-of-the-art report on the usage of business capability maps in enterprise architecture management. We conducted expert interviews with 25 organizations to reveal the benefits and challenges of capability-based enterprise architecture management and evaluated 14 use cases on the feasibility and benefit of using business capability maps in practice. The results reveal increasing interest and acceptance of the approach in practice and among support organizations

Causes and Consequences of Application Portfolio Complexity – An Exploratory Study

Part 2: Regular PapersInternational audienceApplication Portfolio (AP) complexity is an increasingly important and strongly discussed issue by both researchers and practitioners. Application portfolios in large organizations have become more and more difficult to understand, resulting in costly efforts to maintain and operate them. Although this is an urgent topic in large organizations, researchers and industry experts do not yet have a common understanding of this phenomenon and lack appropriate methods to measure and manage the respective complexity. We conduct an exploratory case study with the central enterprise architecture management (EAM) governance team and ten application owners of a large European automotive company to identify and link root causes and consequences of AP complexity. Furthermore, we evaluate possible solutions to decrease or manage this complexity from an application owners perspective. The results are interpreted from a socio-technical systems perspective