Event and data sources for enterprise architecture documentation

Enterprise Architecture Management (EAM) has become a widely accepted discipline that concerns itself with the alignment of business and information technology (IT) and deals with the increasing complexity of information system (IS) landscapes. It is the practice of documenting the interrelations of business and IT entities by developing a model of the enterprise architecture (EA). This documentation of the EA acts as basis for planning the future architecture and prioritizing the projects that transform the current architecture. Due to globalizing markets and the fast paced evolution of IT, organizations are forced to become agile and frequently adapt their internal architecture in order to stay competitive. Today, the majority of tasks concerning the maintenance of EA documentation are based on manual methods and are connected to huge data collection efforts. This has a negative impact on the quality and reliability of the EA documentation and therefore diminishes the benefits of EAM. The popularly utilized EA frameworks cover the process of keeping the EA documentation up-to-date only marginally. In recent literature, there has been an effort to automate the EA documentation with the use and integration of different productive IS across the company. It was identified that common IS in organizations not only hold data about the EA, but also can contain events that could be used to trigger EA documentation processes. However, only few concrete implementations of automated EA documentation are examined in research or found in practice, because organizations face several challenges when attempting to integrate IS into an automated EA documentation. This thesis identifies possible sources of EA data and EA events in the form of common IS or databases in today’s enterprises. Further, the thesis develops an evaluation catalogue for potential source candidates in order to recommend a set of suitable documentation techniques for each source. A proof of concept through a case-study at a financial institution demonstrates the feasibility and applicability of the catalogue. The proposed data source selection catalogue for EA documentation supports the identification and selection of productive IS and suitable techniques in order to increase the quality of EA documentation.