Towards Automation of Enterprise Architecture Model Maintenance

Enterprise Architecture Management (EAM) is a common practice in organizations that need to have a model of how their business relates to the supporting IT-landscape in order to make informed decisions to enhance the enterprise architecture (EA). Creating and maintaining such an EA model is an expensive and time consuming but crucial task in today’s organizations. This has been recognised by both researchers and practitioners. However, only little research literature and practical approaches can be identified that target the automation of the EA model maintenance and to reduce the manual work. In this thesis we elaborate means to increase the data quality attributes of actuality and consistency of EA models via semi-automated data collection processes from external data sources and events. It is our main goal to better synchronize EA models with what they represent in the real world and thus reduce the manual labor of model maintenance.