Combining Enterprise Architecture & Operational Data To Better Support Decision-Making

Decision-making may be complex in large organizations dealing with many stakes and situations. Currently, decision-making is often done using business intelligence solutions, combined with data warehousing technologies storing operational data. Enterprise architecture is often used for strategy purposes and provides an overview of complex organizational architectures, showing business entities and relations. This thesis combines best of both worlds, filling the gap between enterprise architecture and business intelligence by combining operational data with enterprise architecture using the Enterprise Architecture Intelligence Lifecycle (EAIL), which is the main contribution of this thesis. Furthermore, we present a Concept Match Library (CML), which is a database that is able to store enterprise architecture meta data and operational data in the 'time' dimension. The CML may be used for forecasting purposes; using operational data with enterprise architecture meta data to predict future situations and adapt the corporate strategy to it.