Decision support oriented Enterprise Architecture metamodel management using classification trees

Models are an integral part of the discipline of Enterprise Architecture (EA). To stay relevant to management decision-making needs, the models need to be based upon suitable metamodels. These metamodels, in turn, need to be properly and continuously maintained. While there exists several methods for metamodel development and maintenance, these typically focus on internal metamodel qualities and metamodel engineering processes, rather than on the actual decision-making needs and their impact on the metamodels used. The present paper employs techniques from information theory and learning classification trees to propose a method for metamodel management based upon the value added by entities and attributes to the decision-making process. This allows for the removal of those metamodel parts that give the least “bang for the bucks” in terms of decision support. The method proposed is illustrated using real data from an ongoing research project on systems modifiability.