Drafting Enterprise Models

Drafting Enterprise Models

Hector Florez, Mario Sanchez, Jorge Villalobos
DOI: 10.4018/978-1-5225-5522-3.ch008
(Individual Chapters)
No Current Special Offers


Enterprise models are created to analyze, document, and communicate the state of an enterprise under multiple perspectives. In addition to being large and complex, the construction of these models presents several difficulties: firstly, they require information provided by sources that might be inaccurate, incomplete, or even obsolete; secondly, although they should be structured, it is not possible to completely define their metamodel a priori. To support this construction process, the usage of enterprise model drafts is proposed, which should have the capacity to conform to changing metamodels and should also support incomplete or imperfect information. Unfortunately, current frameworks and tools have limitations for supporting these two features. Therefore, a set of strategies for the construction of modeling environments that make it possible to properly handle drafts is also proposed. These strategies include the support of metamodel flexibility during the modeling process and an approach to model imperfect information.
Chapter Preview


Enterprise Modeling refers to the practices to build and use Enterprise Models to represent multiple aspects of an enterprise. The purpose of enterprise models is to document, communicate, analyze organizational structures, business processes, information systems, and infrastructure with the aim of improving these elements (Buschle, Ullberg, Franke, Lagerström, & Sommestad, 2010; Iacob & Jonkers, 2006; Kurpjuweit & Winter, 2007; Lankhorst, 2013). While enterprise models can be small, simple, and well-focused, most are typically the opposite; they are large, complex, and cover a large number of areas and lines of business. Such extensive and complex models are useful for enterprise analysis purposes because they enable holistic approaches for understanding an organization, combining both business and technological aspects (Lagerström, Franke, Johnson, & Ullberg, 2009; Lankhorst, 2013).

However, this complexity has strong implications for the construction of the models. The construction process starts with the selection of a framework of reference that defines the structure of the model, and which is typically based on a metamodel. Then, modelers have to discover and classify multiple sources of enterprise information (e.g., people, documents, meetings) which can be located anywhere in the company, or may even be external (e.g., regulations, policy documents). Afterwards, modelers perform different kinds of observations on sources of enterprise information (e.g., interviews, reviews, meeting minutes), extract information, and introduce these into the model.

It is at this point that problems start to appear. First of all, there is the issue of compatibility between the facts and the metamodel selected as the underlying structure for the enterprise model. If the metamodel does not offer an adequate metatype to represent a certain fact, that fact will either have to be omitted from the model, or it will have to be adjusted in order to match the metatypes existing in the metamodel. For example, standard metamodels commonly have a metatype to represent infrastructure devices, but do not include a specific metatype to represent load balancers. Thus, models sacrifice a bit of semantics when they represent load balancers using the simpler infrastructure device concept. The second problem has to do with the quality of the sources and the observations, which may have different levels of precision and reliability. For example, it is possible to find sources with contradictory information, opposite points of view, obsolete information, and conflicts of interest which can generate inconsistency in the model.

A natural way to handle the construction of enterprise models is to use drafts, that is, to a model in phases without having committed to a definitive metamodel from the beginning. Furthermore, drafts may lack complete information and even be imperfect (imprecise, inconsistent, vague, uncertain) (Florez, Sanchez, & Villalobos, 2014b; Florez, Sánchez, & Villalobos, 2013). The problem is that current enterprise modeling environments, as well as general purpose modeling frameworks and tools, lack the capabilities to support what was just described: typically there is little or no support for flexible or changing metamodels, nor is there support for imperfection in the models, or to trace the modeling processes.

Models have to be perfectly structured during the entirety of the modeling process.

The objective of this chapter is to propose mechanisms to support the construction of drafts for enterprise modeling, while addressing problems highlighted in existing tools. This proposal is based on three core elements: 1) a strategy to support flexible metamodels which makes it possible to easily change the metamodels even after the modeling process has started; 2) the necessary elements to model imperfection caused by the information gathering process and the quality of the sources; and 3) the support for documenting the construction and evolution of the models. This documentation, which presents information about the sources, observations, and facts used by modelers to build the drafts, is valuable to build the definitive versions of the models. The whole proposal described has been implemented in iArchiMate, which is a modeling tool for the creation of enterprise model drafts based on ArchiMate (The Open Group, 2012).

Key Terms in this Chapter

ArchiMate: Modeling language for describing and visualize enterprise models involving several enterprise domains sorted the aspects active structure, passive structure, behavior, and motivations, as well as in the layer, business, application, infrastructure, motivation, and implementation.

Enterprise Modeling: Practice to create, maintain, and use models in order to represent certain aspects of the enterprise.

iArchiMate: Enterprise modeling tool for creating drafts of ArchiMate models.

Imperfect Models: Models that include imprecision, inconsistency, vagueness, uncertainty, and incompleteness.

Draft of an Enterprise Model: Not finished enterprise model that might include imperfection features to be refined when new useful information is gathered from enterprise sources.

Linguistic Conformance: Relation between a model and its corresponding metamodel in terms of structure.

Validation Rules: Artifacts that verifies the accomplishment of linguistic and ontological conformance of models.

Ontological Conformance: Relation between a model and its corresponding metamodel in terms of semantics.

Complete Chapter List

Search this Book: