How to Build Enterprise Data Models to Achieve Compliance to Standards or Regulatory Requirements (and Share Data)
Kim, Henry M., Fox, Mark S., Sengupta, Arijit, Journal of the Association for Information Systems
Sharing data between organizations is challenging because it is difficult to ensure that those consuming the data accurately interpret it. The promise of the next generation WWW, the semantic Web, is that semantics about shared data will be represented in ontologies and available for automatic and accurate machine processing of data. Thus, there is inter-organizational business value in developing applications that have ontology-based enterprise models at their core. In an ontology-based enterprise model, business rules and definitions are represented as formal axioms, which are applied to enterprise facts to automatically infer facts not explicitly represented. If the proposition to be inferred is a requirement from, say, ISO 9000 or Sarbanes-Oxley, inference constitutes a model-based proof of compliance. In this paper, we detail the development and application of the TOVE ISO 9000 Micro-Theory, a model of ISO 9000 developed using ontologies for quality management (measurement, traceability, and quality management system ontologies). In so doing, we demonstrate that when enterprise models are developed using ontologies, they can be leveraged to support business analytics problems - in particular, compliance evaluation - and are sharable.
Key Words: enterprise modeling, ontologies, quality management, ISO 9000, regulatory requirements
A (computational) enterprise (data) model 1 is "a computational representation of the structure, activities, processes, information, resources, people, behavior, goals, and constraints of a business, government, or other enterprise" [Fox and Gruninger, 1998]. The model can be a conceptual artifact resulting from an analysis phase. It can also be logical, resulting from design; and physical, resulting from implementation. It is a broad term that encompasses the models of the following: General Enterprise Reference Architecture and Model (GERAM) applicable to all industries [Kosanke et al., 1997; Tølle and Bernus, 2003; Vernadat, 1996]; Partial Enterprise Reference Architecture and Model (PERAM) applicable to a few industries; or instantiated models applicable to all or parts of one enterprise.
Enterprise data models underlie, for example, all Enterprise Resource Planning (ERP) and supply chain management applications. However, effective use of data models used by different applications within the same organization, let alone between organizations, is an issue of concern. The Internet provides a ubiquitous infrastructure, though how best to use this infrastructure for data model use is still an open question.
In this paper, we postulate and demonstrate the following about enterprise data models: When they are developed using computational ontologies, they: 1) can be leveraged to support business analytics problems, in particular, compliance evaluation (to e.g. ISO 9000 [ISO, 2000] or Sarbanes-Oxley [Sarbanes-Oxley, 2002]) and 2) are easier to share, increasingly and especially over the Web.
A (computational) ontology2 is a data model that "consists of a representational vocabulary with precise definitions of the meanings of the terms of this vocabulary plus a set of formal axioms that constrain interpretation and well-formed use of these terms" [Campbell and Shapiro, 1995]. Because precise definitions and axioms exist, proper interpretation of data results from automated theorem proving (inference). Hence, correct interpretation by a computer-i.e. computational inference, not a referential theory of semantics-or a decision maker who did not develop the definitions and axioms is possible. Therefore, ontologies are the base for presenting the second postulate of this paper. Furthermore, ontologies are a fundamental base for the nascent semantic Web [Berners-Lee et al., 2001], an enhanced Web wherein software agents conduct commerce automatically over the Web by applying business rules and using business vocabulary, both represented in Web-sharable ontologies. …