A recurring and problematic characteristic of information systems’ (IS) use has been the development of isolated ‘islands of information’. As technological advances have provided solutions, so, however, have the original problems been transformed. The current archipelago exists largely unrecognized, partly because the matter from which the islands are formed has also changed. It is no longer the content of the computerized systems that is isolated but information about IS practice itself. Academic research, practitioner reports and vendor marketing all contribute to a disparate collection of contradictory and often unevaluated information concerning IS practice, while the large volume of this information and the ease of its accessibility serve to disguise the extent of its fragmentation. Following the lead established by medicine and healthcare, the creation of an ‘evidence-based’ culture within the IS community, particularly the creation of systematic analyses of relevant literature, has been proposed as one means of bridging the gaps between these islands. There are, however, significant differences between IS and healthcare which need to be recognized if the transfer of concepts is to be successful. In this paper we identify and describe some of those differences and provide an initial sketch of a framework in which an evidence-based IS culture could flourish.
Entity‐relationship (E‐R) models continue to be the most common means of documenting the data requirements of information systems. Whether used as the basis for relational database design or to record organisational conceptual data structures, it is essential that the information content (the semantics) of such models is clearly understood by both the builders and the users of such models. In particular, novice data modellers, or users, and their teachers need to understand how well a model represents a particular scenario description. Presents a practical method that has been developed for use by data modelling students. This method, termed NaLER (Natural Language for E‐R), provides student data modellers with a more organised way of assessing the information content of models that they or others have produced. It can also be used as a means of comparing those models with the information contained within the original description of the Universe of Discourse (UoD). It is suggested that the method could be of practical benefit not only to students but also to anyone with a need to ascertain the semantic content of a data model.
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