Methodology for data quality management using a data driven approach to implement data quality assessment and improvement processes in master data management initiatives.In recent years, the rise of data-driven decision making has skyrocketed. This has led to several data quality issues, highlighting the importance of strategies to improve and ensure the quality of data sets when implementing master data initiatives. This research focuses on establishing a methodology that allows evaluating and solving data quality problems directly on the data, giving first a review and evaluation of the efforts and methodology found in the literature, followed by the presentation of the methodology and proposal. detailing the processes for its immediate implementation and finally, carrying out the implementation of the methodology in a data set and presenting the results obtained at each stage.

W ithin the information systems field, the task of conceptual modeling involves building a representation of selected phenomena in some domain. High-quality conceptualmodeling work is important because it facilitates early detection and correction of system development errors. It also plays an increasingly important role in activities like business process reengineering and documentation of best-practice data and process models in enterprise resource planning systems. Yet little research has been undertaken on many aspects of conceptual modeling. In this paper, we propose a framework to motivate research that addresses the following fundamental question: How can we model the world to better facilitate our developing, implementing, using, and maintaining more valuable information systems? The framework comprises four elements: conceptual-modeling grammars, conceptual-modeling methods, conceptual-modeling scripts, and conceptual-modeling contexts. We provide examples of the types of research that have already been undertaken on each element and illustrate research opportunities that exist. (Conceptual Modeling; Information Systems Development; Ontology)

Information systems analysis and design (ISAD) methodologies provide facilities for describing existing or conceived real-world systems. These facilities are ontologically expressive if they are capable of describing all real-world phenomena completely and clearly. In this paper we formally examine the notion of the ontological expressiveness ofa grammar and discuss some of its implications forthe design and use of ISAD methodologies. We identify some generic ways in which ontological expressiveness may be undermined in a grammar and some potential consequences of these violations. We also examine ontological expressiveness within the context of some other desirable features that might be considered in the design of ISAD methodologies.

The deep structure of an information system comprises those properties that manifest the meaning of the real-world system the information system is intended to model. In this paper we describe three models we have developed of information systems' deep-structure propeflies. The first, the representational model, proposes a set of cdnstructs that enable the ontological expressiveness of grammars used to model information systems (such as the entity-relationship model) to be evaluated. The second, the state-tracking model, proposes four requirements that information systems must satisv if they are to faithfully track the real-world system they are intended to model. The third, the good-decomposition model, proposes three necessary conditions that information systems must meet if they are to be welt decomposed. The three models provide a theoretically based, structured way of evaluating grammars that are used to analyse, design and implement information systems and scripts that have been generated using these grammars to describe specific information systems.

Abstracf-Theoretical developments in the CS and IS disciplines have been inhibited by inadequate formalization of basic constructs. I O this paper we propose an ontological model of aa information system that provides precise definitions of fundamental concepts like system, subsystem, and coupling. We use this model to analyze some static and dynamic properties of an information system and to examine the question of what constitutes a "good" decomposition of an information system.