Metrics for Managing Quality in Information Modeling
Mario Piattini (Universidad de Castilla-La Mancha, Spain), Marecela Genero (Universidad de Castilla-La Mancha, Spain), Coral Calero (Universidad de Castilla-La Mancha, Spain), Macario Polo (Universidad de Castilla-La Mancha, Spain) and Francisco Ruiz (Universidad de Castilla-La Mancha, Spain)
In a global and increasingly competitive market, quality is a critical success factor for all economical and organisational aspects and especially in Information Systems (IS). We can affirm that in the next millennium information quality will be an essential factor for company success in the same way product and service quality have been over the last years. It is essential to tackle the subject of information quality in order to achieve a good IS for the company; this way data become true information and knowledge. Companies must manage information as an important product, capitalise knowledge as a main asset, surviving and prospering in the digital economy (Huang et al., 1998). Improving information quality will enhance client satisfaction and, at the same time, personnel satisfaction, while improving the company as a whole. Unfortunately until a few years ago, quality approaches focused on program quality and disregarded information quality (Sneed and Foshag, 1998). Even in traditional information modeling and database design, quality related aspects have not been incorporated explicitly (Wang and Madnick, 1993). It is time to consider information quality as a main goal to pursue, instead of as a subproduct of information modeling or a database creation processes. Quality in information modelling has traditionally been a poorly understood area. Most of the work done until a few years ago was limited to listing a set of properties or desirable characteristics for conceptual data models and proposing different transformations for improving schema quality (Batini et al., 1992; Reingruber and Gregory, 1994; Boman et al., 1997). Recently, some interesting frameworks have been proposed for addressing quality in information modeling in a more systematic way (Moody and Shanks, 1994; Krogstie et al., 1995; Shanks and Darke, 1997; Moody et al., 1998). However, quality criteria alone are not enough to ensure the quality in practice because people will generally make different interpretations of the same concept. According to the Total Quality Management (TQM) literature, measurable criteria for assessing quality is necessary to avoid “arguments of style” (Zultner, 1992). Measurement is fundamental in order to apply statistical process control which is one of the key techniques in the TQM approach (Deming, 1986). Measurement is used not only for understanding, controlling, and improving development, but also for determining the best ways to help practitioners and researchers (Schneidewind, 1997). The objective should be to replace intuitive notions of quality in information modeling, with formal, quantitative measures, thus, helping to reduce subjectivity and bias in the evaluation process. In this chapter we will give an overview of the work carried out regarding quality in information modeling, and we will also propose a set of new metrics for evaluating quality in information modeling. Finally, we discuss future and emerging trends in this area and provide some concluding remarks.