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Using Quality Function Deployment To Improve The Quality Of Data Models and Database Designs
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Daniel L. Moody
Using Quality Function Deployment To Improve The Quality Of Data Models and Database Designs
Track: J6
Abstract:
Data modelling is a method for defining information requirements independently of
how the information will be physically stored (Hull and King, 1987; ISO, 1987).
It is widely used in practice to define user information requirements as part of
the systems development process (Hitchman, 1995). The resulting data model can be
transformed into a physical database design in a relatively straightforward way
(Batini et al, 1994; Teory, 1994). Although data modeling represents only a small
proportion of the total systems development effort (estimated to be about 2%), its
impact on the quality of the final system is probably greater than any other
phase (Moody and Shanks, 1998; Witt and Simsion, 2000). The data model is a major
determinant of system development costs (ASMA, 1996), system flexibility (Gartner
Research, 1992), integration with other systems (Moody and Simsion, 1995) and the
ability of the system to meet user requirements (Banker and Kauffman, 1991). The
combination of low cost and high impact suggests that the data modelling phase
represents a high leverage point for improving software development quality and
productivity. The traditional thrust of software quality assurance has been to
use "brute force" testing at the end of development (van Vliet, 1993). However
empirical studies show that more than half the errors which occur in the systems
development process are the result of inaccurate or incomplete requirements
(Martin, 1989; Lauesen and Vinter, 2000). Also, the most common reason for failure
of systems development projects is incomplete requirements (Standish Group, 1995;
1996). This suggests that substantially more effort should be spent during early
development phases to catch defects when they occur, or to prevent them from
occurring altogether (Zultner, 1992). According to Boehm (1981), relative to
removing a defect discovered during the requirements stage, removing the same
defect costs on average 3.5 times more during design, 50 times more at the
implementation stage, and 170 times more after delivery. Empirical studies have
shown that moving quality assurance effort up to the early phases of development
can be 33 times more cost effective than testing done at the end of development
(Walrad and Moss, 1993). However, it is during analysis that the notion of software
development as a craft rather than an engineering discipline is strongest, and
quality is therefore most difficult to assess. There are relatively few guidelines
for evaluating the quality of data models, and little agreement even among experts
as to what makes a "good" data model. As a result, the quality of data models
produced in practice is almost entirely dependent on the competence of the
designer (Moody and Shanks, 1994; Krogstie et al, 1995).
Objectives Of This Paper:
This paper defines an approach to improving the quality of data models which
incorporates the principles of QFD:
- It defines a set of quality factors for evaluating the quality of data models.
These correspond to technical requirements of the data model. The quality
factors have been empirically validated in practice using the technique of
action research (Moody and Shanks, 1998).
- The set of quality factors are then mapped to external software quality factors
(user defined dimensions of quality), as defined by Fitzpatrick and Higgins
(1998). These correspond to customer requirements of the system. The mapping
between data model quality factors and external quality factors forms the first
House of Quality as defined in QFD (Evans and Lindsay, 2002).
- It defines interactions between the data model quality factors (technical
requirements). These are derived from theory and empirical studies of data
modelling.
- It shows how characteristics of the data model can be mapped to characteristics
of the physical database design (corresponding to component characteristics) to
form the second House of Quality. Together these Houses of Quality provide the
basis for designing a database that more effectively meets user requirements.
Author:
Daniel Moody is an Associate Professor in the Department of Computer and
Information Science at the Norwegian University of Science and Technology
(visiting from the School of Business Systems, Monash University). He is the
Australian President of the Data Management Association (DAMA) and Australian
World-Wide Representative for the Information Resource Management Association
(IRMA). Daniel has held senior data management positions in some of Australia's
largest commercial organisations, and has worked as a consultant for IBM Australia
and Simsion Bowles & Associates (an Australian-based data management consultancy).
He has consulted in a wide range of organisations both in Australia and overseas,
including Singapore, Hong Kong, Indonesia, Taiwan and South Korea. He has held
academic positions at a number of Australia's leading universities, including the
University of Melbourne, the University of New South Wales and the University of
Queensland. His research interests include data modelling, information resource
management, information economics, data warehousing and knowledge management. He
has published over 50 papers in the IS field, in both practitioner and academic
forums, and has chaired a number of national and international conferences.
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