New sources of failure are present in
DSS: correctness and trustworthiness of the information are the basis of the decision-
making process. We do not only need to understand the user??™s information
needs, but also account for keeping the data repository up-to-date according to user
specifications. Also, update processes and their frequency must be considered, as
well as the analysis of the quality and completeness of the data sources.
It follows that there is a need for techniques that, besides accounting for the software
process cycle and functional requirements, also consider the quality of the information
the system will deliver. There are several reasons for this. For instance, most
of the time, people developing information systems do not consider the impact of
low quality data (Kimball, Reeves, Ross, & Thornthwaite, 1998). Low data quality
is more a rule than an exception. Just to give an example, it has been detected
in the U.S., that approximately 50 to 80% of the computerized criminal records are
inaccurate, incomplete, or ambiguous (Strong, Yang, & Wang, 1997). So far, the
contribution of software engineering for addressing the problems stated has been
60 Vaisman
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Idea Group Inc. is prohibited.
limited, although many techniques have been proposed in order to analyze and
measure a data quality requirement.
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