A Model of Error Propagation in Conjunctive Decisions and its Application to Database Quality Management

A Model of Error Propagation in Conjunctive Decisions and its Application to Database Quality Management

Irit Askira Gelman (DQIQ, USA)
Copyright: © 2012 |Pages: 24
DOI: 10.4018/jdm.2012010105
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Abstract

This study centers on the accuracy dimension of information quality and models the relationship between input accuracy and output accuracy in a popular class of applications. These applications consist of dichotomous decisions that are implemented through conjunction of selected criteria. In particular, the model provides the tools for quantifying the effect of errors in each decision input on the accuracy of a decision. Application of the model is illustrated through the example of a residential real estate database, where users’ preferences are captured by conjunctive decision rules. This example demonstrates how the new model can be utilized for quantifying the aggregate damage that errors in different database attributes inflict on property selection decisions. Finally, this paper reports on an initial empirical validation of the proposed model through a series of Monte Carlo simulations. Numerical estimates of the model that have been developed through this inquiry can be useful for data and information quality assessments or policy-making purposes. Mainly, they can be integrated into cost-benefit analyses that assess alternative data accuracy enhancements or process and system designs.
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Introduction

Nearly every organization is plagued by bad data, which result in higher costs, angry customers, compromised decisions, and greater difficulty for the organization to align departments. The overall cost of poor data quality to businesses in the US has been estimated to be over 600 billion dollars a year (Eckerson, 2002), and the cost to individual organizations is believed to be 10%-20% of their revenues (Redman, 2004). Evidently, these estimates are not expected to dramatically improve anytime soon. A survey that covered a wide range of organizations in the US and several other countries showed that about half of the organizations had no plans for improving data quality in the future (Eckerson, 2002).

The low motivation of organizations to improve the quality of their data is often explained by the general difficulty of assessing the economic consequences of the quality factor (Eckerson, 2002; Redman, 2004). The economic aspect of data quality has been drawing a growing research interest in recent years. An understanding of this aspect can be crucial for convincing organizations to address the data quality issue. It can guide decisions on how much to invest in data quality and how to allocate limited organizational resources. The economics of data quality, however, is partly determined by the relationship between the quality of the data and the quality of the information that the information system outputs (Note that, in this paper, the term “data” will largely describe the raw, unprocessed input of an information system; the term “information” will mostly designate the output of the system). An increasing number of management information systems (MIS) studies have centered on this relationship, while parallel questions have been studied in numerous research areas (e.g., Condorcet, 1785; Cover, 1974; Clemen & Winkler, 1984; Grofman, Owen, & Feld 1983; Kuncheva & Whitaker, 2003). However, our grasp of the relationship between an information system’s data quality and its output information quality is still often limited.

This study highlights the accuracy dimension of information quality (Wang & Strong, 1996) and uncovers the relationship between input accuracy and output accuracy in a popular class of applications. These applications consist of dichotomous decisions that are implemented through logical conjunction of selected criteria. Decision-making instances that are implemented through conjunctive rules are often labeled “satisficing.” This term was coined by Herbert Simon to denote problem-solving and decision-making that aims at satisfying a chosen aspiration level instead of an optimal solution (Simon, 1957). Research indicates that satisficing decision rules agree with human choices in diverse situations, often involving complex problems such as when the number of alternatives or decision criteria is high (Payne, Bettman, & Johnson, 1993). Evidence in this direction has been found in consumer choice settings, medical diagnosis, job preference decisions, university admission decisions, residential rental searches, political leaders’ decision-making, and in many other domains (e.g., Einhorn, 1970, 1971, 1972; Lussier & Olshavsky, 1979; Mintz, 2004; Park, 1976; Payne, 1976; Phipps, 1983).

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