Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems

Abstract

This paper presents a general model to assess the impact of data and process quality upon the outputs of multi-user information-decision systems. The data flow/data processing quality control model is designed to address several dimensions of data quality at the collection, input, processing and output stages. Starting from a data flow diagram of the type used in structured analysis, the model yields a representation of possible errors in multiple intermediate and final outputs in terms of input and process error functions. The model generates expressions for the possible magnitudes of errors in selected outputs. This is accomplished using a recursive-type algorithm which traces systematically the propagation and alteration of various errors. These error expressions can be used to analyze the impact that alternative quality control procedures would have on the selected outputs. The paper concludes with a discussion of the tractability of the model for various types of information systems as well as an application to a representative scenario.information systems: management, reliability: quality control, computers: systems design

Empowering Society through Knowledge Records

“…Shanks and Tansley [19] define quality in terms of "fitness for purpose". Others consider accuracy, reliability, importance, consistency, precision, timeliness, understandability, conciseness and usefulness as desirable quality dimensions [2], [24]. The framework by Strong et al [22], suggests four quality dimensions: intrinsic, contextual, representational and accessibility.…”

A risk based model for quantifying the impact of information quality

“…Models proposed in academia are either focusing on information quality processes and technical metrics without characterizing the impact of information quality, e.g. (D. P. Ballou and Pazer 1985;D. Ballou et al 1998;Askira Gelman 2011;Parssian, Sarkar, and Jacob 2004), or use utility theory to characterize the impact of information quality (Even and Shankaranarayanan 2007;Ahituv 1980), which is difficult to apply in practice as utilities are difficult to link to key business performance indicators.…”

eHealth-as-a-Service: A Service Based Design Approach for Large Scale eHealth Architecture

“…Based on logical deduction, it was predicted that eHaaS architectural concepts will have a positive effect on information quality. Simulations provide the basis for an evaluative analysis using information quality mapping (IP-Map) to generate the empirical traces for observation processes (Ballou & Pazer, 1985;Shankaranarayanan, Wang, & Ziad, 2000). This…”

“…Using scanner technologies which identifies the individual and retrieves their SSOT demographic and health-related information satisfies meta-requirement MR1. The risk of errors is reduced with the minimisation of human mediated transformation activities leading to better information quality (Ballou & Pazer, 1985). It is assumed that clinicians are using a web-native CIS (ePractice) however, consideration is given to legacy systems storing information locally however, an eArchive service is available which automates the storage of clinical practice data in Cloud based archives.…”

“…The effect of these functions highlights the impact of cumulative errors resulting from transformation functions. Ballou and Pazer's (1985) observation that the influence of errors may be diminished or accentuated by functional processes brings into sharp focus the implications of minimising manual (human) activities. The literature identifies human error as a principal cause of data problems (Experian Information Systems, 2015), with a high degree of errors occurring at transition points in the care process (Cain & Haque, 2008).…”