A Model for Data Quality Assessment

Piprani, Baba; Ernst, Denise

doi:10.1007/978-3-540-88875-8_99

Cited by 21 publications

(14 citation statements)

References 3 publications

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“…Poor quality data can, therefore, have significantly negative impacts on the efficiency of an organization, while high quality data are often crucial to a company's success (Madnick et al, 2004;Haug et al, 2009;Batini et al, 2009;Even & Shankaranarayanan, 2009). However, several industry expert surveys indicate that data quality is an area, to which many companies seem not to give sufficient attention or know how to deal with efficiently (Marsh, 2005;Piprani & Ernst, 2008;Jing-hua et al, 2009). Vayghan et al (2007) classify the data that most enterprises deal with in three categories: master data, transactional data, and historical data.…”

Section: Introductionmentioning

confidence: 99%

The costs of poor data quality

Haug

Zachariassen²,

Liempd

2011

JIEM

113

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Purpose: The technological developments have implied that companies store increasingly more data. However, data quality maintenance work is often neglected, and poor quality business data constitute a significant cost factor for many companies. This paper argues that perfect data quality should not be the goal, but instead the data quality should be improved to only a certain level. The paper focuses on how to identify the optimal data quality level.Design/methodology/approach: The paper starts with a review of data quality literature. On this basis, the paper proposes a definition of the optimal data maintenance effort and a classification of costs inflicted by poor quality data. These propositions are investigated by a case study.Findings: The paper proposes: (1) a definition of the optimal data maintenance effort and (2) a classification of costs inflicted by poor quality data. A case study illustrates the usefulness of these propositions.Research limitations/implications: The paper provides definitions in relation to the costs of poor quality data and the data quality maintenance effort. Future research may build on these definitions. To further develop the contributions of the paper, more studies are needed.

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Section: Introductionmentioning

confidence: 99%

The costs of poor data quality

Haug

Zachariassen²,

Liempd

2011

JIEM

113

View full text Add to dashboard Cite

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“…The second of the five factors relates to problematic aspects of the data. In respect of AI-based data analytics, the quality of outcomes is dependent on many features of data that need to reach a threshold of quality before they can be reliably used to draw inferences (Wang & Strong, 1996;Shanks & Darke, 1998;Piprani & Ernst, 2008; summarised in Clarke 2016 into 13 factors).…”

Section: Disbenefits and Risks Of Aimentioning

confidence: 99%

Responsible application of artificial intelligence to surveillance: What prospects?1

Clarke

2022

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Artificial Intelligence (AI) is one of the most significant of the information and communications technologies being applied to surveillance. AI’s proponents argue that its promise is great, and that successes have been achieved, whereas its detractors draw attention to the many threats embodied in it, some of which are much more problematic than those arising from earlier data analytical tools. This article considers the full gamut of regulatory mechanisms. The scope extends from natural and infrastructural regulatory mechanisms, via self-regulation, including the recently-popular field of ‘ethical principles’, to co-regulatory and formal approaches. An evaluation is provided of the adequacy or otherwise of the world’s first proposal for formal regulation of AI practices and systems, by the European Commission. To lay the groundwork for the analysis, an overview is provided of the nature of AI. The conclusion reached is that, despite the threats inherent in the deployment of AI, the current safeguards are seriously inadequate, and the prospects for near-future improvement are far from good. To avoid undue harm from AI applications to surveillance, it is necessary to rapidly enhance existing, already-inadequate safeguards and establish additional protections.

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“…(), van der Pijl (), Wand and Wang (), Wang and Strong (), Shanks and Darke (), Shanks and Corbitt (), Rusbridge et al . (), English (), and Piprani and Ernst (). An ISO 8000 series of international data quality standards has been slowly emergent for some time (Benson, ), but its value is limited because of its naive model of data and information.…”

Section: Big Data Qualitymentioning

confidence: 99%

Big data, big risks

Clarke

2015

Information Systems Journal

161

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The 'big data' literature, academic as well as professional, has a very strong focus on opportunities. Far less attention has been paid to the threats that arise from repurposing data, consolidating data from multiple sources, applying analytical tools to the resulting collections, drawing inferences, and acting on them. On the basis of a review of quality factors in 'big data' and 'big data analytics', illustrated by means of scenario analysis, this paper draws attention to the moral and legal responsibility of computing researchers and professionals to temper their excitement, and apply reality checks to their promotional activities.

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A Model for Data Quality Assessment

Cited by 21 publications

References 3 publications

The costs of poor data quality

The costs of poor data quality

Responsible application of artificial intelligence to surveillance: What prospects?1

Big data, big risks

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