Data representation factors and dimensions from the quality function                 deployment (QFD) perspective

Pinto, María

doi:10.1177/0165551506062325

Cited by 15 publications

(11 citation statements)

References 49 publications

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“…In general, we identified that there is a difference between the DQ assessment in the financial sector and other sectors. This result confirms that DQ depends on the context of the intended use (Chen and Tseng, 2010;Pinto, 2006). It has been shown that DQ problems increase when there is a large amount of data to be collected and managed (Parssian and Jacob, 2004).…”

Section: Basic Statistic Analysissupporting

confidence: 74%

A total data quality management for credit risk: new insights and challenges

Moges

Dejaeger

Lemahieu

et al. 2012

IJIQ

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Recent studies indicated that companies are increasingly experiencing data quality (DQ) related problems resulting from their increased data collection efforts. Addressing these concerns requires a clear definition of DQ but typically, DQ is only broadly defined as 'fitness for use'. While capturing its essence, a more precise interpretation of DQ is required during measurement. While there is a growing consensus on the multi-dimensional nature of DQ, no exact DQ definition has been put forward due to its context dependency. On the contrary, it is often stated that its constituting dimensions should be identified and defined in relation to the task at hand. Answering this call, we identify the DQ dimensions important to the credit risk assessment environment. In addition, we explore key DQ challenges and report on the causes of DQ problems in financial institutions. Statistical tests indicated nine most important DQ dimensions.

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Section: Basic Statistic Analysissupporting

confidence: 74%

A total data quality management for credit risk: new insights and challenges

Moges

Dejaeger

Lemahieu

et al. 2012

IJIQ

View full text Add to dashboard Cite

show abstract

“…In addition, they and their staff spend up to 30 percent % of their working time on checking the quality of data provided [6]. Here, ensuring completeness, correctness and currency of data -such properties are known as data quality dimensions [7] -still remains an important problem for many companies and public institutions [8][9][10][11][12][13]. But how good is an organization's data quality?…”

Section: Introductionmentioning

confidence: 99%

Assessing data currency — a probabilistic approach

Heinrich

Klier

2010

Journal of Information Science

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Abstract.The growing relevance of data quality has revealed the need for adequate measurement. As time aspects are extremely important in data quality management, we propose a novel approach to assess data currency. Our metric, which is founded on probability theory, enables an objective and widely automated assessment for data liable to temporal decline. Its values are easy to interpret by business users. Moreover, the metric makes it possible to analyse the economic impacts of data quality measures like data cleansing and can therefore build a basis for an economic management of data quality. The approach can be applied in various fields of application where the currency of data is important. To illustrate the practical benefit and the applicability of the novel metric, we provide an extensive realworld example. In cooperation with a major German mobile services provider, the approach was successfully applied in campaign management and led to an improved decision support.Keywords: data quality; data quality assessment; data quality metrics IntroductionBoth the benefit and the acceptance of information systems depend heavily on the quality of data provided by these systems [1][2][3]. Executives and employees need high-quality data in order to perform business, innovation, and decision-making processes properly [4,5]. It is, therefore, not surprising that bad data quality may lead to wrong decisions and correspondingly high costs. According to an international survey, 75 percent of all respondents have already made wrong decisions due to incorrect or outdated data. In addition, they and their staff spend up to 30 percent % of their working time on checking the quality of data provided [6]. Here, ensuring completeness, correctness and currency of data -such properties are known as data quality dimensions [7] -still remains an important problem for many companies and public institutions [8][9][10][11][12][13]. But how good is an organization's data quality? To answer this important question, well-founded and applicable metrics are needed [8,14,15]. In addition, assessing data quality (e.g. master data) is essential for analysing the economic effects of bad or improved data quality as well as for planning data quality measures in an economic manner. In this paper, we present a novel metric for currency, as empirical investigations reveal that time aspects are extremely

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“…Variables and attributes are proposed after a selection process regarding the existing literature (LANCASTER, 2003;HARTLEY et al, 1996;HARTLEY, 2002;HARTLEY et al, 2003;TIBBO, 1992) and our own research on the topic (PINTO, 1995(PINTO, , 2003(PINTO, , 2005. Within the first pragmatic stage two parallel fieldworks are developed around the previously selected sets of abstracting variables and abstract attributes.…”

Section: Methodsmentioning

confidence: 99%

A grounded theory on abstracts quality: Weighting variables and attributes

Pinto

2006

Scientometrics

Self Cite

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The goal is to deepen the knowledge of both sides of the abstract/ing topic: abstracting variables and abstract attributes. Six abstracting variables (representing abstract, represented source, abstracting means, documentary goal, cognitive domain and user needs) and eight abstract attributes (representativeness, comprehensiveness, usefulness, accuracy, consistency, coherence, density and perceived quality) are proposed and weighted. While abstracting means is uncovered as the main abstracting variable, the representativeness and accuracy attributes stand out, and usefulness, comprehensiveness, consistency, coherence and density are regarded as the basic ones. The feedback of this quality model is performed by the perceived quality attribute, which depends exclusively on users.

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Data representation factors and dimensions from the quality function deployment (QFD) perspective

Cited by 15 publications

References 49 publications

A total data quality management for credit risk: new insights and challenges

A total data quality management for credit risk: new insights and challenges

Assessing data currency — a probabilistic approach

A grounded theory on abstracts quality: Weighting variables and attributes

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