Metric-based data quality assessment — Developing and evaluating a probability-based currency metric

Heinrich, Bernd; Klier, Mathias

doi:10.1016/j.dss.2015.02.009

Cited by 35 publications

(30 citation statements)

References 30 publications

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“…The benefits of applying a data quality metric in this context are generally non-negligible. Indeed, considering the quality of the customer data (as discussed by [Even et al 2010] and [Heinrich and Klier 2015]) will lead to better decisions (e.g., if an offer is sent to an outdated or incomplete address, this will only cause mailing costs).…”

Section: Application Of the Requirementsmentioning

confidence: 99%

“…Heinrich et al [2012], for example, propose a metric for the data quality dimension currency [cf. also Heinrich and Klier 2015]. The metric values represent probabilities that the data values under consideration still correspond to their real-world value at the instant of assessing data quality.…”

Section: Table 2 Decision Matrixmentioning

confidence: 99%

“…To make the evaluation of the metrics more transparent and comprehensible, we refer to the following context of application [cf. Even et al 2010;Heinrich and Klier 2015]: Based on the stored data of existing customers (e.g., corporate customers), a company has to decide which customers to contact with a new product offer in a CRM mailing campaign. The two decision alternatives for the company with respect to each customer in the database are 1 : to select the customer for the campaign or 2 : not to do so.…”

Section: Application Of the Requirementsmentioning

confidence: 99%

“…Each data quality dimension provides a par-ticular perspective on the quality of data views. As a result, researchers have developed corresponding metrics for the quantitative assessment of these dimensions for data views [e.g., Ballou et al 1998;Hinrichs 2002;Even and Shankaranarayanan 2007;Heinrich et al 2007;Fisher et al 2009;Heinrich et al 2009;Blake and Mangiameli 2011;Heinrich et al 2012;Wechsler and Even 2012;Heinrich and Klier 2015;Heinrich and Hristova 2016]. Metrics assessing such data quality dimensions for data views and data values stored in IS are in the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

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Requirements for Data Quality Metrics

Heinrich

Hristova

Klier

et al. 2017

J. Data and Information Quality

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Data quality and especially the assessment of data quality have been intensively discussed in research and practice alike. To support an economically oriented management of data quality and decision-making under uncertainty, it is essential to assess the data quality level by means of well-founded metrics. However, if not adequately defined, these metrics can lead to wrong decisions and economic losses. Therefore, based on a decision-oriented framework, we present a set of five requirements for data quality metrics. These requirements are relevant for a metric that aims to support an economically oriented management of data quality and decision-making under uncertainty. We further demonstrate the applicability and efficacy of these requirements by evaluating five data quality metrics for different data quality dimensions. Moreover, we discuss practical implications when applying the presented requirements.

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Section: Application Of the Requirementsmentioning

confidence: 99%

Section: Table 2 Decision Matrixmentioning

confidence: 99%

Section: Application Of the Requirementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Requirements for Data Quality Metrics

Heinrich

Hristova

Klier

et al. 2017

J. Data and Information Quality

Self Cite

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“…Data quality has long been recognized as an important issue in information systems [1][2][3]. Poor data quality often prevents information systems from yielding satisficing performance [4], which severely impacts system robustness.…”

Section: Introductionmentioning

confidence: 99%

Robustness Management with Data Quality in Information Systems

Song¹,

Sun

2016

IJHIT

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DMN for Data Quality Measurement and Assessment

Valencia-Parra

Parody

Varela-Vaca

et al. 2019

Lecture Notes in Business Information Processing

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Data Quality assessment is aimed at evaluating the suitability of a dataset for an intended task. The extensive literature on data quality describes the various methodologies for assessing data quality by means of data profiling techniques of the whole datasets. Our investigations are aimed to provide solutions to the need of automatically assessing the level of quality of the records of a dataset, where data profiling tools do not provide an adequate level of information. As most of the times, it is easier to describe when a record has quality enough than calculating a qualitative indicator, we propose a semi-automatically business rule-guided data quality assessment methodology for every record. This involves first listing the business rules that describe the data (data requirements), then those describing how to produce measures (business rules for data quality measurements), and finally, those defining how to assess the level of data quality of a data set (business rules for data quality assessment). The main contribution of this paper is the adoption of the OMG standard DMN (Decision Model and Notation) to support the data quality requirement description and their automatic assessment by using the existing DMN engines.

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Metric-based data quality assessment — Developing and evaluating a probability-based currency metric

Cited by 35 publications

References 30 publications

Requirements for Data Quality Metrics

Requirements for Data Quality Metrics

Robustness Management with Data Quality in Information Systems

DMN for Data Quality Measurement and Assessment

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