Requirements for Data Quality Metrics

Heinrich, Bernd; Hristova, Diana; Klier, Mathias; Schiller, Alexander; Szubartowicz, Michael

doi:10.1145/3148238

Cited by 79 publications

(68 citation statements)

References 49 publications

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“…Data quality is critical to promoting fruitful decision-making in schools [1,15,16,20,21]. However, it is difficult to define the concept because quality is a subjective notion that is "judged by the user in terms of its credibility and usefulness" [1] (p. 609).…”

Section: Data Qualitymentioning

confidence: 99%

“…The specific concepts are defined as follows: (1) data quality demands the KEIDS to keep high-quality, timely, accurate data [13,15,20,24,25,32]; (2) calibration addresses educators' data use in teaching and learning and how teachers conduct teaching under these definitions [3,4]; (3) principal leadership requires administrators to invest in and support the use of data systems and addresses whether school leaders encourage educators to directly access data [13,19,33]; (4) faculty involvement and collaboration question whether teachers are enthusiastic about the use of a data system and whether educators closely collaborate with each other for effective data use [2,3,35]; and (5) institutional support addresses the training and time provided to educators for accessing and examining data [2,16,46].…”

Section: Theoretical Framework For Facilitating Dbdmmentioning

confidence: 99%

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Promoting Sustainable Data-Based Decision-Making in the Korean Educational Information Disclosure System

Joo

2020

Sustainability

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This study investigates the Korean Educational Information Disclosure System (KEIDS) and suggests sustainable development policies for KEIDS to improve school-level data-based decision-making (DBDM) from the educational administration’s perspective. It also raises the following questions: What are the barriers impeding effective data use by the KEIDS? How do school teachers, who are directly involved in using data, effectively prepare for DBDM using the KEIDS? How can the KEIDS be improved for DBDM concerning quality data, school context, and institutional support? To answer these questions, the study reviewed KEIDS-related documents and interviewed 24 school teachers through an interpretive case study approach while using a research framework of data quality, school contexts, and institutional support. Its results highlight important issues with the KEIDS and sustainable DBDM, in other words, teachers and administrators are not always conscious of the need for using data; the lack of data use understanding creates issues among principal leadership and teachers’ involvement and cooperation; the quality of the student data in the Schoolinfo system is questionable; and the central education authority focuses on simply disclosing student data rather than pursuing the goal of the KEIDS. The study suggests facilitating DBDM through the KEIDS in terms of data quality, school context, and institutional support.

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Section: Data Qualitymentioning

confidence: 99%

Section: Theoretical Framework For Facilitating Dbdmmentioning

confidence: 99%

Promoting Sustainable Data-Based Decision-Making in the Korean Educational Information Disclosure System

Joo

2020

Sustainability

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“…From a methodological and decisionoriented perspective, both benefits can be estimated by comparing the effects resulting from decisions with respectively without considering the metric results and the improved data quality (for a detailed discussion cf. Heinrich & Hristova, 2016;Heinrich, Hristova, Klier, Schiller, & Szubartowicz, 2018).…”

Section: Fig 3 Core Steps To Instantiate and Apply The Proposed Metricmentioning

confidence: 99%

“…Product sales being caused by these additionally considered customers constitute a third benefit resulting from fixing identified inconsistencies (thus representing a succeeding effect of applying the metric). In addition, both metric results and improved data quality cannot only be used in a single campaign, but also in future campaigns and customer interactions resulting in further benefits dependent on the particular application context (for a general decision-oriented framework comprising efforts and benefits of data quality assessment, we refer to Heinrich et al 2018). Overall, in the case of the insurer, the efficiency can be supported; however, without any doubt efficiency has to be examined individually for each application context.…”

Section: Fig 3 Core Steps To Instantiate and Apply The Proposed Metricmentioning

confidence: 99%

Assessing data quality – A probability-based metric for semantic consistency

Heinrich

Klier

Schiller

et al. 2018

Decision Support Systems

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Abstract:We present a probability-based metric for semantic consistency using a set of uncertain rules. As opposed to existing metrics for semantic consistency, our metric allows to consider rules that are expected to be fulfilled with specific probabilities. The resulting metric values represent the probability that the assessed dataset is free of internal contradictions with regard to the uncertain rules and thus have a clear interpretation. The theoretical basis for determining the metric values are statistical tests and the concept of the p-value, allowing the interpretation of the metric value as a probability. We demonstrate the practical applicability and effectiveness of the metric in a real-world setting by analyzing a customer dataset of an insurance company. Here, the metric was applied to identify semantic consistency problems in the data and to support decision-making, for instance, when offering individual products to customers.

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“…[10,11,14]. Zaveri et al [15,16], have discussed sixty nine data quality metrics and all these may not be used in the context of single application data, some are important for one case that others will be more suitable in another context. Therefore, it is also a significant contribution to identify the required data quality metrics in the application's context and nature of data.…”

Section: Introductionmentioning

confidence: 99%

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

Jarwar

Chong²

2020

Applied Sciences

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Due to the convergence of advanced technologies such as the Internet of Things, Artificial Intelligence, and Big Data, a healthcare platform accumulates data in a huge quantity from several heterogeneous sources. The adequate usage of this data may increase the impact of and improve the healthcare service quality; however, the quality of the data may be questionable. Assessing the quality of the data for the task in hand may reduce the associated risks, and increase the confidence of the data usability. To overcome the aforementioned challenges, this paper presents the web objects based contextual data quality assessment model with enhanced classification metric parameters. A semantic ontology of virtual objects, composite virtual objects, and services is also proposed for the parameterization of contextual data quality assessment of web objects data. The novelty of this article is the provision of contextual data quality assessment mechanisms at the data acquisition, assessment, and service level for the web objects enabled semantic data applications. To evaluate the proposed data quality assessment mechanism, web objects enabled affective stress and teens’ mood care semantic data applications are designed, and a deep data quality learning model is developed. The findings of the proposed approach reveal that, once a data quality assessment model is trained on web objects enabled healthcare semantic data, it could be used to classify the incoming data quality in various contextual data quality metric parameters. Moreover, the data quality assessment mechanism presented in this paper can be used to other application domains by incorporating data quality analysis requirements ontology.

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Requirements for Data Quality Metrics

Cited by 79 publications

References 49 publications

Promoting Sustainable Data-Based Decision-Making in the Korean Educational Information Disclosure System

Promoting Sustainable Data-Based Decision-Making in the Korean Educational Information Disclosure System

Assessing data quality – A probability-based metric for semantic consistency

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

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