A Data Quality in Use Model for Big Data

Caballero, Ismael; Serrano, Manuel; Piattini, Mario

doi:10.1007/978-3-319-12256-4_7

Cited by 37 publications

(20 citation statements)

References 7 publications

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“…To discovery valuable knowledge and fully realize the business potential of energy big data, various big data analytics techniques, such as data quality evaluation and modeling [103][104][105], data clustering and classification [68,[106][107][108][109], stream data processing [110][111][112], knowledge inference [113,114], statistical machine learning [115], neural networks modeling and deep learning [116,117], can be implemented on the data. The objective of energy big data analytics is to develop more effective and efficient data-driven applications and services.…”

Section: Energy Big Data Driven Applications In Energy Internetmentioning

confidence: 99%

Energy Internet: The business perspective

2016

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Section: Energy Big Data Driven Applications In Energy Internetmentioning

confidence: 99%

Energy Internet: The business perspective

2016

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“…In other cases, other terms have been used to describe the same dimension of accuracy. Accuracy has been referred to as precision and also semantic accuracy [23]. Dong et al [24] explained the notion of accuracy as 'true value' in the context of data fusion, which refers to a process of integrating data from different sources together while maintaining a standard of data quality.…”

Section: Interpretabilitymentioning

confidence: 99%

Data Governance in the Health Industry: Investigating Data Quality Dimensions within a Big Data Context

Juddoo

George

Duquenoy

et al. 2018

ASI

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In the health industry, the use of data (including Big Data) is of growing importance. The term ‘Big Data’ characterizes data by its volume, and also by its velocity, variety, and veracity. Big Data needs to have effective data governance, which includes measures to manage and control the use of data and to enhance data quality, availability, and integrity. The type and description of data quality can be expressed in terms of the dimensions of data quality. Well-known dimensions are accuracy, completeness, and consistency, amongst others. Since data quality depends on how the data is expected to be used, the most important data quality dimensions depend on the context of use and industry needs. There is a lack of current research focusing on data quality dimensions for Big Data within the health industry; this paper, therefore, investigates the most important data quality dimensions for Big Data within this context. An inner hermeneutic cycle research approach was used to review relevant literature related to data quality for big health datasets in a systematic way and to produce a list of the most important data quality dimensions. Based on a hierarchical framework for organizing data quality dimensions, the highest ranked category of dimensions was determined.

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“…Although models for assessing DQ exist, DQ models in the field of metadata are scarce, however, to bridge this gap in the literature, [29] provided a classification based on user's assessment or on information quality criteria, identifying three sources of metadata information quality: user, source, and query process. Based on ISO25012 and ISO25024, [33] created the 3C model consisting of three DQ dimensions, namely contextual consistency, operational consistency, and temporal consistency, which are used to assess the quality of data used in a metadata set. In another, [34] presented the 3A model, which includes three DQ characteristics used to assess data quality level in huge projects: contextual adequacy, temporal adequacy, and operational adequacy.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Importance of Data Factors of Data Quality Model in the Business Intelligence Area

Hejazi¹,

Abdolvand²,

Harandi³

2017

IJTEF

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Abstract-Business Intelligence (BI) is defined as the intelligent transfer of data from all variety of sources with a view to providing a real time quantification of business performance. Moreover, by utilizing BI senior managers are able to make better-informed decisions, guided by the integration of comprehensive information from all facets of an organization in real time. Further, the quality of corporate decision making is directly dependent upon the quality of data. Quality dimensions have proven to be of importance in evaluating how an organization understands and manages its data. Hence, in this study we employed the data quality model of Wang and Strong as our benchmark, and by questioning experts five additional factors (Usefulness, Efficiency, Availability, Navigation, and Usability) were added to the model to improve the reference model. Furthermore, the importance of these factors (and how they relate to each other, in terms of their dimensions) will be measured by the AHP method and an expert panel. Finally, we will reveal how the findings demonstrate that the intrinsic, contextual, representational and accessibility IQs in terms of their dimensions and reputation, accuracy, believability, and reliability, proved to be the most important factors.. Index Terms-Business intelligence, data, data quality, data quality model.

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A Data Quality in Use Model for Big Data

Cited by 37 publications

References 7 publications

Energy Internet: The business perspective

Energy Internet: The business perspective

Data Governance in the Health Industry: Investigating Data Quality Dimensions within a Big Data Context

Assessing the Importance of Data Factors of Data Quality Model in the Business Intelligence Area

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