Definition and Evaluation of Data Quality: User-Oriented Data Object-Driven Approach to Data Quality Assessment

Nikiforova, Anastasija

doi:10.22364/bjmc.2020.8.3.02

Cited by 25 publications

(27 citation statements)

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“…Nikiforova [5] combined the definitions above and defines data quality as the suitability of a given data set and its properties for a particular usage or use case, which depends on the data consumer using them, for example, in analytics, making business decisions, planning, etc.. Low-quality data profoundly influence the quality of business processes. It is recognized as a relevant performance issue of operating processes of decision-making activities and of inter-organizational cooperation requirements [6].…”

Section: A Data Quality Definitionmentioning

confidence: 99%

“…The presence of incorrect or inconsistent data can significantly distort the results of analyses, often negating the potential benefits of information-driven approaches [7]. Based on these definitions with corresponding effects of low data quality, the definition of [5] is used as it emphasizes the importance of taking a consumer viewpoint of quality. Without ignoring the effects, low data quality has across teams and organizational boundaries.…”

Section: A Data Quality Definitionmentioning

confidence: 99%

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Unsupervised Anomaly Detection in Data Quality Control

Poon

Farshidi

et al. 2021

2021 IEEE International Conference on Big Data (Big Data)

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Data is one of the most valuable assets of an organization and has a tremendous impact on its long-term success and decision-making processes. Typically, organizational data error and outlier detection processes perform manually and reactively, making them time-consuming and prone to human errors. Additionally, rich data types, unlabeled data, and increased volume have made such data more complex. Accordingly, an automated anomaly detection approach is required to improve data management and quality control processes. This study introduces an unsupervised anomaly detection approach based on models comparison, consensus learning, and a combination of rules of thumb with iterative hyper-parameter tuning to increase data quality. Furthermore, a domain expert is considered a human in the loop to evaluate and check the data quality and to judge the output of the unsupervised model. An experiment has been conducted to assess the proposed approach in the context of a case study. The experiment results confirm that the proposed approach can improve the quality of organizational data and facilitate anomaly detection processes.

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Section: A Data Quality Definitionmentioning

confidence: 99%

Section: A Data Quality Definitionmentioning

confidence: 99%

Unsupervised Anomaly Detection in Data Quality Control

Poon

Farshidi

et al. 2021

2021 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

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“…This assessment appears to be one of the most up-to-date assessments covering set of open data portals, as they have published results for 2019. According to their ISBN: 978-989-8704-20-7 © 2020 analysis, impact is currently the weakest aspect of OGDs, following by quality (partially addressed in (Nikiforova, 2020a)) and portal. As for the portal category, they focus on evaluation of portal functions that allow both versed and less versed users to access open data through the national portal and features that encourage interaction between publishers and re-users.…”

Section: Related Workmentioning

confidence: 99%

Assessment of the Usability of Latvia's Open Data Portal or How Close Are We to Gaining Benefits From Open Data

2020

Proceedings of the 14 Th International Conference on Interfaces and Human Computer Interaction 2020 and 13 Th International Con

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Nowadays, more and more countries are launching their own open data portals, seeking to provide their citizens with open data in a form that is useful and suitable for the original purpose of the open data, and Latvia is not an exception. Despite the fact that the Latvian open data portal was launched only in 2017, it is considered to be a fast-tracker. However, despite the overall high evaluations, critical voices, and many discussions about whether the Latvia's open data portal is of sufficient quality to be appeared. Therefore, while previous studies deal with quality of open data, this study focuses on the analysis of the Latvian open data portal and aims to find the key challenges that may have a negative impact on user experience. The paper assesses the current situation and recommends corrective actions, highlighting the aspects to be considered when developing and improving open data portals.

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“…Sometimes only limited data sets with low quality are published (Charalabidis et al, 2018). The fact is that open data portals rarely check the quality of their open data sets (Nikiforova, 2020a). On the other hand, technical issues in published data sets (e.g.…”

Section: Introductionmentioning

confidence: 99%

Systematic and ontology-based approach to interoperable cross-domain open government data services

Masoumi

Farahani

Aliee

2021

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Purpose Open government data (OGD) has emerged as a radical paradigm shift and endeavor among government administrations across the world mainly due to its promises of transparency, accountability, public-private collaboration, civic participation, social innovation and data-driven value creation. Complexity, cross-cutting nature, diversity of data sets, interoperability and quality issues usually hamper unlocking the full potential value of data. To tackle these challenges, this paper aims to provide a novel solution using a top-down approach. Design/methodology/approach In this paper, the authors propose a systematic ontology-based approach combined with a novel architecture and its corresponding processes enabling organizations to carry out all the steps in the OGD value chain. In addition, an OGD Platform including a portal (www.iranopendata.ir) and a data management system (www.ogdms.iranopendata.ir) are developed to showcase the proposed solution. Findings The efficiency and the applicability of the solution are evaluated by a real-life use case on energy consumption of the buildings of the city of Tehran, Iran. Finally, a comparison was made with existing solutions, and the results show the proposed approach is able to address the existing gaps in the literature. Originality/value The results imply that modeling and designing the data model, as well as exploiting an ontology-based approach are critical pillars to create rich, relevant and well-described OGD data sets. Moreover, clarity on processes, roles and responsibilities are the key factors influencing the quality of the published data services. Thus, to the best of the knowledge, this is the first study that exploits and considers an ontology-based approach in a top-down manner to create OGD data sets.

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Definition and Evaluation of Data Quality: User-Oriented Data Object-Driven Approach to Data Quality Assessment

Cited by 25 publications

References 0 publications

Unsupervised Anomaly Detection in Data Quality Control

Unsupervised Anomaly Detection in Data Quality Control

Assessment of the Usability of Latvia's Open Data Portal or How Close Are We to Gaining Benefits From Open Data

Systematic and ontology-based approach to interoperable cross-domain open government data services

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