Towards integrated Data Analysis Quality: Criteria for the application of Industrial Data Science

West, Nikolai; Gries, Jonas; Brockmeier, Carina; Göbel, Jens Christian; Deuse, Jochen

doi:10.1109/iri51335.2021.00024

Cited by 26 publications

(6 citation statements)

References 14 publications

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“…As Industry 4.0 progresses, data science approaches have become essential for decision-making within the manufacturing environment. [2,13]. This underscores the need for deep integration into business processes and empowering the individuals who operate and manage those processes.…”

Section: A Special Role Of Data Science and Quality Managementmentioning

confidence: 99%

Modular Data Analytics as a Tool for Citizen Data Scientists in Quality Management

Pähler,

Syberg,

Deuse

2024

Preprint

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The vision of Industrial Data Science to link data along the entire value chain with data from the product life cycle contains great potential for holistic quality improvements. In order to be able to exploit this increasing potential, tools and evaluations as well as the qualification of experts in quality management in the field of Industrial Data Science are essential. A promising approach is the generalisation and modularisation of data analysis processes, which can be reused in Quality Management for similar analytics tasks of different data sources in this discipline. With an appropriate integration into the company’s processes and easy accessibility of the generalized analytics modules, this approach promises to reduce the complexity and difficulty of data analysis. In this paper a total of seven different exemplary analysis modules that quality management experts can use to handle, visualise and analyse new data in Field Quality Surveillance. These are provided as part of a research project via a browser-based platform that enables users to perform data analyses without the use of other software or programming knowledge. The analysis modules are a central building block for supporting Quality Management experts to act as Citizen Data Scientists. In addition, skills training and general change management for organisational restructuring must go hand in hand with the use of the modules in order to achieve valid analytics results quickly.

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Section: A Special Role Of Data Science and Quality Managementmentioning

confidence: 99%

Modular Data Analytics as a Tool for Citizen Data Scientists in Quality Management

Pähler,

Syberg,

Deuse

2024

Preprint

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“…Effective data quality assessment is based on a consumer's (application) fitness for use requirements, and indeed, this changes from one user to another [13]. Previous research [14,15] has used the phrase fitness for use and data quality interchangeably.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Existing solutions consider data quality assurance to be a necessary step at a single stage (data preprocessing) [13], applied like a Quality Gate with a predefined end point. Once the objective is satisfied, they assume a lasting standard of data quality [13]. It is, however, a common practice in most IoT deployments to use the same data for different applications and use cases, or enrich the data with external data sources, hence different fitness for use requirements.…”

Section: Introductionmentioning

confidence: 99%

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IoT Data Quality Assessment Framework Using Adaptive Weighted Estimation Fusion

Byabazaire

O’Hare

Collier

et al. 2023

Sensors

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Timely data quality assessment has been shown to be crucial for the development of IoT-based applications. Different IoT applications’ varying data quality requirements pose a challenge, as each application requires a unique data quality process. This creates scalability issues as the number of applications increases, and it also has financial implications, as it would require a separate data pipeline for each application. To address this challenge, this paper proposes a novel approach integrating fusion methods into end-to-end data quality assessment to cater to different applications within a single data pipeline. By using real-time and historical analytics, the study investigates the effects of each fusion method on the resulting data quality score and how this can be used to support different applications. The study results, based on two real-world datasets, indicate that Kalman fusion had a higher overall mean quality score than Adaptive weighted fusion and Naïve fusion. However, Kalman fusion also had a higher computational burden on the system. The proposed solution offers a flexible and efficient approach to addressing IoT applications’ diverse data quality needs within a single data pipeline.

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“…DQ aims to measure the suitability of the data to produce meaningful information and the ease with which it can be processed. [6]. It also refers to the ability to meet the needs and expectations of data consumers [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Towards Reliable Collaborative Data Processing Ecosystems: Survey on Data Quality Criteria

Sahi,

Laborde,

Kandi

et al. 2023

2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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Data quality plays a crucial role in the data governance of organizations, as it is essential to ensure that data are fit for the purpose for which they are intended, whether for operational activities, decision-making processes, or strategic planning. As data silos begin to be integrated to form data spaces, guaranteeing data quality becomes a necessity to achieve a reliable collaborative ecosystem. Nevertheless, the concept of data quality remains ambiguous, with various definitions and interpretations offered in the literature, despite its importance. This lack of consensus has led to the need for a thorough review of the different data quality criteria used in scientific work. Therefore, this paper serves as a systematic survey aimed at exploring and consolidating diverse perspectives on data quality. By thoroughly analyzing existing literature, this study compiles a comprehensive set of 30 agreed-upon data quality criteria, with their respective names and definitions. These criteria act as a valuable resource for organizations seeking to establish effective data quality monitoring practices. Then, we expose challenges raised by collaborative data processing and highlight possible research directions where data quality plays a major role.

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Towards integrated Data Analysis Quality: Criteria for the application of Industrial Data Science

Cited by 26 publications

References 14 publications

Modular Data Analytics as a Tool for Citizen Data Scientists in Quality Management

Modular Data Analytics as a Tool for Citizen Data Scientists in Quality Management

IoT Data Quality Assessment Framework Using Adaptive Weighted Estimation Fusion

Towards Reliable Collaborative Data Processing Ecosystems: Survey on Data Quality Criteria

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