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

West, N; Gries, J; Brockmeier, C; Gobel, JC; Deuse, J

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

West, N Gries, J Brockmeier, C Gobel, JC Deuse, J

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI), 2021, 00, pp. 131-138
Issue Date:: 2021-11-18

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 18 Nov 2023

Adobe PDF

Download Accepted versionAdobe PDF (682.75 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	West, N
dc.contributor.author	Gries, J
dc.contributor.author	Brockmeier, C
dc.contributor.author	Gobel, JC
dc.contributor.author	Deuse, J https://orcid.org/0000-0003-4066-4357
dc.date	2021-08-10
dc.date.accessioned	2022-06-12T06:27:33Z
dc.date.available	2022-06-12T06:27:33Z
dc.date.issued	2021-11-18
dc.identifier.citation	2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI), 2021, 00, pp. 131-138
dc.identifier.isbn	9781665438759
dc.identifier.uri	http://hdl.handle.net/10453/158116
dc.description.abstract	The application of Industrial Data Science in context of connected Smart Products requires modeling and structuring data for its design, development and use. Especially for Smart Products, a comprehensive handling of data quality is mandatory, because of their interdisciplinary character and broad range of heterogeneous stakeholders covering the entire product lifecycle. The overall goal of data preparation is to provide high-quality data for application and evaluation by users. Established process models for industrial data analysis often treat the specification and assurance of data quality as a single-point activity with a defined conclusion. Providing end-to-end data quality has received little attention in the field of industrial data analytics. In this paper, we will (1) structure four distinct phases for ensuring end-to-end data quality along data analytics activities, (2) define a set of criteria and measures for meeting and quantifying data quality requirements based on established criteria, and (3) provide a step-by-step model for establishing and maintaining high Data Quality for Industrial Data Science applications. The quality criteria aim to identify pointwise and continuous actions during the data analysis process. Such criteria target a shared responsibility for maintaining data quality during analyses between analyst and user. The developed model provides an actionable approach for assessing and ensuring the requirements of Data Analysis Quality.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI)
dc.relation.ispartof	2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science
dc.relation.isbasedon	10.1109/iri51335.2021.00024
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Towards integrated Data Analysis Quality: Criteria for the application of Industrial Data Science
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Las Vegas, NV, USA
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-11-18T00:00:00+1000Z
dc.date.updated	2022-06-12T06:27:32Z
pubs.finish-date	2021-08-12
pubs.publication-status	Published
pubs.start-date	2021-08-10
pubs.volume	00

Abstract:

The application of Industrial Data Science in context of connected Smart Products requires modeling and structuring data for its design, development and use. Especially for Smart Products, a comprehensive handling of data quality is mandatory, because of their interdisciplinary character and broad range of heterogeneous stakeholders covering the entire product lifecycle. The overall goal of data preparation is to provide high-quality data for application and evaluation by users. Established process models for industrial data analysis often treat the specification and assurance of data quality as a single-point activity with a defined conclusion. Providing end-to-end data quality has received little attention in the field of industrial data analytics. In this paper, we will (1) structure four distinct phases for ensuring end-to-end data quality along data analytics activities, (2) define a set of criteria and measures for meeting and quantifying data quality requirements based on established criteria, and (3) provide a step-by-step model for establishing and maintaining high Data Quality for Industrial Data Science applications. The quality criteria aim to identify pointwise and continuous actions during the data analysis process. Such criteria target a shared responsibility for maintaining data quality during analyses between analyst and user. The developed model provides an actionable approach for assessing and ensuring the requirements of Data Analysis Quality.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/158116