Evaluation model for the analysis of data maturity

Eickelmann, M; Wiegand, M; Deuse, J; Bernerstätter, R

Evaluation model for the analysis of data maturity

Eickelmann, M Wiegand, M Deuse, J Bernerstätter, R

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Publisher:: Carl Hanser Verlag
Publication Type:: Journal Article
Citation:: ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, 2019, 114, (1-2), pp. 29-33
Issue Date:: 2019-02-01

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Field	Value	Language
dc.contributor.author	Eickelmann, M
dc.contributor.author	Wiegand, M
dc.contributor.author	Deuse, J
dc.contributor.author	Bernerstätter, R
dc.date.accessioned	2020-06-16T23:28:16Z
dc.date.available	2020-06-16T23:28:16Z
dc.date.issued	2019-02-01
dc.identifier.citation	ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, 2019, 114, (1-2), pp. 29-33
dc.identifier.issn	0947-0085
dc.identifier.issn	0947-0085
dc.identifier.uri	http://hdl.handle.net/10453/141460
dc.description.abstract	© Carl Hanser Verlag GmbH & Co. KG. Determination of the data maturity level for the application of machine learning methods in industrial manufacture. The digital transformation of business processes leads to a steady increase in available data. For the efficiently use of knowledge hidden in the data, companies are striving for the application of machine learning methods. Data quality is of outstanding importance for the applicability of machine learning methods and the quality of the results. This paper presents a model for the derivation of data maturity, to evaluate the chances of success of industrial data analytics projects and identify necessary steps for the improvement of data maturity.
dc.language	de
dc.publisher	Carl Hanser Verlag
dc.relation.ispartof	ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
dc.relation.isbasedon	10.3139/104.112037
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Industrial Engineering & Automation
dc.title	Evaluation model for the analysis of data maturity
dc.type	Journal Article
utslib.citation.volume	114
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-16T23:28:12Z
pubs.issue	1-2
pubs.publication-status	Published
pubs.volume	114
utslib.citation.issue	1-2

Abstract:

© Carl Hanser Verlag GmbH & Co. KG. Determination of the data maturity level for the application of machine learning methods in industrial manufacture. The digital transformation of business processes leads to a steady increase in available data. For the efficiently use of knowledge hidden in the data, companies are striving for the application of machine learning methods. Data quality is of outstanding importance for the applicability of machine learning methods and the quality of the results. This paper presents a model for the derivation of data maturity, to evaluate the chances of success of industrial data analytics projects and identify necessary steps for the improvement of data maturity.

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

http://hdl.handle.net/10453/141460