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Enabling inter-organizational analytics in business networks through meta machine learning

Hirt, Robin; Kühl, Niklas ORCID iD icon; Martin, Dominik ORCID iD icon 1; Satzger, Gerhard 1
1 Karlsruhe Service Research Institute (KSRI), Karlsruher Institut für Technologie (KIT)


Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained. As data is distributed across several legal units, potentially even across countries, the fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions—all while still reaching superior prediction performance. In this work, we propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network. We follow a design science research approach and evaluate our method with respect to feasibility and performance in an industrial use case. First, we show that it is feasible to perform network-wide analyses that preserve data confidentiality as well as limit data transfer volume. Second, we demonstrate that our method outperforms a conventional isolated analysis and even gets close to a (hypothetical) scenario where all data could be shared within the network. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000160032
Veröffentlicht am 03.07.2023
DOI: 10.1007/s10799-023-00399-7
Zitationen: 2
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 1385-951X, 1573-7667
KITopen-ID: 1000160032
Erschienen in Information technology and management
Verlag Springer
Vorab online veröffentlicht am 03.06.2023
Nachgewiesen in Dimensions
Web of Science
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