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Hirt, Robin ; Kühl, Niklas ; Martin, Dominik ; Satzger, Gerhard:
Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning.
In: Information Technology & Management.
(2023)
.
ISSN 1573-7667
DOI: https://doi.org/10.1007/s10799-023-00399-7
Abstract
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. Thus, we provide a fundamental contribution for making
business networks more effective, as we remove a key obstacle to tap the huge potential of learning
from data that is scattered throughout the network.