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.

翻译：成功的分析解决方案能够提供有价值的洞察，这通常依赖于多个数据源的连接。虽然组织内部生成更大数据池通常是可行的，但在（跨组织）商业网络中应用分析仍受到严重限制。由于数据分布在不同法律实体之间，甚至可能跨越国界，对泄露敏感信息的担忧以及需要交换的数据量庞大，是创建有效的系统级解决方案的主要障碍——同时还要保持卓越的预测性能。在这项工作中，我们提出了一种元机器学习方法，旨在克服这些障碍，以实现商业网络内的全面分析。我们遵循设计科学研究方法，并在工业用例中评估了我们的方法在可行性和性能方面的表现。首先，我们证明了在网络范围内进行分析是可行的，这既能保护数据机密性，又能限制数据传输量。其次，我们表明，我们的方法优于传统的孤立分析，甚至接近（假设性的）所有数据都可在网络内共享的场景。因此，我们为提升商业网络的效率做出了基础性贡献，因为消除了利用分散在网络中各处的数据进行学习这一巨大潜力的关键障碍。