In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making.

在大多数执行领域中，大多数战略决策都涉及常态的概念。然而，支持战略决策的数据驱动工具很少。我们引入并扩展了自动编码器的使用，以提供具有战略意义的细粒度反馈。第一个实验表明专家的决策不一致，突出了战略决策支持的必要性。此外，使用两个由行业提供的大型人力资源数据集，在排名准确性、与人类专家的协同作用和维度级反馈方面对所提出的解决方案进行了评估。该三点方案使用 (a) 合成数据、(b) 数据质量角度、(c) 盲专家验证和 (d) 透明专家评估进行验证。我们的研究证实了人类决策的几个主要弱点，并强调了模型与人类之间协同作用的重要性。此外，无监督学习，特别是自动编码器被证明是战略决策的宝贵工具。