  Business analytics refers to methods and practices that create value through
data for individuals, firms, and organizations. This field is currently
experiencing a radical shift due to the advent of deep learning: deep neural
networks promise improvements in prediction performance as compared to models
from traditional machine learning. However, our research into the existing body
of literature reveals a scarcity of research works utilizing deep learning in
our discipline. Accordingly, the objectives of this overview article are as
follows: (1) we review research on deep learning for business analytics from an
operational point of view. (2) We motivate why researchers and practitioners
from business analytics should utilize deep neural networks and review
potential use cases, necessary requirements, and benefits. (3) We investigate
the added value to operations research in different case studies with real data
from entrepreneurial undertakings. All such cases demonstrate improvements in
operational performance over traditional machine learning and thus direct value
gains. (4) We provide guidelines and implications for researchers, managers and
practitioners in operations research who want to advance their capabilities for
business analytics with regard to deep learning. (5) Our computational
experiments find that default, out-of-the-box architectures are often
suboptimal and thus highlight the value of customized architectures by
proposing a novel deep-embedded network.
