  The implementation of robust, stable, and user-centered data analytics and
machine learning models is confronted by numerous challenges in production and
manufacturing. Therefore, a systematic approach is required to develop,
evaluate, and deploy such models. The data-driven knowledge discovery framework
provides an orderly partition of the data-mining processes to ensure the
practical implementation of data analytics and machine learning models.
However, the practical application of robust industry-specific data-driven
knowledge discovery models faces multiple data-- and model-development--related
issues. These issues should be carefully addressed by allowing a flexible,
customized, and industry-specific knowledge discovery framework; in our case,
this takes the form of the cross-industry standard process for data mining
(CRISP-DM). This framework is designed to ensure active cooperation between
different phases to adequately address data- and model-related issues. In this
paper, we review several extensions of CRISP-DM models and various
data-robustness-- and model-robustness--related problems in machine learning,
which currently lacks proper cooperation between data experts and business
experts because of the limitations of data-driven knowledge discovery models.
