## 5 Discussion
### What Stands in the Way of Application?  
Most technologies presented in this article are not completely new. This raises the question why contextualization still is a challenge. Why are the available technologies not used as much as they could be? Several reasons might account for this. First, using the technologies can be _difficult and effortful_. For instance, eliciting high-quality models of system relations from domain experts is a major effort [167]. In complex industrial systems, models are incomplete and not even engineers can fully specify them [168; 169]. For instance, it is impossible to exhaustively describe the behavior of chocolate under all conditions that might occur in a plant. The modelling of system relations gets even more difficult when models have to bridge domain boundaries (e.g., interactions of chocolate characteristics and machines). Some people hope that purely data-driven approaches such as machine learning can compensate for a limited understanding of the underlying processes. However, statistical associations are not always informative. A promising alternative is to use causal models that combine data based on relatively simple causal diagrams by performing particular computations [98]. Initial evidence suggests that such models can produce better predictions than searching for statistical associations and controlling for known covariates [170]. Concerns about feasibility also apply to purely data-driven approaches. If data are to be integrated, they must be available in the first place.  
However, lots of important data (e.g., state of packaging material) are not measurable in principle, in-line, or at a reasonable price [171]. Additionally, simply acquiring more data is insufficient if their quality is not guaranteed, as data can have biases, ambiguities, and inaccuracies [172].  
A second reason that people do not use the full potentials of digital transformation technologies is that they are _not used to it_. For instance, despite the invaluable power of the semantics offered by OPC UA, current controllers do not provide sematic information models [173]. They do use OPC UA, but in the same way as they have previously used list-based fieldbus standards. Concepts and technologies that are established in other domains (e.g., ontologies from the area of knowledge engineering) are only gradually being adopted, and it takes time for industrial practitioners to recognize their potential.  
A third, similar reason is that engineers often _do not know how to apply_ the technologies in ways that bring out their benefits for human-machine interaction. For instance, it is unclear how to use ontologies in ways that help people think (e.g., encourage them to consider alternative hypotheses). Psychological knowledge is not usually considered in the industrial application of technologies. Moreover, a psychologically valuable application of ontologies calls for immense formalization efforts and sophisticated querying mechanisms. Therefore, applying digital transformation technologies to optimize human-machine interaction will require two things: technological advances to support the people who conceptualize and implement these technologies, and a close cooperation between different disciplines such as computer scientists, engineers, and psychologists.