Increasing digitization leads to a constantly growing amount of data in a wide variety of application domains. Data analytics, including in particular machine learning, plays the key role to gain actionable insights from this data in a variety of domains and real-world applications. However, configuration of data analytics workflows that include heterogeneous data sources requires significant data science expertise, which hinders wide adoption of existing data analytics frameworks by non-experts. In this paper we present the Simple-ML framework that adopts semantic technologies, including in particular domain-specific semantic data models and dataset profiles, to support efficient configuration, robustness and reusability of data analytics workflows. We present semantic data models that lay the foundation for the framework development and discuss the data analytics workflows based on these models. Furthermore, we present an example instantiation of the Simple-ML data models for a real-world use case in the mobility application domain and discuss the emerging challenges.