Abstract: Distributed computing infrastructures are evolving from traditional models to environments that involve sensors, edge devices, instruments, etc, and, as well, high-end computing systems such as clouds and HPC clusters. A key aspect is how to describe and develop the applications to be executed in such platforms. What is more, the use of data analytics and artificial intelligence in general is on high demand in current HPC applications. However, the methodologies to develop such workflows that integrate HPC simulations and data analytics is not well integrated. eFlows4HPC project aims at providing workflow software stack and an additional set of services to enable the integration of HPC simulations and modelling with big data analytics and machine learning in scientific and industrial applications. The project is also developing the HPC Workflows as a Service (HPCWaaS) methodology that aims at providing tools to simplify the development, deployment, execution and reuse of workflows. The project will demonstrate its advances through three application Pillars with high industrial and social relevance: manufacturing, climate and urgent computing for natural hazards; these applications will help to prove how the realization of forthcoming efficient HPC and data-centric applications can be developed with new workflow technologies. The talk will present the motivation, challenges and project workplan.