  Many modern software-intensive systems employ artificial intelligence /
machine-learning (AI/ML) components and are, thus, inherently data-centric. The
behaviour of such systems depends on typically large amounts of data processed
at run-time rendering such non-deterministic systems as complex. This
complexity growth affects our understanding on needs and practices in
Requirements Engineering (RE). There is, however, still little guidance on how
to handle requirements for such systems effectively: What are, for example,
typical quality requirements classes? What modelling concepts do we rely on or
which levels of abstraction do we need to consider? In fact, how to integrate
such concepts into approaches for a more traditional RE still needs profound
investigations. In this research preview paper, we report on ongoing efforts to
establish an artefact-based RE approach for the development of datacentric
systems (DCSs). To this end, we sketch a DCS development process with the newly
proposed requirements categories and data-centric artefacts and briefly report
on an ongoing investigation of current RE challenges in industry developing
data-centric systems.
