Scientific fields don’t necessarily emerge because fundamental new knowledge is being generated, but often because a shift in the key questions that are facing us, and the tools that we have to answer them. The current information revolution is causing us to reassess our approach to data. Our mathematical and computational toolsets are co-evolving. The potential of very large interconnected data is placing urgent demands on our methodologies. In this talk, inspired by these challenges, I will give a personal perspective on what this means for those of us at the interface of Computer Science/Mathematics and Statistics. I’ll attempt to do this not only in the context of modelling and analysis, but also in the context of how we deploy our conclusions for the benefit of wider society. Many of our current suite of methodologies were motivated by different needs, and I’ll argue that it may now be time to return to the fundamental ideas from where these methodologies were inspired, but with a contemporary slant on the nature of data. My own perspective is that if what I describe *is* data science, then it does not stand as a field alone, but it represents a new and pressing set of questions that bridge the computational and mathematical sciences. Regardless of its phylogeny, exploring this interface through these questions will be mutually beneficial.