My take on ‘data-driven’ is on the one hand that of simple data transparency, in the sense that data is given direct graphical expression through tight data-to-graphics bindings, and on the other that these bindings can be manipulated at various granularities using selections and associated transformations.
A simple analogy might be a plant seed. Left to itself, it will grow to assume a shape characteristic (‘transparent’) to that species. By manipulating it’s growing conditions, we can encourage it to reveal specific qualities such as it’s ability to climb, spread, survive arid conditions, cope with shade and so on. Each form assumed is an expression of the underlying seed type, but also of it’s manipulation. In this way a single data set can express itself in many ways, allowing us to learn better what to expect from it.