Data analytics platforms today largely employ data processing kernels (e.g. implementation of selection and join operator algorithms) that were developed for a now bygone hardware era. Hardware has made fundamental shifts in recent years, driven by the need to consider energy as a first-class design parameter. Consequently, across the processor-IO hierarchy, the hardware paradigm today looks very different than it did just a few years ago. I argue that because of this shift, we are now building a 'deficit' between the pace at which the hardware is evolving and the pace that is demanded of data processing kernels to keep up with the growth of big data. This deficit is unsustainable in the long run. One way to 'pay off' this deficit is to have hardware and software co-evolve to exploit the full potential of the hardware. I will provide some examples of recent work from our Wisconsin Quickstep project that demonstrates the merit of this line of thinking. I'll focus on analytical data processing environments, and argue that our new way of storing data, called BitWeaving, and flattening databases into BitWeaved de-normalized tables, which we call WideTables, provides a dramatic new way to build 'sustainable' analytical data processing systems. I will also discuss the implications of our approach on future hardware-software co-design for data analytics platforms.