While there is wisdom in the old adage "the two constants in life are death and taxes,'' there are unavoidable truths facing modern experimental and computational science. First is the growing "impedance mismatch'' between our ability to collect and generate data, and our ability to store, manage, and gain understanding from it. The second is the fact that we cannot continue to rely on the same software technologies that have worked well for the past couple of decades for data management, analysis, and visualization. A third is that these complementary activities must be considered in a holistic, rather than Balkanized way. The inseparable interplay between data management, analysis, visualization, and high performance computational infrastructure, are best viewed through the lens of case studies from multiple scientific domains, where teams of computer and scientists combine forces to tackle challenging data understanding problems.