Big data in healthcare is experiencing the perfect storm: The volume is increasing exponentially with accelerating speed, the variety of data ranges from multi-omics information to lifestyle measures with the help of mobile devices backed by cloud infrastructures. State of the art analytical methods are generally limited by computational approaches. Furthermore, the convergence of data analytics, sophisticated modelling approaches and cognitive computing gives promise to solve the big data challenges in healthcare and lifescience. Data analytics especially in todays ‘omics era yield results of large volumes given computational challenges are overcome. Sieving through the results requires expert and translational knowledge. Cognitive computing can play a significant role in making transparent results. Cognitive computing tools can be used to create hypotheses to guide experimental studies but also as prior knowledge that drives data analytics. The increasing amount of data requires a larger amount of computation that can at some point only be tackled using supercomputers. In biophysical modelling we have already shown how the computational challenge can be overcome using high performance computing systems. The sophistication of computer modelling of biophysical processes has made the transition from basic research to translational science and medicine. It is feasible today that data in healthcare will be augmented by simulation of biophysical models tailored to each patient. Cognitive computing is a promising path to make the analytical results transparent. The IBM Watson technology allows analysis results to be represented within a global context of accumulated knowledge of published literature. To view data and analysis in that global context will not only enable verification of results, but also helps accelerate discovery and identification of, for example, new targets in drug discovery. The combination of data-driven and knowledge-based analytics in a cognitive computing environment becomes a powerful way to create hypothesis and to limit the search space so that it can efficiently be tested using traditional laboratory methods. The IBM Watson technology allows one to find “the needle in the haystack” of today’s big data challenge. Hence, the power of big data can only be unleashed by embracing new approaches in data-driven analysis within a cognitive computing environment. This creates a holistic view that places big data analytics into the context of the accumulated knowledge of the scientific community.