"Who is Bram Stoker?" – With this $1 million prize winning final question in the game show Jeopardy, IBM's Watson supercomputer using DeepQA technology ushered in a new era in artificial intelligence and informatics. Welcome to the era of deep neural networks and self-driving cars! This has far reaching implications for knowledge modeling and reasoning in a number of fields including materials engineering. Designing new materials and formulations with desired properties is an important and difficult problem, encompassing a wide variety of products in the specialty chemicals and pharmaceuticals industries. Traditional trial-and-error design approaches are laborious and expensive, and cause delays time-to-market as well as miss some potential solutions. Furthermore, the growing avalanche of high throughput experimentation data has created both an opportunity, and a major modeling and informatics challenge, for material design and discovery. Such a data deluge is coming from smart sensors in process plants, ab initio quantum calculations, molecular dynamics simulations, and so on. We are moving from an era of limited data obtained through time consuming experiments and simulations to one of a tsunami enabled by high throughput experiments and TeraGrid computing environments— it's a dramatic transition from a "data poor" to a "data rich" era. A systematic way to convert raw data into information and first-principles knowledge that can be used ‎for real-time decision making is very much lacking. A new paradigm is needed that increases the idea flow, broadens the search horizon, and archives the knowledge from today's successes to accelerate those of tomorrow. Data science, loosely defined as a body of knowledge comprising of machine learning, natural language processing, databases and informatics, will play a crucial role in materials design and discovery, process development and commercial scale manufa