 to privacy concerns [6]. Yet, despite these very real security and privacy concerns, communities are more frequently being used to derive more value from the data. Nearly, every industry is experiencing a trend toward "open innovation" where companies partner with academic institutions, researchers, and other companies, at times, even competitors [7]. The boundaries between a company and its eco- system are becoming increasingly invisible [8]. For example, Hortonworks along with Arizona Building the Network of the Future State University, Baylor College of Medicine, and the Mayo Clinic have formed a consortium to define and develop an open source genomics platform to accelerate genomics-based precision medicine [9]. DJ Patil, Chief Data Scientist, White House Office of Science and Technology Policy stated "Unleashing the power of data through open community and collaboration is the right approach to solve a complex problem like precision medicine. Initiatives like this one will break data silos and share data in an open platform across industries to speed genomics-based research and ultimately save lives" [10]. The surge in available data is also one of the key changes that has led to the emergence of practical ML and AI applications. The amount of data available, new training techniques for deep networks, and the use of graphical processing units, used in PCs and video game consoles, to model neural networks [11] have enabled the modern advancement in AI. These AI technologies, when trained on massive amounts of data, are demonstrating significant advancements in the areas such as object detection, speech recognition, and natural language processing. ML and deep learning techniques are designed to be iterative in nature, constantly learning and optimizing outcomes [12]. Instead of a rule or policy-based automation to guide the system on the next best step, AI is able to make a more intelligent recommendation based on data, context, patterns, and outcomes. As the system continues to iterate, more data are collec