Modern data analytics and decision-making techniques can benefit the regulatory process just as they have benefitted almost every business sector and industry. Today, most companies operate on an iterative cycle, trying to constantly improve the way they perform and better achieve real world outcomes. They gather data, model it, base decisions on data analysis and translate those insights into action. They fly through this cycle continuously employing a combination of computers, experts and crowds. Regulation can be informed and enforced by way of a similar process - a process of: 1. Collecting relevant data about performance from the regulated entities 2. Properly organizing that data and unifying it with data from other sources 3. Applying data analytics techniques to the data to learn how a market and the regulated entities are performing vis-à-vis a set goal 4. Based on that knowledge, regulators can take action to shift the direction of regulation or enforcement In rapidly evolving markets, there is much favoring outcome-based regulatory models over prescriptive rules. The traditional concerns both regulators and companies have had with regard to such models can be overcome through the application of data analytics, iteration and collaboration. Key here is constructing a process that is very fast around the cycle.