8.1 Historical domain evolution. What's Next? significant amounts of data. While only a small portion of the data available was analyzed, it presented meaningful information for use in decision-making. Domain 2 (D2) was the natural evolution to a cloud centric environment, first showing up in IT, and now in the network. Optimization in this environment is primarily characterized by functional abstraction which enables the complexity of a multivendor environment to be masked by software. The evolution from hardware-based functions to software-based functions running on commodity cloud infrastructure leads to extensibility and agility. It unshackles data from physical architectures, SO prevalent in D1, and forms the basis for coherent, closed-loop automation by recognizing events and patterns and taking rule- based actions. Domain 3 (D3) is a logical next step in the evolution, given four key trends: the accelerat- ing pace of data generation, the need for communities to gain meaningful insights, the requirement for advanced security and identity management, and the utilization of AI to enable hyper automation. In D3, establishing communities of data and capabilities can unlock internal hyper automation opportunities, and can facilitate pertinent innovation across entities. The complexity from the massive data generation of D2 is harvested to power smart optimization and mass personalization, paving the way for new business and operating models across multiple industries. 18.3 HYPER AUTOMATION WITHIN THE NETWORK In a post NFV/SDN world, communities of internal data and capabilities will form within the enter- prise and use massive ML to make network operations invisible. In earlier chapters (Chapters 8, 9, and 16), key applications of ML and AI pertaining to software-defined networking, were described. These include 1. Self-optimizing networks 2. Cybersecurity and threat analytics 3. Fault management 4. Customer experience Rather than performing reactive analytics, ML helps to predict anomalies or events well in ad