d a variety of other aspects of great interest to the community. Threat analytics is another area that demonstrates the power of D3. For example, ML in the net- work could identify an anomaly in traffic patterns between two customer locations and predict that there may be a security threat. That network metadata is shared within the protected information exchange where it can be combined with all available and relevant data. The AI engine identifies that the traffic pattern does not correlate with known explanations based on the collective data and a network anomaly alert is sent to customers notifying of a potential threat. From there, customers could have the ability to combine the shared threat data with their internal data to determine the extent of their risk, or grant permission to automatically adjust the network, using software-defined networking capabilities, to mitigate the risk. 18.6 SUMMARY A post NFV/SDN world is characterized by data-powered optimization. It harvests the complex- ity of massive data generation through ML and AI. Communities of data and capabilities drive hyper automation in the network and within the enterprise. Network operations become invisible, augmented employee experiences that deliver a better customer experience. In this future state, a company will not only drive data-powered optimization within its internal communities of applica- tions, employees, and business units, but also within communities of relationships across suppliers, partners, customers, universities, and other institutions. Of the many tantalizing ramifications of such democratized innovation, perhaps chief among them will be the innovative business models that will rise-entirely new theories of value exchange, social engagement, and market conception. What's Next? REFERENCES 1. http://adage.com/article/digitalnext/iot-redefine-data-sharing-privacy/300084/ 2. Why a Connected Data Strategy is Critical to the Future of Your Data, Hortonworks White Paper. htps://hortonworks.com/info/connected-data-platform 3. :/