The recent advances in Software Defined Networking (SDN) have demonstrated more flexibility in controlling and managing access networks by the different stakeholders, such as users, infrastructure providers, or virtual network operators (see [3]). SDN is a natural evolution that brings programmability to network elements by merging datacoms with telecoms where the need for higher programmability of the data plane has been pushed from data centres into wider area networks. Network operators are making sure to leave no stone unturned to take advantage of the flexibility offered by the SDN paradigm to manage and make time and cost critical traffic engineering decisions, thus avoiding/reducing the costs incurred to the different stakeholders that use and operate the network. Network operators perform large scale data analytics on internal network and traffic data which is then fed back to network applications that utilise specific control plane functions to reconfigure and manage the network. The integration of Big Data with SDN is seen as an important strategy by the network operators. In this paper, we propose the idea of integrating external data (such as information from Facebook, Twitter, news feeds etc.) with SDN to enable operators to make intelligent traffic engineering decisions with their networks. Such integration would pave the way for novel ways of identifying not only the internal network and traffic characteristics (as currently carried out by operators) but also external/user