In recent years, the explosive development of mobile communications and networking, together with the wave of Internet of Things (IoT), has led to super-complex systems, which are difficult to model and manage. At the same time, such systems are generating a large amount of data on a real-time basis, from both the user and network sides. How to utilize such data to relieve the dependence on restrictive, sometimes even unrealistic, system models are the key leading to more efficient and effective future networks, especially when under various resource constraints as in IoT systems. Fortunately, recent advancements in artificial intelligence (AI), empowered by modern machine learning algorithms, have demonstrated remarkable success in a variety of fields and are stimulating numerous data-driven approaches as well as applications. Combining the availability of big data in complex IoT communication networks and the recent advancements in AI, it now comes the time to renovate how we resolve network management issues to more efficiently and effectively fulfill the dynamic demands of network subscribers, especially in the presence of stringent network resource constraints. With the fuel (IoT data) and the engine (AI), data-driven network management will enable us to dynamically and adaptively meet the spatio-temporal network demands in the most resource-aware and resource-smart manner.