The evolution of modern networks is emphasizing the role of a smart and agile management having the control of the network itself, the enabled services, and the relevant operations and business-related activities. These are historically duties of OSSs and BSSs, but currently the legacy tools are evolving towards more integrated, flexible and feature-rich systems.
Furthermore, the rise of new concepts (for example, Network Function Virtualization and Software Defined Networking, often coexisting with traditional networks), poses new challenges to a global Service Management activity, which needs to provide an end-to-end view, in order to avoid an inefficient silo-ed process.
Many areas are impacted. First off, the ability to monitor the network resources, and to optimize performances, has long been considered crucial to guarantee the quality of end-user experience, and ultimately to ensure customer satisfaction: it is now required to consider all resources involved with the enabled services.
For any Enterprise Big Data represent a real hidden treasure. But the data are useless, if not combined with a rigorous, scientific approach, able to extract actionable insights, and ultimately value, from the raw informative base.
This is where Data Science and Analytics come into play. The impressive variety of data (structured or unstructured, batch or real-time, punctual or streaming) makes the data ingestion a key point. Base analytics apply statistical methods to establish correlations and generate reports. Data Science goes further, predicting future scenarios, making extensive use of human insights, advanced statistical models and a machine learning iterative approach and prescribing proper actions to take in order to deliver decisions.