The digital economy is spreading into everyday life driven by near-ubiquitous connectivity. Underpinned by network and internet access, ‘smart’ devices and the “internet of things” enabling vehicles, buildings and other items to collect and exchange data. The challenge facing communications is daunting with forecasts predicting there could be a hundred billion devices connected by 2025 and a 500 fold increase in the amount of data a person uses.
Last week in Openet’s blog our CEO Niall Norton wrote about the main foundations of digital transformation being real-time and automation. At the heart of automation is intelligence. This sounds obvious, but intelligence is needed to ensure that the right decisions are taken in any automated process. The better the intelligence, then the better the outcome from actions taken by automated systems and processes. As we move to 5G, greater levels of personalization and see the roll out of IoT gather pace, the volume and complexity of data that is needed to be processed in real-time to enable intelligent decisions (and therefore better outcomes) is light years ahead of what operators have been used to (e.g. processing and analysing CDRs).
The good news is that this move to real-time and automation to provide the foundation for digital transformation has already started. Many operators are transitioning existing legacy networks (built using physical hardware and offering limited flexibility and automation) to software centric networking. Using virtualization and cloud technologies to deliver intelligent programmable networks that can rapidly automatically scale and adapt to changing needs. AT&T has described this transformation as the biggest software project AT&T has ever undertaken, and the hardest; it’s probably the most sophisticated thing we’ve ever done. The architecture is horribly complex.” – John Donovan, CEO AT&T.
A key requirement to allow networks automatically scale and adapt is an analytics capability based on artificial intelligence and machine learning. Providing real-time analysis to automatically detect customer experience degradation or service quality exception to trigger the network to self-heal in case of network problems. Example use cases include predictive elastic scaling of network resources to match demand or proactive maintenance that can predict network incidents before they occur and trigger automatic actions to avoid or minimize outages.
As we move from networks into marketing and customer experience the possibilities for automation based on real-time analytics, and then artificial intelligence and machine learning become huge. Using automation driven by real-time and predictive analytics to deliver the best customer experience at all steps of a continuous journey is a real possibility for operators.
