Digitization of businesses and virtualization of networks promise business benefits but are also introducing new challenges for the communications service providers.
Ongoing exponential increase in data consumption by subscribers, especially of video applications and OTT, has taken a toll on the capacity of mobile networks, forcing CSPs to carry out continuous expensive capacity upgrades. In this context, IoT will only exacerbate the problem by adding another layer of traffic to the existing CSP networks.
Through customer-centric analytics, which offer insights into customer behavior, trends and predictions, a new approach to the resolution of capacity problems can be introduced. Carrier-grade business analytics with automation capabilities can solve critical capacity bottlenecks. Using such analytics (real-time and non-real-time), CSPs can trigger automatic processes for proactive or prescriptive actions to solve capacity issues.
To optimize infrastructure expansion, CSPs need to utilize analytics that add business value to decisions for planning new sites and capacity upgrades. Analytics help in the identification of revenue-generating locations, capabilities of handsets, customer behavior, uplink and downlink video traffic, consumption of video/conversational services, etc. This allows optimal network investments and helps in directing marketing campaigns for maximum business impact. However, analytics must be generated intelligently in real time or as long-term trends, so that revenue-impacting decisions can be accurately taken for both short-term as well as long-term expansions.
With this, CSPs are able to introduce dynamic ways of forecasting to allow the network planning teams to roll out the investment in line with the consumption patterns. Predictive analytics combined with dynamic forecasting techniques provide such insights.
Another key benefit of analytics comes from its use for network monetization. CSPs can proactively identify congestion-free network locations (where customer traffic is low and predicted to remain so for a while) and rapidly fill spare capacity with revenue-generating traffic from new service offers created in near real-time by analytics-driven, multi-team design and rollout, such as video streaming, mobile TV or smartphone apps. Such offers can be bound by time and location, and personalized for specific customer segments.
Through network analytics, the CSP can also identify processes/workflows that need a high level of automation and those that need manual control, as well as ways to distinguish between the two. As a quick example, automation of CSPs’ radio optimization processes, some of them open-loop and others closed-loop, can dynamically optimize network capacity and release skilled teams for detailed/complex analysis. The choice of automating workflows shall be based on telecom expertise of engineers involved in capacity planning and performance management, who understand the delays and inefficiencies that creep in because of human involvement.
The combination of customer analytics with automation techniques can help the CSP to create and drive new on-the-fly personalized services for locations and customers that will consume them rapidly, bringing almost instant monetization of available capacity.
The need for personalized or contextualized services will increase with virtualization of the CSP network, by introducing NFV.As CSPs move towards virtualization, the networks will transform and bring about a significant transformation of services. The new services will compete with the speed of delivery and dynamic creation/teardown offered by OTT providers, in turn impacting the network’s capacity to scale-up or scale-down.For a next generation digital service provider, virtualization promises the creation and deployment of new services in shorter time periods, down from a few months to a few days.
NFV offers agility in creation, modification and retiring of services, for which managing and maintaining QoS is very important. To assure the QoS of NFV-based services, the role of Service Quality Management (SQM) is key. With rapid deployment/tearing down of short-life services, SQM systems need to track the services, which might only last from a few hours to a few days, driven by events, location, customer context, etc.
In NFV environments, Service Quality Management will offer proactive, predictive and rapid RCA. Part of this digital service assurance can be achieved by adding analytics to the SQM information, which helps in failure prediction and assessment of service impact. Additionally, automation across the SQM outputs will help in managing network/service configurations. Using service modeling, the alarm relationship with underlying network elements can be quickly ascertained and eliminated, reducing MTTR.