The capacity crunch is hitting operators across the globe, as the fight for spectrum space intensifies. The simple fact is that data growth is escalating at a much faster pace than spectrum auctions, network build-outs and RAN spectral efficiency improvements. The importance of het-net / small cell strategies and recent spectrum deals reflect this challenging reality. In the US, for example, Verizon Wireless has acknowledged that some of its largest markets are experiencing serious LTE capacity crunches. It is arguable, therefore, that effective capacity planning and management is the biggest challenge facing mobile operators.
No Margin for Error
Operators need to think smart about their investment choices. They are being forced to do more with less, balancing customer satisfaction against profitability; one false move can cost billions, with repercussions reverberating for years.
In order to safeguard the future health of their businesses, operators need to be confident that the decisions they make today are the right ones; investments need to be carefully planned, with future revenue and usage predictions based on empirical evidence, not finger in the air estimations. Networks have to run at optimal efficiency, with minimal waste, while still delivering a first class customer experience. The only way to achieve this is by having access to timely, relevant data across your business and operations to support decision-making; the operational intelligence that can only be achieved by using Big Data analytics.
The Big Data Boom
Data analytics tools have long been a key part of the CSP IT arsenal, but have been hampered by the high cost of storage, restricted compute power and limited scope; data would be gathered and stored, with analytics applied at the end, the whole process took months. Yet previously calls and texts were all an operator had to contend with. Today, CSPs handle petabytes of information on a daily basis, from multiple sources, and volumes are growing exponentially; this means the old store and query approach is simply untenable.
Added to this, with services such as Voice over LTE (VoLTE) and video, quality of service (QoS) is increasingly critical. Yet ensuring this QoS requires much faster response times; users expect problems to be handled in seconds, rather than months. The challenge CSPs face, therefore, is how to transform these varied data sources into an end-to-end view of their network and user experience in real-time, so they can always make the best possible business decision; this is where Big Data analytics have become invaluable.
Applying Operational Intelligence to Capacity Planning
Big Data analytics tools are particularly useful in capacity planning as they enable analytics at the edge, meaning operational intelligence is applied much earlier in the process, providing insights in real-time. Also, by having the ability to correlate multiple data sources, operators are given a more complete picture of current usage, helping to identify and correlate relationships between different pieces of information, including devices, systems, applications and usage data. This provides the operational intelligence to support effective decision-making in three key ways:
- Infrastructure Planning: Trends analysis of tonnage, flows and subscribers per device, application, geographies and network elements allows CSPs to forecast infrastructure needs for new device introduction, new service geographies and new applications. By correlating information from these data points, CSPs can predict uptake and accordingly plan capacity by assessing the impact of services on the underlying network.
- Improving Quality: Service usage analytics help to isolate and plan for congestion, network errors, QoS, and instrument capacity traffic routing cashing and other optimisations. As a result, CSPs can monitor existing services to ensure consistent QoS and automate follow on actions should common problems persist.
- Cost Optimisation: By identifying end-to-end traffic utilisation and trends, CSPs can optimise operational costs by comparing: ingress/egress bandwidth, sessions and concurrency; subscriber clustering and behaviour; device profiling and penetration; and cross-referencing these data points across applications and geographies. By having a complete visibility into these operational costs and correlating with subscriber data, CSPs can identify areas of waste, most profitable customers, and plan to ensure they gain the greatest return on investment.
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