As mobile network operator, you have a wealth of data available to help optimize and extend your network coverage and connectivity. The problem is that, with the current approach, the data is scattered in different silos. Unless we can bring this data together, you won’t be able to obtain the insights you need to do things better and to truly leverage it to enhance the coverage we deliver. And, you won’t be able to unlock valuable new use cases.
Optimizing mobile network coverage has become an increasingly challenging task. Networks have become more complicated, with new services to support. Maintaining connectivity with the right service levels, even as you add new capabilities is difficult. Delivering new capacity is something that must be got right, so you can ensure your investments are a success.
So, you have to continuously optimize performance for the areas already covered, ensure that conditions are optimal for every service launched or enabled for others, and extend coverage to new locations.
Currently, most MNOs use static data to optimize coverage, such as population numbers and density, local topology, signal strength demands, and so on, while leveraging network data to track performance and experience as part of assurance programs.
However, that approach is limiting. Yes, it gives the ability to tune around baselines and to troubleshoot when problems emerge. But it depends on isolated data sets, and this means you cannot leverage a unified data set that can drive deeper and richer insights: MNOs need the ability to get a lot smarter about how they manage critical mobile network coverage.
To achieve this aim, a new approach to data is required. You need to be able to leverage the other data sources that are also available. These include information from Performance Management outputs – generated by systems in the OSS, which include telemetry data, status indicators, and much more. There is also Configuration Management data, Fault Management data, information from ticketing systems and so on.
Each data set provides a different perspective. Traditionally, they have been viewed separately, essentially in silos. As a result, information that can complement other data cannot not easily be correlated.
Yes, you can learn about network performance from monitoring data, while also checking system status from alarm handling solutions – but you cannot view these essential inputs together.
In turn, that means that you can’t use all the available information sources efficiently to plan and optimize the best coverage for a given cell. There is information regarding traffic patterns; there is also information regarding service quality, RAN performance and so on – but the desired unified view is absent.