The 5G world is coming, and this requires us to transform the network to handle the business demands. To prepare for and fuel 5G, we will need to explore the application of advanced technologies to unlock the value of geospatial fiber asset data. Thankfully, Machine Learning (ML) and Artificial Intelligence (AI) techniques can be applied to improve the strategy, operations, and product offerings of service providers who plan, build, and operate fiber networks.
Revenue sales and marketing programs
Sales and marketing managers want to activate their customers’ services as soon as there is network access — also known as speed-to-revenue. ML/AI can be applied to geospatial and asset data, giving businesses the tools for:
- Identifying potential customers that are within the current network footprint, which can add revenue opportunities with minimal network construction costs.
- Prioritizing or de-risking planned expansions.
- Synchronizing marketing initiatives with network turn-up.
- Estimating cost and coverage achieved.
It is important for service providers to understand their market penetration and use of capital. As ML/AI techniques become more robust, they will enable autonomous processes, such as self-serve activation, which previously would have been unthought of.
Service providers want to predict where and when network failures will happen in order to improve the quality of service delivered to customers. Techniques that show or predict where the network is under strain give service providers the best possible coverage and operations in all scenarios. Not only can it be a business differentiator to have the best and most reliable service, but operating costs can also be kept lower by modelling the number and locations of spares required. Automated workflows and systems triggered by circumstance add efficiency by affording deeper insights.
Network engineering and deployment
Cost and time efficiency in network planning and construction can benefit from automation as much as any other network activity, especially where these processes encounter more difficulty because of the unique locations and conditions where the physical network is built. ML/AI and mathematical optimization techniques can encode business logic to generate consistent rules-based designs, leaving only the edge cases for human interdiction.
Create network data veracity
If service providers could adopt the automation that would allow their customers self-serve activation today, would they? The hard answer to that question is no. In most cases, the network layer data needed to support automated actions is not accurate or complete. But by ensuring data quality and utilizing ML/AI, service providers can improve the quality of service and accessibility. In essence, operators need their physical and digital assets to be linked. Once established, this can even perhaps prevent bad data from entering the asset management environment, both from manual edits and systematic uploads.
Service providers are exploring ways to use data that will not only automate workflows for predictable outcomes but also provide faster service activation and product differentiation. ML/AI techniques can unlock the latent potential of the layer one asset data and break down data silos in ways that will realize business benefits.
Where in your business are you ready to unlock the value of combined outside plant and the network layer data?
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