- This topic has 0 replies, 1 voice, and was last updated 3 years ago by Wallis Dudhnath.
24th September 2017 at 08:05 #70090Wallis DudhnathGuest
Before we look at Use-Cases each Telecommunication CSP – Customer Service Provider – must look at their Business Strategy to see if Data Science is an area that they will invest and progress. This is key as this has to be a driven using a top-down approach – from the “C” level management to the Engineers. The implementation of a Data Science Layer is similar to a CSP Transforming their OSS/BSS/IT stack so that it is horizontal (SOA) and can support micro-services.
Once the business strategy is in place the discussions and the implementation of a Data Science Operating System Layer needs to be addressed. As this is pervasive this will have an impact on the whole organisation end to end.
The Data Science Operating System Layer will need to have touch points with Artificial Intelligence (AI) and Machine Learning (ML) areas. Compare this with crude or refined oil. If there is not a need for plastics, aviation fuel, fuel for compressors, Internal Combustion Engine, Diesel engines, etc.. then oil becomes a useless commodity.
The clever part is that the combination or the confluence of AI/ML and the Data Science Operation System Layer will allow customer insights to be obtained and a qualified decision can be made to provide a new Campaign to the End User, Increase capacity to a B2B partner, extend the life cycle of a SME that has a history of good credit and has hit upon a bad business period, etc..
As this will help to increase the automation process this will help to elevate the overall customer experience for all segments.
VBR/ Wallis Dudhnath