Spotlight on Tech

Artificial Intelligence – making the best use of data to draw actionable insights

Gaurav Jain
Director of Engineering
Rakuten Symphony
April 5, 2023
minute read

Artificial Intelligence (AI) is no longer a buzzword and is implemented across telecom operator functions worldwide. However, the true potential of AI can only be utilized provided the foundation of organization-wide data gathering and its discovery is available to all. And it has been repeatedly proven that having easy access to diverse data and AI Machine Learning (ML) algorithms solves business obstacles.

AI in telecom

Compared to other industries, telecom has some catching up to do when it comes to growth and innovation. With continually increasing cost pressure, colossal customer demand for a better user experience, and to keep ahead of the competition, the mass adoption of AI must become a key factor in improving business outcomes and efficiency.

Telecom service providers generate vast data dealing with performance KPIs, fault metrics, network planning, customer support, etc. This structured and unstructured data can be used for descriptive, predictive and prescriptive analytics that deliver business value using powerful AI or ML algorithms to augment network optimization, fraud detection, customer experience, capacity planning, recommendation systems and more.

The pressing need for telcos, both greenfield and brownfield, is to own an AI/ML platform that is tailored for their consumption on day one, providing solutions that transcend all horizontal functions and are flexible to be hosted on any cloud or on-premises infrastructure.

Rakuten Symphony’s data and AI platform is vertically integrated, designed and tested to support the telecom operator, from network operations to business and customer support systems. Hence, the use cases we can deliver are pre-tested with telecom data deployed on a massive scale.

An AI platform can be a catalyst of growth for telcos

Our AI platform offers use cases to solve telecom business problems and an arena for data scientists to train, test and deploy models quickly and easily. Each execution in the platform, either to get data or run algorithms on top of these to generate scalable APIs for inferences, is containerized. Sometimes the model behind these use cases decays over time and needs to be monitored for performance degradation. The platform also offers features to govern and monitor ML models and trigger automated re-training in case of drift.

The AI platform allows data scientists and SMEs to save time accessing datasets of different applications allowing them to develop cutting-edge AI solutions pertinent to their specific business demands.

Significantly helping them stay ahead in making smarter data-led decisions and reimagining how they can run their businesses.

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