Big Data Is Back In Fashion As Fuel for AI

Evolve your data architecture to acquire, store and manage new data types — existing data capability investments represent some of the most valuable assets for a service provider.
AI can propose the optimal price, content, size, validity, or other parameters of a product catalog entry and configure it based on deep learning of the competition from available data such as advertising, voice of the customer feedback, and BSS data. It will also provide the justification for this recommendation, outlining the performance benefits; for example, reducing the price by 50 cents will increase take rate by 2.1%.

Network optimization

AI will be grounded in service providers' SDN and NFV. For example, a fully NFV-enabled network will ultimately be controlled by a single NFV orchestrator (NFVO) that decides about critical network operations such as assigning more resources to a network function, creating new network elements, or tearing down network elements that are underutilized. Eventually traffic will be controlled by a centralized SDN controller that may be augmented by AI functionality. This will allow the efficient and proactive routing of traffic so that capacity can be managed effectively, network outages minimized, and faults bypassed. AI can also be used to optimize the configuration of a service provider's network according to dynamic network-capacity demands, the characteristics of the traffic volumes, user behavior, and other parameters. Network deployments may also be further improved by AI, which will be used to predict traffic patterns and forecast user trends.

Recommendations for Success

Understand what data is available and its relevance to AI opportunities — exploring what data sources are available means not just looking at new sources but also reexamining existing applications and systems around a service provider's business, including product catalog data. It can also include external sources from data partners that sell access to data sets or those that engage in data sharing for mutual benefit. 

Use business context to enable data validation — engaging with the wider business is critically important. Do not limit efforts to well-understood areas like IT, networking and finance, but rather include more novel areas like customer service, marketing, and sales, which contain highly valuable customer insights. Work with third parties to help broaden the context of the data that already exists and introduce customer experience insights that may not already exist in the organization.

Evolve your data architecture to acquire, store and manage new data types — existing data capability investments represent some of the most valuable assets for a service provider. Extend them to handle the scale, variability, and speed that comes with new unstructured data types, such as that coming from sensors and the IoT. This will likely mean developing new big data capabilities that are able to both ingest and store the data, as well as integrate multiple data sets. In many cases, placing such new capabilities in the cloud or another managed service can offer benefits that enable the flexible scaling of technology without impacting existing architecture.

Integrate multiple insights and unify customer profile. The foundation for intelligent customer interactions in marketing and customer care lies in a service provider's ability to discover its customers’ intents, while seizing those moments in real time to provide relevant, personalized, and proactive customer experiences across all channels. Seek out solutions that help procure this 360-degree view by fusing the widest variety of first-party with third-party data. 

In summary, service providers in the past few years have moved away from subjective-based decision making towards big data-driven strategies. The advent of AI is bringing the industry ever closer to an era driven primarily by intelligence and real-time insights. In order to realize the full potential of data-driven intelligence, each service provider needs to develop its own comprehensive data management roadmap. Such a strategy is the only way to ensure the ability to seamlessly and continuously process, extract, and integrate the increasingly diverse and rapidly growing data sources – and as a result, make accurate predictions, automate decisions and manage conversations directly with customers.


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