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NetApp has elevated its Solutions Engineering team by appointing Martin Cooper senior director of Solutions Engineering EMEA, following more than two years as senior director of NetApp’s Cloud Infrastructure Business Unit. (more…)
At Intel’s recent Data-Centric Innovation Summit, the company shared its strategy for the future of data-centric computing, as well as a view of Intel’s total addressable market (TAM), and new details about our product roadmap. (more…)
It’s a pretty safe bet to say that former US President Theodore Roosevelt would have loved machine learning, writes Derek Canfield, the general manager for Business Analytics at TEOCO. He left behind a treasure trove of quotes, but one of his most famous ones is perhaps even truer today than it was for Teddy.
Artificial intelligence-based platform’s enhancements connect front-end experiences to back-end operations
Genpact, a global professional services firm focused on delivering digital transformation, announced new customer experience enhancements to Genpact Cora, its modular, artificial intelligence (AI)-based platform that helps enterprises accelerate digital transformation at scale. (more…)
Cloudera, Inc., the modern platform for machine learning and analytics optimised for the cloud, and Tata Communications, a global provider of network, cloud, mobility and security services, announced a strategic partnership that enables enterprises to unleash the power of their data to fuel business growth. (more…)
No one has missed the telecom industry’s big hope that Big Data and Artificial Intelligence (AI) will come to the rescue and improve the current level of service assurance in the service provider domain.
Service assurance systems are promising everything from real-time service health information, inferred from low-level resource event data, to digital customer service assistants. This white paper assesses the realism and viability of such approaches.
We start by defining service assurance itself and by describing the types of service assurance systems available on the market today, in terms of what questions they can answer and what kinds of data they provide. Next, taking a strictly fact-based and neutral, engineering view of the topic, we look into what may be possible with Big Data and AI and what is not realistic – in terms of cost as well as resources.
We therefore discuss what questions should be answered by service assurance systems and what kinds of data we can pull from the network as input to provide these answers. We also summarize the roles of Big Data and AI in the context of service assurance.
Finally, we introduce the missing piece in service assurance today: active testing and monitoring. Active testing and monitoring addresses the question: “Are we meeting the level of service quality we promised?” We describe how this activity complements the data landscape derived from the service assurance stack with precisely the kind of pertinent and high-quality data that is missing.
We close the white paper with a brief look at some of the relevant results from a recent NFV Service Assurance and Analytics research study and survey completed by Heavy Reading in October 2017. The findings of the survey and the industry’s views on what is needed to satisfy the fundamental goals of service assurance are discussed in relation to the research and findings of this white paper.
CSPs feel management siloes can be avoided in hybrid networks, Frost / VanillaPlus survey finds: Part 1
Analysts Frost & Sullivan collaborated with VanillaPlus recently to ask our readers their thoughts on 10 key questions facing communication service providers (CSPs) involved in digital transformation of their business and operations. In a 2-part summary, Jeremy Cowan reports on the findings. (more…)
Gartner says deep learning will provide best-in-class performance for demand, fraud and failure predictions
Deep learning, a variation of machine learning (ML), represents the major driver toward artificial intelligence (AI). As deep learning delivers superior data fusion capabilities over other ML approaches, Gartner, Inc. predicts that, by 2019, deep learning will be a critical driver for best-in-class performance for demand, fraud and failure predictions. (more…)