Service Assurance – In Need of Big Data or Small Data?

This white paper discusses current service assurance systems and how they relate to Big Data, Artificial Intelligence, Machine Learning, and Deep Learning. It also introduces the concept of Small Data – using active testing and monitoring to provide direct answers to the relevant service quality questions. Also included are some highlighted results of the recent NFV Service Assurance and Analytics research study and survey completed by Heavy Reading in October 2017.

Key takeaways from the research include:

  • The industry’s expectations on Big Data and AI should be lowered to a more realistic level. These technologies will not provide a panacea for all service assurance needs and transformation challenges.
  • As Big Data and AI rely on relevant and high-quality data, large amounts of low-level data will not satisfy the requirements to receive satisfactory answers to the relevant service assurance questions. With current systems, it is very difficult to obtain high quality service-related data on network services using traditional infrastructure-centric assurance tools or Big Data and other AI technologies.
  • Data from active testing and monitoring can provide detailed, real-time service KPIs. These KPIs, which can be referred to as Small Data, provide great value by themselves, but they are also enablers for the successful application of Big Data and AI.

Download the white paper to learn more.

One could compare finding the relevant data within a Big Data lake for answering the fundamental service assurance questions to finding needles in a haystack. To find those needles, you must comb through a haystack, hoping that the relevant data has in fact been added at some point and also willing to spend both significant costs and time resources to search – only with Big Data and AI, that haystack also continues to grow larger and larger.

By contrast, Small Data, obtained through active testing and monitoring, presents you directly with a pertinent handful of needles, bypassing the Big Data requirements as well as resource-hungry data lakes.

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