Network operators require analytics solutions that are not only predictive but can also test automated network infrastructure, finds Frost & Sullivan
The widespread adoption of machine-to-machine (M2M) communication, shift from reactive to predictive analytics for the Internet of Things (IoT), and continuing virtualization of network functions are compelling service providers to seek advanced testing solutions for big data and cloud analytics. Testing methodologies that can check the conformance of higher level infrastructure will prove critical in a digital environment that is characterised by long-term evolution (LTE), heterogeneous networks (HetNets) and cloud computing.
New analysis from Frost & Sullivan, Global Big Data and Cloud Analytics Test Service Market and Monitoring Equipment Market, finds that the market earned revenues of $650.1 million in 2014 and estimates this to reach $1.63 billion by 2019. The market consists of testing participants that aid in the overall visibility of the network as well as probe-based network infrastructure testing and service assurance, which aids in the monitoring of network metrics that will be collected for data analytics.
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As M2M communications enabled by IoT becomes ubiquitous across industries, the copious amounts of digital data have begun to strain the networks. The issue is exacerbated by the deployment of self-organising networks (SON) and cloud radio access networks (C-RAN) technologies.
“To reduce churn in the price-sensitive telecommunication and service providers space, network operators need to actively offer exceptional quality of experience and service,” said Frost & Sullivan Measurement & Instrumentation Research Analyst Rohan Joy Thomas. “They can no longer afford to rely on traditional analytics solutions; innovative solutions that can aggregate relevant information from heaps of data in a smaller window, as well as make forecasts by visualising patterns among end users, are becoming vital.”
However, these end users continue to be sceptical about adopting big data analysis due to the market shortage of talent and skillsets. Poor technical expertise of the product could lead to serious ramifications from a security perspective.
Furthermore, end users are reluctant to deploy big data analytics due to the complexities inherent in migrating from traditional data analytics to more contemporary and innovative forms of big data analytics, particularly in more well established organizations. The siloed approach of assigning tasks to specific teams based upon the nature of work, coupled with the multi-vendor nature of network infrastructure, often challenge testing specialists. It is difficult to efficiently deploy any analytics across networks with such variable characteristics.
The scepticism associated with adopting big data and cloud analytics test services is expected to gradually abate as the CAPEX and OPEX benefits for SON and C-RAN become evident. Many telecommunications and service providers have already started restructuring their IT staff in order to provide a more holistic view into the network infrastructure.
“Industry vendors should fill the gaps in their product portfolio to facilitate a more open testing environment for their end users,” observed Thomas. “This can be achieved through partnerships with participants from other niches of the industry, as well as the strategic acquisition of market participants.”