According to GSMA Intelligence there are more than 8 billion mobile connections in the world today – a billion more than there are people on this planet. This is growing at a phenomenal rate, with over 25 billion networked devices expected by 2020, attributable largely to 5G and the internet of things (IoT). You only need to look at how Qualcomm, a Teradata customer, portrays the demands placed on the 5G network.
One thing is clear: 5G will be a multi-network convergent architecture comprising macro-cell 4G (LTE), fixed line fibre broadband, Wi-Fi, small cell etc. – a network of networks.
Optimizing the complex web of 5G networks to ensure those 100+ billion messages from pace makers, street lights, security cameras and other connected IoT devices can reliably and securely transmit without fail will require advanced data management techniques, machine learning and AI.
With an unrivalled portfolio of international wholesale solutions for fixed line, mobile communication service providers and ISPs worldwide, this customer offers a comprehensive, flexible and innovative suite of solutions that connects over 100 networks in over 30 countries offering Carrier Ethernet services, DWDM network offering 100G services.
A key differentiator for this Teradata customer in the highly competitive wholesale carrier services market is “Analytics as a Service”: a cloud initiative which allows internal employees and customers to have a clear 360 degrees view on their network signalling and mobile data products (SS7/GRX/LTE). It also provides the mobile operators full visibility on the activity of their roaming subscribers and on the quality of service these subscribers experience.
This customer has the following challenges:
Increase in data volumes between 100-200%
Increase in business complexity across products (GRX/SS7/LTE/LTE DR/TAP files/…) with the same service level agreement expectations
Inability to trend/forecast long term
As service providers embark on their journey towards 5G and IoT, common themes are emerging from case studies.Tweet This
In preparing for the above challenges and in getting ready for evolution to 5G and IoT, this customer has incorporated a hybrid architecture that integrates the open source Hadoop big data platform into the analytic eco-system. This is expected to provide cost effective scalable data storage, and cost effective scalable processing power while enabling near real-time analytics.
Australasian multi-service provider
As a leader in retail and wholesale service, an Australasian multi-service provider promotes itself as the preferred international carrier for business markets. Owing to many recent network outages caused by explosive traffic growth, the company has initiated a multi-year programme of work focused on data and analytics with an emphasis on machine learning (ML) and artificial intelligence (AI). This customer’s goal is to provide a reliable and resilient network to its corporate customers and has sought help from Teradata to help perform advanced analytics.
As part of this programme, massive volumes of data from the network management systems are integrated into several big data platforms. Many data scientists are developing machine learning algorithms to eventually automate the management of the network, rather than relying on field technician intervention. In the initial phase of the programme, the focus is on assurance and resilience of the edge networks to ensure customer satisfaction and retention.
North American full-service provider
This customer has launched a programme focused on leveraging cloud technologies, network virtualization and software-defined networks to offer services while reducing capital and operations expenditures and achieving significant levels of operational automation.
In order to engineer, plan, bill and assure these dynamic services, the architecture framework gathers key performance, usage and telemetry events. It draws these from the dynamic, multi-vendor, virtualized infrastructure in order to compute various analytics and respond with appropriate actions based on any observed anomalies or significant events – all in real-time.
The key design goals are to collect data, analyse and correlate. Anomalies are detected and published for action in various forms to be realized at multiple layers (e.g., infrastructure, network and service). The platform consists of multiple components to deal with massive growth in data volumes: common collection framework, data movement, edge and central data lake, analytic framework and analytic applications.
As service providers embark on their journey towards 5G and IoT, common themes are emerging from case studies like these. Service providers want to analyse anything (i.e. network traffic load, network signalling events, network resiliency, service performance, customer experience and more).
In doing so, they are deploying analytics anywhere in their network, evolving from descriptive analytics to predictive and prescriptive analytics with machine learning and AI, looking for the flexibility in their architecture to move data at any time, and mirroring that flexibility with payment options that allow them to buy in any way they like.
Sundara has been a Telecom professional for over 30 years with a wide range of interests and multi-national experience in product management, solution marketing, presales for new generation networks and services, information management strategy, business intelligence, analytics and enterprise architecture development.
At Teradata, Sundara focuses on Business Value Framework, Business Outcome, Business Value Consulting, Business Intelligence, Discovery Analytics and Customer Journey / Experience Management solutions.
Sundara has a Master’s Degree in Business and Administration with research on economic value of information from Massey University, New Zealand.
For the last 20+ years, Sundara has been living in Sydney, Australia. In his spare time, Sundara enjoys walking and maintaining an active life style. Sundara is an inventor and joint holder of an Australian patent with his clinical psychologist wife. The invention is an expert system in cognitive mental health that applies machine learning algorithms.