August 28, 2020 By Holly Vatter 4 min read

As 5G networks drive the next wave of transformation for the communications industry, big data and analytics will play a critical role as communication service providers (CSPs) realign their business strategies and implement plans to restructure their operations, architecture and networks. The fuel driving this transformation is data  — specifically using advanced data analytics, machine learning, artificial intelligence (AI) and other data-centric technology advances.[1] The challenge for the CSP is to harness, analyze and manage the growing volume, velocity and variety of incoming data, which is too vast and complex to be analyzed by traditional means.

The magnitude of the shift to 5G is evident:

AI’s footprint in the telecommunications industry is growing at a staggering pace of 46.8% and expected to reach nearly USD 2.5 billion by 2022. In addition, by 2030, 5G networks are expected to contribute USD 700 billion to the global economy with a compound annual growth rate of 20%. Overall, CSPs are expected to spend upwards of USD 1.5 trillion to roll out 5G networks and services.[2]  






Driving CSP use cases

When using a governed data lake and the latest digital tools, data scientists can develop real-time forecasts, arming communications service providers with the intelligence needed to improve customer relations, innovate and improve product and service offerings and drive more efficient operations. Aggregating data and using the inputs from machine learning (ML)and Artificial Intelligence (AI), data scientists can drive real-time testing results and more accurate predictive and prescriptive analytics. predictive and prescriptive analytics.

Three key areas where CSP benefit from using big data include:

  • Customer experience analytics: By applying analytics across key data sets, CSPs can create a true 360-degree view of customers using customer profiles, user data, network performance metrics, location data and social media streams. This aggregation of data can be used to predict and prevent churn; develop targeted marketing using real-time analytics; develop personalized offers and recommendations; improve the overall customer experience; and help develop a deeper relationship with the customer.
  • Network optimization analytics: The network continues to be the biggest cost center for CSPs, consuming up to 40% of capital and operating budgets.[3] Network optimization requires complex and fast analysis of large data sets, including usage, mobility patterns, network logs, hardware bottlenecks, peak loads and other granular details. With advanced analytics and machine learning, CSPs expand their ability to monitor and manage network capacity, build predictive capacity models and prioritize and plan network expansion.
  • Operational analytics: Ensuring peak operational performance is key to reducing costs, mitigating risk and growing revenues. Day-to-day operational data sources offer actionable insights across a CSP’s organization, including revenue assurance, cybersecurity, fraud, financial forecasting and equipment maintenance. For example, big data analytics combined with machine learning and AI enable CSPs to collect and analyze log data, find anomalies and alert security teams. Advanced analytics, machine learning and AI enable CSPs to detect fraud in real time, minimizing false positives and identifying known and unknown types of fraud. According to research from the Communications Fraud Control Association, CSPs can discover up to 350% more fraud incidents using data analytics and machine learning.[4]

The blueprint for big data and communication service providers with IBM and Cloudera

IBM and Cloudera provide the solutions, products, services and multi-vendor support needed to help leading healthcare organizations build, manage, govern, access and analyze data in a secure, governed data lake. Users can benefit from:

  • Faster ROI – end-to-end capabilities enable advanced analytics, machine learning and AI from data lakes and from connecting clouds with traditional infrastructures such as data warehouses or EHR systems.
  • Industry expertise – IBM and Cloudera are experienced in building an integrated vision to deliver specific opportunities for healthcare facilities, from a 360-degree profile of patients, to cybersecurity and compliance, to empowering innovation for research, telemedicine and other care priorities.
  • Security and governance – Leverage Cloudera’s Shared Data Experience to ensure that all data is always secure and governed, anywhere, from the edge to AI.
  • Speed to innovation – Together, IBM and Cloudera have the largest number of contributions to the open source community, ensuring increased availability and interoperability across all vendors.
  • One-stop support and single-pane-of-glass management – Reduce costs, eliminate finger-pointing and maximize availability and agility.
  • Freedom of choice – Enjoy the flexibility to modernize existing on-premises infrastructure as well as the ability to leverage next-generation hybrid and multi-cloud platforms.

The next step to accelerating the benefits of 5G adoption for your customers

Let IBM and Cloudera help you turn the big data at your fingertips into operational improvements and happier customers. Dive deeper into use cases for Communication Service Providers with TechTarget’s recent paper, or schedule a free one-on-one consultation with one of our experts on our website.

[1] “Big Data Analytics in Healthcare Market Worth $67.82 Billion by 2025, Says AMR,” Allied Market Research, May 7, 2020

[2] “AI in Telecommunications Market – Global Forecast to 2022,” MarketsandMarkets, February 2018

[3]  “Global Telecom Survey Sheds New Light on the Status of Fraud Within the Industry,” Communications Fraud Control Association, Nov. 21, 2019

[4] “Global Telecom Survey Sheds New Light on the Status of Fraud Within the Industry,” Communications Fraud Control Association, Nov. 21, 2019

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