{"id":73618,"date":"2024-05-02T15:23:18","date_gmt":"2024-05-02T13:23:18","guid":{"rendered":"https:\/\/intellias.com\/?p=73618"},"modified":"2024-07-25T15:28:59","modified_gmt":"2024-07-25T13:28:59","slug":"eal-time-big-data-analytics-for-telecom","status":"publish","type":"post","link":"https:\/\/intellias.com\/real-time-big-data-analytics-for-telecom\/","title":{"rendered":"Achieving a 30% Revenue Increase: How CSPs Can Benefit from Real-Time Analytics"},"content":{"rendered":"

Business challenge<\/h2>\n

Today, there are two undeniable trends in telecommunications: the staggering proliferation of data and the supplanting of batch processing with real-time data processing. A US-based cloud communications platform powering telecommunications service providers took advantage of these trends<\/a> and chose Intellias to enhance their expertise in big data analytics.<\/p>\n

With all of their data being generated and processed in real time, our client, a US-based cloud communications platform powering service providers with a broad range of modern carrier-class telecom functions, needed to address a pivotal question: How does these trends impact their telecom analytics strategy?<\/em> The resounding answer: They transform it entirely.<\/p>\n

Here is how we helped our client reach this conclusion and devise a new analytics strategy.<\/p>\n

\n
\n
\n
\n
Discover Benefits of Data Analytics for the Telecom Industry<\/div>\n
\n Get in touch<\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n

Technology solution<\/h2>\n

Real-time analytics is catalyzed by factors such as the transition from 4G to 5G, the surge in IoT adoption<\/a>, and the ever-expanding variety of connected devices. Consequently, communications service providers (CSPs) face a critical imperative: seize new revenue avenues and exert greater network control. This entails embracing innovative revenue models such as pre-paid services and integrated subscriber policy management.<\/p>\n

\n
\n

Working with our client, we started from creating a data strategy<\/a> for the next five years to help the cloud communications platform become data-driven. With a data strategy in mind, we then established the data analytics business process by conducting a design thinking workshop.<\/p>\n <\/div> \n <\/div>\n

\"Our<\/p>\n

We helped at each step of our cleint\u2019s data journey.<\/em><\/p>\n

When working on our data projects, we follow a four-step process that includes a discovery and assessment stage, solution design, implementation, and continuous improvement. This is how we reach long-term benefits such as:<\/p>\n

    \n
  1. Self-service analytics and data mining<\/li>\n
  2. Seamless integration of heterogeneous data sources<\/li>\n
  3. Scalability and minimal latency<\/li>\n
  4. History preservation and time travel functionality<\/li>\n
  5. Auditability and data profiling<\/li>\n<\/ol>\n

    \"The<\/p>\n

    When working on data projects, we go through four stages to reach long-term benefits. <\/em><\/p>\n

    \n
    \n

    The architecture of the real-time analytics platform integrates various technologies to ensure scalability, real-time data processing, and a seamless user experience. Here\u2019s a breakdown of the high-level architecture<\/p>\n <\/div> \n <\/div>\n

    Data ingestion layer<\/h4>\n