This course addresses the strategic intent of telecom operators for leveraging big data. It provides a big picture of the various Big Data ecosystem tools and platforms and helps you understand how it is different from traditional data analytics.
It will also touch on the various predictive and prescriptive analytics approaches using machine learning that could result in new use cases for a telecom operator’s business.
Furthermore, this course will help you build the right mindset for embracing big data and guide you to frame your own big data use case as per your department’s charter, either as a business, operation or a service function within your organisation.
Overview of training:
Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience. And data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month.
Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity.
Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored.
With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.)
This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain.
- Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office.
- Business Analysts in Telco
- CFO office managers/analysts
- Operational managers
- QA managers
- Should have basic knowledge of business operation and data systems in Telecom in their domain
- Must have basic understanding of SQL/Oracle or relational database
- Basic understanding of Statistics ( in Excel levels)
- Business Over view of Why Big Data Business Intelligence in Telco.
- Introduction of Big Data-1
- Predictive analytics in Business Intelligence -1: Fundamental Techniques & Machine learning based BI
- Predictive analytics eco-system-2: Common predictive analytic problems in Telecom
- Network Operation analytic- root cause analysis of network failures, service interruption from meta data, IPDR and CRM
- Tools for Network service failure analysis
- Big Data BI for Marketing/Sales –Understanding sales/marketing from Sales data: ( All of them will be shown with a live predictive analytic demo )
- BI needed for Telco CFO office
- Fraud prevention BI from Big Data in Telco-Fraud analytic
- From Churning Prediction to Churn Prevention
- How to use predictive analysis for root cause analysis of customer dis-satisfaction
- Big Data Dashboard for quick accessibility of diverse data and display
- How to justify Big Data BI implementation within an organization
- Step by Step procedure to replace legacy data system to Big Data System
- Review of Big Data Vendors and review of their products . Q/A session