Table of Contents
- Business drivers for CSPs
- Key benefits of big data analytics for the communications service provider industry
- Big data analytics
- From data to insights across the CSP network
- Technology requirements to support a new class of analytically powered applications
- Key takeaways
- About William McKnight
- About GigaOm
The communications service provider (CSP) industry has undergone a dramatic shift in recent years. The traditional model of competing on subscription plans is no longer an adequate business strategy. Since most internal systems were built with this model in mind, these environments, with non-enriched, non-integrated, and latent data, fit for after-the-fact reporting, are struggling to keep up with the changes.
This research report will explain how CSPs establish a framework for their analytics as well as review the business drivers for telcos and the key benefits that big data analytics provide. It will also address the impact of the business drivers and the advantages of streaming analytics, combined with the ability to harness big data to meet several CSP competitive requirements. It will conclude by summarizing this comprehensive big data analytics framework for CSPs.
Key findings in this analysis include:
- Three main pillars epitomize the competitive environment in telecommunications today: stagnating or declining average revenue per user (ARPU), over-the-top (OTT) competition, and marginalized profits.
- The enriched, integrated, timely, and all-inclusive data known as analytical data forms the basis for telco competition today.
- Key benefits of big data analytics in telecommunications companies include more-accurate capex planning, the creation of new revenue sources, a reduction in opex, and more-precise marketing, upsell, and resell opportunities and the ability to improve the customer experience.
- Telco competitive requirements include customizing marketing messages, analyzing call-detail records, proactive equipment servicing, infrastructure investment protection, context-sensitive bandwidth allocation, and product development.
- Meeting the performance requirements necessitates a comprehensive big data analytics framework, which includes enterprise data warehousing, analytic databases, data integration, scale-out architectures, and real-time stream processing.
- Those telcos transitioning to SDN, SON, and NFV technologies will find that streaming analytics can support and optimize their investments.