Telecoms data analytics – Where’s the real value?
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Although nearly all operators aspire to deploy autonomous networks and personalised customer services, few have actually implemented advanced analytics at scale across their organisations. Almost universally, telcos are hampered by incomplete and siloed data sets and cultural resistance. What have the industry’s leaders done to overcome these challenges?
Format: PDF filePages: 37 pages Charts: 09 Author: Darius Singh Publication Date: January 2019
Table of Contents
- Executive Summary
- Future-proofing: what to do?
- Building an advanced telecoms data analytics capability
- High ambitions: data and the AI continuum
- Laying the groundwork: stepping stones toward data analytics
- In practice: Assessing real analytics use cases
- Improve business as usual
- Monetise user data
- Enable next-generation services
- Key recommendations
Table of Figures
- Figure 1: The effect of increasing 4G subscriber penetration on ARPUs
- Figure 2: The journey to AI and telco automation
- Figure 3: Top 4 issues faced by telcos looking to make use of data
- Figure 4: Telefónica’s data management structure across multiple opcos
- Figure 5: What is your biggest challenge in leveraging analytics?
- Figure 6: The opportunity areas for telcos in advanced analytics
- Figure 7: A comparison of Iliad against the leading Italian operators
- Figure 8: A graphical representation of KPN’s Data Services Hub
- Figure 9: Where operators are compared to their AI aspirations
Technologies and industry terms referenced include: AI, analytics-as-a-service, automation, chief data officer, churn management, Culture, data lake, data monetisation, data structure, data-centricity, ML, network optimisation