Advanced Analytics & AI for Network Operations
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Presentation and panel at Informa's Telco AI Summit, 2019
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Advanced Analytics & AI for Network Operations
- 1. © 2019 Heavy Reading Advanced Analytics & AI for Network Operations Telco AI Summit Europe 6 November 2019
- 2. © 2019 Heavy Reading Panelists 2 Lucy Goudie Customer Experience Manager Telefonica Dima Alkin VP, Service Assurance Solutions TEOCO Moderator: James Crawshaw Senior Analyst Heavy Reading George Iskenderian Director, Big Data & AI Bell Canada
- 3. © 2019 Heavy Reading Use Cases for AI/ML in Telecom 3 92% 75% 67% 58% 58% 42% 42% 33% 25% 25% 25% 8% 17% 33% 42% 42% 58% 50% 58% 67% 50% 42% 8% 8% 8% 8% 25% 33% Predictive maintenance Security Self-organizing networks Network management Fraud/revenue assurance Data monetization CEM Dynamic pricing New business models Strategic marketing Sales Critical Somewhat important Not important at all Source: Heavy Reading Survey – Thought Leadership Council, Nov. 2017
- 4. © 2019 Heavy Reading RoI From AI/ML in Networking 4
- 5. © 2019 Heavy Reading Phased Adoption of AI/ML in Telecom Networking 5
- 6. © 2019 Heavy Reading Type Description Descriptive Analytics Summarizing data and presenting visually in a table or chart to monitor a system or service. Reactive Analytics Raising alarms based on events such as hardware failures. Uses traditional rules and statistical methods such as clustering. Predictive Analytics Predicting events and future states based on time series data. Can use supervised, unsupervised or semi-supervised learning. Algorithms can be linear (e.g., regression) or non-linear (neural networks, support vector machines). Prescriptive Analytics Similar to AI, where the goal is to identify an optimized sequence of steps or actions to take. An example would be optimizing data routing across all network nodes and end devices as traffic changes. Analytics Classifications – From Passive to Proactive 6
- 7. © 2019 Heavy Reading Barriers to Data Science/ML in Telecom 7 49.4% 47.9% 47.9% 33.5% 32.7% 32.3% 27.2% 22.2% 21.4% 21.0% 19.8% 17.1% 17.1% 16.7% 16.0% Dirty data Lack of data science talent Lack of management/financial support Lack of clear question to answer Data unavailable or difficult to access Results not used by decision-makers Privacy issues Integrating findings into decisions Need to coordinate with IT Explaining data science to others Lack of domain expert input Expectations of project impact Multiple ad-hoc environments Limitations of tools Can't afford data science team Source: Kaggle
- 8. © 2019 Heavy Reading TM Forum AI Maturity Model 8 Strategy - Portfolio & ideation - Finance & investment - Strategic management Culture - Culture - Leadership & culture - Organizational design & talent management Party - Engagement - Experience - Trust Operations - Service lifecycle management - Closed-loop service assurance - Governance Data - Data governance - Data curation - Data capitalization Technology - AI Techologies & capabilities - AI Architecture - Automation & applications +54 specific digital criteria