Predictive analytics and proactive automation in mobile telecommunication industry

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1 Predictive analytics and proactive automation in mobile telecommunication industry Jari Lehtonen Milan, February 3rd, Nokia 2016

2 Mobile communications evolution requires analytics and automation Communication and digital services become unmanageable by conventional means, requiring analytics and automation 2 Nokia 2016

3 Mobile communications use cases and used data for analytics and automation Nokia Telecom services, digital services SW and HW products, professional services Mobile operators Network management Service and customer experience management Security and privacy Data monetization > 95% of data volume, 2017 estimate 10 EB / month, 10 % of global internet traffic (*), TBs per sec globally 53 % CAGR, in 2020 ~20% of internet traffic (*) Strict real-time (RT) analytics, not stored Slow (2%) revenue and negative (-2%) ARPU growth User data (cellular traffic) Applications used Quality of experience, security 3 Nokia 2016 (*) < 5% of data volume Increasingly RT analytics, small fraction stored Complex, structured/ semi-structured, huge amount of variety/ features Network and business internal data: Performance, Configuration, Fault, Equipment logs, Signaling, Subscription policies, Accounting & charging, Security, Geolocation/position End-user device data (*) Global Cellular Traffic Cisco VNI Mobile Forecast, Feb 01, 2016; Cisco VNI Forecast and Methodology, er device data, June 2016 <<< 0.1% of data Normal big data, Offline analysis, stored External data Internet, analysts, biz

4 Use case examples Network performance management Based on (thousands of different) collected performance data from network elements describing e.g. element and interface load levels etc. Predictions based on time series analysis can estimate e.g. where and when capacity upgrades are required Anomaly detection to verify the quality of SW upgrades has been implemented by learning the correct values of performance data. Troubleshooting / debugging Based on triggered in depth analysis data collected after fault indication. Also ring buffers/black boxes can be used to store the latest status log of the elements for the sake of possible failure. Network elements can provide humongous amounts of log data. This data is typically semistructured legacy/ dark data that is very difficult for humans to interpret. Automated analysis techniques have been tried, but the task is far more difficult than analyzing performance data. More detailed E2E data collection and analytics is needed for root cause analysis. 4 Nokia 2016

5 Use case examples Service and customer experience management Mobile operators are moving from technology driven network management towards service and customer experience management. This requires analytics and automation. Lots of different data needs to be collected, joined and analyzed. Used tools are typically based on offline analyics ecosystems (e.g. Hadoop). Traffic management based on real-time customer experience analytics is emerging. Security and privacy Security and privacy are absolute must requirements in all analytics and automation. Collecting, storing and analyzing data from customer networks can only be done if in compliance with local and international laws, regulations and customer requirements. Security and privacy features like e.g. ccess control, secure connections, data scrambling (removing sensitive information) needs to be properly implemented. Security analytics is complex and becomes even more so in the cloud environment. 5 Nokia 2016

6 Use case examples Automation Network management automation is known as Self Organizing Networks (SON), and is a a well established branch, covering self-configuration, self-optimization and selfhealing. In SON analytics is used for different purposes, e.g. anomaly/problem detection and also when automatically verifying the correct operation of the network after automated actions. Self-operation is a kind-of application of automated case-based reasoning to network management. Manual operator actions are learnt and reused in order to increase automation. $ Data monetization Cellular operators are interested in monetizing the data they own. This typically means selling anonymized data about their subscriber base to third party service providers. A written consent of the subscribers is typically required. Use cases are typically related to targeted advertisement or e.g. traffic or other infrastructure planning. Subscriber geolocation is in most cases a key element in the data to be sold. Improved subscriber geolocation accurracy, indoor positioning and lower technological cost are important for monetization business evolution. 6 Nokia 2016

7 Use case examples 5G introduces support for new industry verticals and use cases such as e.g. Automotive, Health, Events & Tourism, Industry 4.0, Mega cities, Homes & Building, Public Safety, Broadcasting/Multicasting. Networks are cloud based and provide unseen performance (>1 Tbps /km2 and < 1 ms latency). Edge computing enables extremely low latency analytics and automation. Predictive and prescriptive analytics must be implemented natively to the architecture instead of adding-on afterwards. 5G network and cloud must support both analytics of the operators and the verticals. Extreme Mobile Broadband Massive machine communication Critical machine communication 7 Nokia 2016

8 Examples of tools and technical solutions Nokia OSS SW products Internal tools SON automation Self-operation (automated case based reasoning) Nokia SW products for Customer Experience Management, Security Performance data prediction and anomaly detection using time series analysis Lots of different tools, e.g. Log data mining research Global remote framework Strict real-time traffic analytics, e.g. for security and deep packet inspection 8 Nokia 2016 Online data collection triggering and analytics

9 Example future R&D topics Service design & creation Introducing general purpose analytics and closed loop automation capabilities for policy and metadata based cloud native service design, creation and operational management framework. Operational management Exploiting distributed realtime analytics and automation capabilities in central and edge cloud. Introducing Edge Cloud Computing for Internet of Things analytics and automation Research topic examples Mining of complex data, e.g. logs, end-to-end session traces, DPI data Automating data science and making it easier for humans to apply Cloud management automation SW and system engineering analytics 9 Nokia 2016 Geolocationing/positioning Introducing intelligent (automated ondemand) data collection from network

10 10 Nokia 2016 Thank you!