Analytics in Telecom Operations. Whitepaper

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Angelica Ryan
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1 Analytics in Telecom Operations Whitepaper

2 Contents 1. Need of Analytics for Telecom Operators 3 2. Science in Telecom Big Platforms Machine Learning Deep Learning in Telecom 4 3. Analytics categories in Telecom 4 4. Operational Analytics 6 5. Analytics Use Cases in Telecoms 8 6. Value proposition Business Benefits Conclusions References 13 Page 2

3 1. Need of Analytics for Telecom Operators Analytics is changing the way we operate and manage Telecom networks. It helps augment human intelligence and capture new levels of competitive advantage to improve efficiency and reduce cost of operations. It is introducing a paradigm shift in the way networks are run, delivering operational effectiveness, speed in decision making and improving network quality and user experience using machine learning and artificial intelligence. Analytics is helping us introduce predictive and cognitive capabilities in network monitoring and operations, helping operations team take pre-emptive actions to resolve issues before they affect their subscribers. With a growing number of subscribers and internet applications, data traffic and usage worldwide reached 100 exabytes in 2016 and will exceed 450 exabytes in World-wide mobile subscriptions will exceed 9 billion by Telecom operators receive voluminous, high velocity and a wide variety of data which demands an advanced Big Analytics platform to process this large varied data with lightning speed and extract intelligent analytics in real time to open new business opportunities for telecom operators. Network Call Record Equipment Logs IoT Geo-Location Telecom Big Lake Subscriber Security Velocity Application Operational Customer Complaint Figure 1. Sources of Big in Telecom 2. Science in Telecom 2.1. Big Platforms Traditionally telecom industry was using commercial databases like MS-SQL or Oracle and performing business intelligence along with open source tools like Pentaho, BIRT and Jaspersoft, which were processing structured data and delivering interactive reports and dashboards. With a growing number of mobile subscribers, variety of applications and digitization of information, telecom industry is witnessing large amount of unstructured data from variety of sources which is difficult to handle with existing Page 3

4 databases using batch processing approach. Now big data platforms are leveraging distributed processing approach and easily handle unstructured data. The most common big data approaches are based on Hadoop and Apache Spark which use HDFS, Cassandra, No-SQL and MongoDB databases Machine Learning Machine Learning is an approach to find hidden insights out of data using a variety of complex algorithms to understand the behaviour of data towards decision which makes organizations intelligent and act pro-actively to take the right business decisions. Predictive analytics are the domain of big data and machine learning and depend on subtle pattern detection in large datasets that are otherwise humanly impossible to discern. Telecom operators increasingly adopt predictive analytic technologies to deal with real-time requirements, such as security, fraud, and time- or location-based marketing opportunities. This is where big data and machine learning become important: analysing and interpreting the large quantities of data, spotting anomalies, predicting future conditions and opportunities and even prescribing operations for the network Deep Learning in Telecom Deep Learning is an area of Machine Learning research that uses a model of computing that is very much inspired by the structure of the brain. This has been introduced with the objective of moving Machine Learning closer to Artificial Intelligence. Deep Learning uses a cascade of many layers of non-linear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). Deep Learning machines, with massive amounts of computational power, can now recognize objects, translate speech in real time, use textual data parsing and sentiment analysis to act smartly. In telecoms, deep learning is useful to analyse voice data and make it available for textual analysis, predicting customer churn, fraud detection and video analytics to find suspicious activities. 3. Analytics categories in Telecom The Telecom Industry is witnessing data from a variety of sources and analysing data for Customer Experience Management, Network Management, Service Management etc. Telecom players were using business intelligence software to get a descriptive view of every business vertical. Big sources are opening new business areas with Real- Time Streaming based applications in Fraud Management, Network Alarms, Customer Centric Recommendations, Customer Care, IoT and Monetization. Big Technologies are enabling access of zettabytes of data to build advanced descriptive analytics more efficiently using Business Intelligence software. Machine Learning plays an important role in changing the Telecom industry from reactive to proactive and enable them to find hidden insights by analysing large volumes of data. Machine Learning is performing Predictive Analytics, Classification, Clustering, Recommender Systems, Text Analytics, Sentiment Analysis, Social Media Analytics and Deep Learning to generate value for Telecom operators and build additional revenue streams. Predictive Analytics is an important area of Machine Learning where the system performs analysis on large volumes of history data for a given problem and identifies hidden data patterns using Machine Learning methodologies like Classification algorithms, Regression Analysis and Time Series Forecasting to predict Page 4

5 the future behaviour of objects. For Telecom operators, Predictive Analytics is an absolute boon which provides predictive solutions in subscriber insights, network management including capacity planning and optimization, hardware maintenance, customer care and supply chain management. Predictive Analytics solutions assist telecom operators not only in reduce operating cost and performing efficient capex allocation but also by providing new revenue opportunities, building trust for customer retention and focussed advertising to improve profits. Real-Time Streaming using Big Analytics and Machine Learning enables telecom operators to monitor critical services in real-time across networks like Network KPI Degradation, Hot Spot identification based on Traffic and subscriber data, Critical Alarm Monitoring, Hardware Failure, Real Time Fraud Detection and IOT Devices Functioning and act in real time. With the use of Predictive Analytics, telecom operators can resolve issues before they happen. Telecom operators are always interested in reducing turnaround time to resolve customer issues. Real-Time streaming using Text Mining analyses customer complaints and Trouble Tickets based on textual data and raise real-time alarms to provide better services. Text Analytics Telecom operators have operation centres globally and receive textual data in the form of Trouble Tickets and Customer Complaints which makes a big text repository which could be very important to resolve issues fast. Text Analytics provides Intelligent search, Auto-Categorization and Resolutions Recommendations applications for Telecom Textual data. Text Analytics extends extraction of information from the world wide web to help operators in resolving issues. Nowadays customers are extensively using social media applications like Facebook or Twitter to communicate with operators regarding their services and offers. Text analytics analyses social media text and extracts customer sentiments which gives telecom operators an advantage in addressing their customer pain points and improving their brand image. Telecom Operators are running global delivery centres across the world. The operation team uses their native language, which can then be translated into the most preferred language using Natural Language Processing and could be utilized by others across the globe. Monetization - Big is opening new revenue streams for Telecom operators. Telecom operators are collecting a variety of data like CDR, Subscriber Locations, Subscriber Internet Usage, Websites, Applications and Hot Spots. With the addition of Internet of things (IoT), Telecom operators are also getting sensor data from devices. Telecom operators can not only use this data for internal use but also to sell to other organizations after masking confidential information. This data is very useful for almost all organizations like Tourism, Insurance, Health, Transportation or E-Commerce for targeted advertising to find relevant customers. On the other side, Telecom operators can extract insights from data and grow business verticals like advertising, entertainment, games or e-health. With the adoption of Business Intelligence on Big and Machine Learning insights, the Telecom operator s sales and marketing division are getting valuable information about customer preferences, recommendation of services and offers, churn prediction, capacity planning, analysing customer sentiments, Dynamic Pricing using Demographic & Application usage data, Location, and Social Media and making operators profitable. Page 5

6 4. Operational Analytics Analytics is changing the speed of business. Moving from a reactive to a proactive way of addressing issues, moving from historical data analysis to predicting outages and degradations or from focusing on people and processes to decision automation. Reduce Cost New Revenue Opportunities Operational Analytics Improve Quality Manage Complexity Figure 2. Operational Analytics Value Drivers Operations teams identify unique patterns in data to predict issues and take pre-emptive actions. In a conducted trial, the operations teams identify cell sites which are likely to degrade in the next few day and prioritize their actions. The patterns identify the possible reasons of degradation and assist in root cause analysis. This reduces the time spent per incident and reduces cost. As operators move from current the mode of operations and start using predictive and cognitive capabilities, it would help them drive automation and reduce manual effort. As decision making gets automated, operational teams would benefit in terms of speed and quality and be able to focus on higher value generating tasks. Page 6

7 Operational Maturity Decision Automation Recommendations What should I do? Decision support Predictions What will happen? Interactive dashboards Why did it happen? Decision Action Static reports What happened? Manual process Value Figure 3. Analytics Value vis-a-vis Operational maturity Nokia Managed Services has adopted a Sense Analyze Decide - Act methodology which works on cross-domain data collected from network elements, OSS, digital sensors, network probes, real-time events, social networks, action impacts and external data like weather patterns to performs analytics to find out - What is happening? When it will happen? Who is affected? Where will it happen and Why it happened? This process decides and acts smartly to take the right decision fast and improve operational efficiency. 1 Sense 2 Analyze 3 Decide 4 Act Network including FM, PM Configuration, Trouble Tickets, Maintenance and External including weather, subscriber behavior Use statistical and machine learning methods to autonomously detect, isolate and analyze hidden and complex anomalies in large operational data Generate insights & actions to detect and prioritize network incidents & operational alerts much earlier than Telcos currently do in their operational environments Predictive Alerts Assist Root Cause Analysis Prioritize Actions Radio Preemptive Actions Figure 4: Sense Analyze Decide - Act Framework Page 7

8 5. Analytics Use Cases in Telecoms To address multiple domains in an operator environment, use cases are categorized in multiple ways. They provide insights to improve efficiency and reduce cost of operations including maintenance and support. They provide insights to improve network availability and quality including real time insights into Network Planning and Optimization. Analytics combine user, device and network data to support and improve subscriber network and service experience. Analytics provide a complete view of subscriber behaviour to offer the most relevant personalized services to drive higher customer loyalty and lifetime value. Analytics democratize telecom data and monetize it for their enterprise and end customers. Finally, analytics provides capability to IoT applications related data generated from operators and vertical industries. Operational Insights This vertical generates insights from Trouble Tickets & Customer Complaints using Intelligent Search, Auto-Categorization, Recommending Resolution, Sentiment Analysis and Real Time Monitoring using Text Mining and Machine Learning Minimize Repeat issues Field Trip Optimization Intelligent information retrieval from Trouble Tickets & Customer Complaints data Auto-categorization of Trouble Tickets & Customer Complaints resolutions Auto-recommendation for resolutions of Trouble Tickets & Customer Complaints Identify and predict network hot-spots based on traffic volume or capacity issues. Network Insights Network Analytics is focusing on the improvement of network quality and network availability by analyzing network KPI S and counters, network traffic and alarms data. The major analytics use cases are as follows: Anomaly detection based on real-time monitoring of Uplink/Downlink/Total Traffic Prediction of network KPI degradation/non-degradation Cell level degradation prediction. Alarm prediction from alarms generation pattern and signature identification Configuration parameters association analysis to make appropriate changes in network Recommendation of locations based on low network coverage and network capacity Root Cause Analysis for network failures using system and network log data Sleeping Cell prediction via pattern analysis Subscriber Insights Analyzing Subscriber behavior to extract the right information and events from data traffic in near realtime using Statistical and Machine Learning methods Recommendation of services based on subscriber s locations Classification of customers based on their network usage Profiling of customers based on their locations, devices and applications usage Focused advertisements using subscriber s call pattern, applications usage and location Sentiment Analysis to get customer positive and negative reactions Page 8

9 Social Media Analytics to analyse Telecom operator s Twitter and Facebook groups to address complaints and issues proactively Subscriber churn prediction Subscriber demand prediction Cloud Operations Insights Service Management Centralized customer focused complaints repository and using Text Analytics to act and recommend solutions Response Time analytics to access applications by end-users Real Time dashboard for end users to check performance of their applications Forecasting of IT Infra capacity planning for each location Cloud Monitoring Real-Time monitoring and dynamic allocation of IT infra Preventive action to avoid interruption of services Location Analytics to target most affected locations and respective services Application Health Health check of applications running on cloud and diagnostics for mal-functioning applications Knowledge repository of issues and actions using Text Analytics to recommend solutions across locations Predictive maintenance for cloud based applications Security Operation Insights Cyber Security Integrated Real-Time Monitoring of security firewalls to prevent network systems from Spam, Malware and Fraud Machine learning based analysis to predict hidden attacks, suspicious traffic pattern and persistent threat activities for corrective actions Vulnerability Subscriber Security from misbehaving software and malware drives - Machine Learning solutions can discern subscribers using historical patterns, IP headers, destinations and traffic behavior Fraud Detection Robust fraud detection framework using subscriber usage profiles, location data, corporate, and industry-wide data using Machine learning Detecting abnormal subscriber consumption behavior to raise fraud alert and drive processes to block transactions Subscription Fraud Page 9

10 IOT Application Insights Smart Connectivity Advanced Operations Analytics to detect network incidents Real time streaming based operation assistance dashboard for IOT applications Faster complaint and trouble ticket analysis using text mining Recommended RCA by analyzing resolution repository across devices Prediction of Traffic and subscribers to plan network capacity efficiently Smart Health Integrated Real-Time streaming analytics across millions of wearable / portable devices to monitor patient data across hospitals Predictive Maintenance of medical devices Prioritize network incidents and minimize repeat issues Automated upgradation of remote devices configuration/software/firmware Smart Transportation Operation Control Center to make efficient transportation using automatic Vehicle Location System Real-Time electronic fare analysis system to detect fraud Real-Time alerts of oil pilferages Focused advertising based on travelers and locations Real-Time availability of parking and its prediction to improve revenue To deliver analytics services to telecom operators, Nokia has a cloud based, open source delivery platform called Nokia AVA (Analytics, Virtualization and Automation). The global services platform is used to deliver Big processing, Machine Learning, Real-Time Streaming, Text Mining and Business Intelligence capabilities for advance analytics functionality. Nokia AVA stands for Analytics - Advance analytics using Machine learning to recognize anomalies early and take corrective action before affecting customer experience Virtualization - Cloud-based flexibility to enable new use cases to be deployed in days or hours, instead of weeks Automation - Extreme automation to quickly and accurately implement and configure networks Page 10

11 Insight Execution Insight Generation Visualization Business Intelligence Machine Learning Lake ETL / Mediation Layer Multi-vendor Multitechnology network governance Telco & Enterprise IT Monitoring ; Asset & Vendor Management Figure 5. Delivery platform Functional view 6. Value proposition The value of analytics in telecom operations are very diverse, be it helping operations in automated decision-making or reducing workload. It helps field teams in optimization and efficient planning of truck roll-outs for field maintenance trips. It provides faster time to resolution and manages network complexity at lower costs in network operation centres and helps customer care teams in resolving customer complaints faster and doing first time right. Network operations also use analytics to provide faster and deeper insights to improve network availability and quality, it provides insights into value based network planning and optimization and in forecasting network capacity faster and more accurately. Subscriber analytics provides deeper insights in improving end user experience using micro segmentation, geo-location, device and application usage information. It also helps in improve content consumption by subscribers. Analytics also helps telecom operators get a 360-degree view of subscriber behavior to offer most relevant personalized services to drive higher customer loyalty and lifetime value, this benefits in reducing customer churn, improving brand engagement and maximizing subscriber lifetime value. Page 11

12 7. Business Benefits Operators look at three key domains to derive benefits from Analytics, to drive operational effectiveness and save costs, to improve customer experience and loyalty and to generate revenue growth. Multiple value drivers address these business benefits and targets can be set to achieve these benefits by implementing supporting analytics use cases. Business Objective Value Driver Business Benefits Business Target Improve Operational Effectiveness Improve Asset Utilization Drive Automation Faster Root Cause Analysis Reduce CAPEX Reduce OPEX Improve Resource Efficiency MIN TARGET Improve Subscriber Experience & Loyalty Improve Subscriber Experience Improve Customer Loyalty Reduce Customer Complaints Increase Network Usage Reduce Churn Reduce Support Costs More wallet share per subscriber Additional Revenue Drive Revenue Growth New Product & Services Sales Lift New subscriber acquisition Additional Market Share Figure 6. Analytics Business benefits and Value mapping 8. Conclusions Telecom Operators globally are adapting advance analytics to drive operational efficiency, improve customer experience and loyalty and increase revenue generating opportunities based on the large amount of data they have. The business benefits of investing in analytics are tremendous, from strategic decision making to responding to competition. Telecom operators can address a specific pain point like reducing workload in their operation centres or a focus on business targets like reducing subscriber churn or improve sales in a region. Cloud based analytics will further help operators deploy faster and reduce the overhead of managing infrastructure. It also provides a flexible approach to operators to scale and grow as per their business requirements. Working with a partner who has deep telco domain expertise, skills in data science technologies, knowledge repositories and best practices to develop new use cases in a dev ops mode will help operators to address their pain points and requirements more effectively. Page 12

13 9. References 1. Big Key Building Block for CSP s Quest for Value, Capgemini, 2014, resource-file-access/resource/pdf/big_data_-_key_building_block_for_csps_quest_for_value.pdf 2. Big and Machine Learning in Telecom, ABI Research Report, Sep 2016, com/market-research/product/ big-data-machine-learning-in-the-telecom-n/ 3. Benefitting from Big A new approach for the telecom industry, PwC, 2013, strategyand.pwc.com/media/file/strategyand_benefiting-from-big-_a-new-approach-for-the- Telecom-Industry.pdf 4. Big : The next frontier for innovation, competition, and productivity, McKinsey & Company, June 2011, 5. Analytics, Virtualization, and Automation deliver flawless network performance, Nokia, 2016, /en_int/news/releases/2016/02/08/nokia-ava-the-new-cloud-based-cognitiveplatform-for-fast-flawless-service-delivery-to-operators-mwc16 6. Deep Learning in Neural Networks: An Overview, Jurgen Schmidhuber, The Swiss AI Lab IDSIA, Oct 2014, 7. A very brief overview of deep learning, Maarten Grachten, Austrian Research Institute for AI, 8. The Forrester Wave: Big Text Analytics Platforms, Q2 2016, e+forrester+wave+big++text+analytics+platforms+q2+2016/-/e-res Page 13

14 Nokia is a registered trademark of Nokia Corporation. Other product and company names mentioned herein may be trademarks or trade names of their respective owners. Nokia Oyj Karaportti 3 FI Espoo Finland Tel (0) Product code SR EN Nokia 2017 Page 14

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