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Big Data Solution For 
YTT Telecom
YTT Telecom 
● Leading edge mobile voice, data and multimedia services 
Company (63 M customers) 
● Focus on R&D to enrich...
Data Data Everywhere 
YTT Data Challenge Matrix 
POS Data 
Locations 
Payments 
Sensor Data 
Customer Profiles 
Weather 
S...
Problems Highlights 
Smart Devices Data Needs Services Provided 
Customer Churn 
• How to keep the customer happy/satisfie...
Key Trends 
Expected Budget of Telecom Companies for Handling 
Big Data 
Big Data Analytics to optimize 
network performan...
Proposed Solution - Strategy 
Gather 
internal/external 
data 
Ingest and 
standardize data 
Apply S/W Tools 
to Prepare, ...
Solution Tech Stack 
DATA SOURCES 
ERP 
CRM 
Billing 
Records 
Subscriber 
Data 
Network 
data 
Product 
Related 
Data 
Cu...
Proposed Solution Architecture 
Call Detail Records 
Call Center Record 
Network Logs 
Tower CDRs 
Hadoop MapReduce + QoS ...
Solution Design Mock-Up 
Handling Network Congestion 
Tower CDR Log 
Caller A;Caller B;Date;Time;Duration;Call Type;First ...
Deployment- Strategy 
2 Week plan to validate proposed solutions for: 
 Customer churn 
 Network traffic 
 Optimized Ma...
Proposed Solution Benefits
Summary 
• Big data offers YTT Telecom a real opportunity to gain a more complete picture of 
their operations impacting t...
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Big Data and Technology Stack for Telecom Company

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Identified key data patterns to give recommendations for reducing customer churn and better network management

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Big Data and Technology Stack for Telecom Company

  1. 1. Big Data Solution For YTT Telecom
  2. 2. YTT Telecom ● Leading edge mobile voice, data and multimedia services Company (63 M customers) ● Focus on R&D to enrich customer lives ● Adoption rate > 20%. ● 20% users switched to smart phone, > 3 times over 2013 ● Need robust infrastructure to accept rapidly growing network traffic Telecom Eco-system
  3. 3. Data Data Everywhere YTT Data Challenge Matrix POS Data Locations Payments Sensor Data Customer Profiles Weather Shipments Transactions HR Records Financial Records Google+ Twitter Facebook Call Center Data Click Stream Text Messages Online Forums Video Sharepoint 3rd Party Text Documents Velocity Variety and Volume Portal Customer Touch Points Store FB Twitter Yelp Mailers Offers Call Center Email SMS Network Data • Billions of Call Detail Records Location Data • 60 TB of Location Data Customer Data • Millions of records for 63 M customers Structured Unstructured
  4. 4. Problems Highlights Smart Devices Data Needs Services Provided Customer Churn • How to keep the customer happy/satisfied and reduce churn? Network Management • What strategies should YTT apply to store and analyze network data and resolve issues in real time? Marketing Campaign Efficiency • How to run effective and targeted campaigns?
  5. 5. Key Trends Expected Budget of Telecom Companies for Handling Big Data Big Data Analytics to optimize network performance and reduce cost - T Mobile Big Data Analytics for effective promotions Big Data for Real Time Intelligence and control back into the network Responses to Big Data Initiatives
  6. 6. Proposed Solution - Strategy Gather internal/external data Ingest and standardize data Apply S/W Tools to Prepare, Process, Analyze and Export Data  Learn and Label : Segment Customers on the usage patterns, learn preferences, create labels and store with the profile. Create/offer suitable/customized plans.  Empower Customer Service : Allow a representative to help in near real time to resolve issues and make offers. A customer is rated/ranked on the basis of usage, payment history and interests.  Proactive Network Management : Detect network spikes, analyze dropped calls from CDR analysis, inform Customer Service in case of dropped calls to make a friendly call to the customer facing the problem.  Understanding Sentiment : Find out +ve/-ve sentiments on Social Networks/blogs etc. pre/post campaign to see the effectiveness. Derive actionable insights Determine actions on results obtained
  7. 7. Solution Tech Stack DATA SOURCES ERP CRM Billing Records Subscriber Data Network data Product Related Data Customer Behavior Click Stream Online chat Sensor Data Social Media System Operations Server Logs Call Detail Records Merchant Listings Signaling Logs Protocol Logs INGEST Sqoop Flume HDFS.Put Web.HDFS Physical Layer Ad-hoc Query Analysis CDR Analysis Proactive network maintenance Bandwidth Allocation Infrastructure Investment Operational dashboards Customer scorecards Product Development Oracle Workflow Scheduler Pig Data Analytics Hive Data Warehouse Metadata Management : HCatalog Multitenant Processing : YARN Mapreduce Libraries Hbase Database Compute and HDFS Storage Hadoop Distributed File System Analysis Layer
  8. 8. Proposed Solution Architecture Call Detail Records Call Center Record Network Logs Tower CDRs Hadoop MapReduce + QoS Reports, Billing Information Pig Data Analytics Hbase Database + Hive Queries Social Media Data Traffic Reports, Network Audit Reports Hbase Database + Pig Data Analytics Call Volume Reports, Routing Graphs Traditional Datawarehouse + SQL Customer Service Reports, Closed Loop reports Hadoop MapReduce + COGSA Sentiment Analysis Reports, Funnel Reports
  9. 9. Solution Design Mock-Up Handling Network Congestion Tower CDR Log Caller A;Caller B;Date;Time;Duration;Call Type;First Cell ID;Last Cell ID;Cell ID Zip 9096714043;9163281129;8/4/2014;9:45:23;0;SMS-IN;405-799-20-36023;405-799-20-36361;94709 7276789858;9806154895;8/5/2014;9:50:11;1161;CALL-IN;405-799-20-36023;405-799-20-31611;94150 ……………………………………………………………………………………………………………………..... MapReduce Job Generates pairs of (tower id, # calls routed) Tower Number #Calls (in000s) 405-805-105-60382 234 405-805-127-10223 213 405-805-127-33891 206 405-805-127-10221 156 405-805-105-60383 143 …………………….. …..
  10. 10. Deployment- Strategy 2 Week plan to validate proposed solutions for:  Customer churn  Network traffic  Optimized Marketing spend Identify Data Sources Unify And Assemble Data Clean and Enhance Data Quality Append Content Build Analytics Analyze Review Dashboard OK to Proceed  Give us Access to YTT data and approval  Provide following resources:  1 Data Engineer  1 Network Engineer  1 Data Scientist  1 BI Engineer Allow access to Cloud AWS infra (free trial) or equivalent
  11. 11. Proposed Solution Benefits
  12. 12. Summary • Big data offers YTT Telecom a real opportunity to gain a more complete picture of their operations impacting their customers, and to further their innovation efforts. • YTT’s focus on R&D is to enrich customer lives. This solution proposal is in consistence with their focus. • Big data challenge can be met on the lines of the proposed Solution Architecture. • YTT should incorporate new agile strategies into their organizational DNA fast so that it will gain a real competitive advantage over their slower rivals. References: 1. TELECOMS.COM INTELLIGENCE INDUSTRY SURVEY 2014 – http://www.telecoms.com/wp-content/blogs.dir/1/files/2014/03/IndustrySurveyReport14_latest1.pdf

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