Big data is generated from a variety of sources like web data, purchases, social networks, sensors, and IoT devices. Telecom companies process exabytes and zettabytes of data daily, including call detail records, network configuration data, and customer information. This big data is analyzed to enhance customer experience through personalization, predict churn, and optimize networks. Analytics also helps with operations, data monetization through services, and identifying new revenue streams from IoT and M2M data. Frameworks like Hadoop and MapReduce are used to analyze this distributed big data across clusters in a distributed manner for faster insights.

1. Big Data in Telecom
Tilani Gunawardena
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2. • Introduction
• BigDatainTelecom
• ProgrammingModel
Content
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3. Big Data Everywhere!
• Lots of data is being collected
and warehoused
– Web data, e-commerce
– purchases at department/
grocery stores
– Bank/Credit Card
transactions
– Social Network
– Sensor data
– IoT data
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4. How much data?
• Google processes 20 PB a day (2008)
• Walmart handles more than 1 million customer
transactions every hour.
• Facebook ingests 500 terabytes of new data every day.
• eBay has 6.5 PB of user data + 50 TB/day (5/2009)
• Boeing 737 will generate 240 terabytes of flight data
during a single flight across the US.
640K ought to be
enough for anybody.
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5. Data sets whose size is beyond the ability
of typical database software tools to
capture, store, manage, and analyze
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6. What is Big Data?
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7. Big Data
• Exabyte , Zettabyte of data
• Big Data is not about the size of the data,
it’s about the value within the Big Data
Big Data
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8. The Structure of Big Data
• Structured
– Most traditional data sources
• Semi-structured
– XML,JSON
• Unstructured
– FB logs, web chats, Youtube
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9. Types of Telecommunication Data
• Call Detail Data
– average call duration
– Average call originated/generated
– Call period
– Call to/from different area code
• Network Data
– Complex configuration of equipment
– Error Generation
– To support network management function
• Customer Data
– Database information of customers
– Name, age, address, telephone, subscription type
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10. What to do with these data?
• Aggregation and Statistics
– Data warehouse and OLAP
• Indexing, Searching, and Querying
– Keyword based search
– Pattern matching (XML/RDF)
• Knowledge discovery
– Data Mining
– Statistical Modeling
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11. Big Data Analytics
• Examining large amount of data
• Knowledge discovery/Appropriate information
–Data Mining
–Statistical Modeling
• Identification of hidden patterns, unknown
correlations
• Competitive advantage
• Better business decisions: strategic and
operational
• Effective marketing, customer satisfaction,
increased revenue 11

12. Key Business Application Categories
in Telecom Big Data Analytics
• Customer Experience Enhancement
• Network Optimization
• Operational Analytics
• Data Monetization
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13. Big Data Analytics: Customer
Experience Enhancement
• Targeted Marketing & Personalization
– personalized product offerings (ex: personalized data top-
up plans)
• Predictive Churn Analytic
– address “at risk” customers
• Customer Journey Analytics
– Customer’s interactions at various stages of the lifecycle to
promote tailored offerings and campaigns.
• Proactive Care
– Identify issues and fix it or offer a solution before it
impacts the customer
– ex: Telkomsel, in Indonesia build a proactive dashboard’
for their high value customers
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14. Big Data Analytics: Network Optimization
• Network Capacity Planning & Optimization
– Network usage, subscriber density, along with traffic and location data
– More accurately monitor, manage and forecast network capacity
• Network Investment Planning
– Future connectivity needs, strategic objectives, forecasted traffic, customer
experience etc
– ensure they are investing their CAPEX(Capital expenditure) in the right spots
– Ex: how and where they can expand high-speed broadband services to
customers within sri lanka
• Real-Time Network Analytics :
– real-time data collected from the cell towers, events occurring in the network
can proactively respond to network failures and outages helping them save
millions
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15. Big Data Analytics: Operational Analytics
• Revenue Leakage & Assurance
– Analyze many years data instead few months
– Identify new revenue opportunities
• Cyber Security & Information Management
• Customer Care Optimization
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16. Big Data Analytics: Data Monetization
• Data Analytics as a Service (DAaaS)
– retail, financial services, advertising, healthcare, public
services and other customer-facing businesses.
– Ex: customer footfall analytics which is helping retail chains
decipher who is visiting their stores and when
– trffic patterns and bottlenecks,
• IoT & M2M Analytics
– The number of connected objects representing the IoT
ecosystem is expected to reach 50 Billion by 2020
• New Revenue Engine
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17. Hadoop and Big data
• Wont fit on a Single computer
• Distributed Data
• Distributed Data =Faster Computation
• Telecom Service Providers adopt Hadoop &
big data analytics solutions to turn their data
into valuable business insights
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18. • MapReduce is a programming model for processing and
generating large data sets
• MapReduce was used to completely regenerate Google's
index of the World Wide Web.
• Hadoop which allows applications to run using the
MapReduce algorithm.
MapReduce
• Users implement interface of 2 function
– Map
– Reduce
• Map( in-key,in-value) (Out-key,intermediate-value) list
• Reduce(Out-key,intermediate-value list) out_value list
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19. MapReduce Workflow
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20. Big Data Vendors
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21. Big Data and Cloud Computing
• Cloud computing is the use of computing
resources (hardware and software) that are
delivered as a service over a network
• In Business View: When it’s smarter to rent
than to buy…..
– ”If you only need milk, would you buy a cow? “
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22. Thank You !
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