big data in telecom

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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