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Big Data in Telecom

Tilani Gunawardena

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• Introduction • Big Data in Telecom• Programming Model

Content

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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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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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Data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze

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What is Big Data?

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Big Data

• Exabyte , Zettabyte of data• Big Data is not about the size of the

data,it’s about the value within the Big DataBig Data

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The Structure of Big Data• Structured– Most traditional data sources

• Semi-structured– XML,JSON

• Unstructured– FB logs, web chats, Youtube

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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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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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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

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Key Business Application Categories in Telecom Big Data Analytics

• Customer Experience Enhancement• Network Optimization• Operational Analytics• Data Monetization

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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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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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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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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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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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• 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

MapReduce Workflow

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Big Data Vendors

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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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Thank You !