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Agenda
 What is Big Data
 Big Data Characteristics
 Brief history
 Use Cases
 who are the players
 Challenges to deal Big data with traditional approach
 Research & analysis
 Action plans and future approach
 Q & A
 importance of Big Data in Financial service

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Big Data
Big data technologies describe a new generation of technologies and
architectures, designed to economically extract value from very large
volumes of a wide variety of data, by enabling high-velocity capture,
discovery, and/or analysis--Idc
Big data[ is the term for a collection of datasets so large and complex
that it becomes difficult to process using on-hand database
management tools or traditional data processing applications. The
challenges include capture, curation, storage, search, sharing,
transfer, analysis, and visualization--Wiki.
Every day, we create 2.5 quintillion bytes of data — so much that 90%
of the data in the world today has been created in the last two years
alone. This data comes from everywhere: sensors used to gather
climate information, posts to social media sites, digital pictures and
videos, purchase transaction records, and cell phone GPS signals to
name a few. This data is big data--IBM

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History Behind
 Data process done by processors
 Users Becomes processors and generate their own data into the systems
Usage of Social networking sites
Smart phones
 Machines accumulated the data
Humidity, temperature
Electricity usage
Satellites
 Google published a paper in 2003 about their Distributed File Systems,
computation towards unstructured data
 increasing internet, bandwidth speed
Storage mechanisms implemented a lot

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Velocity
volume
v
variety
Characteristics
 12 terabytes of tweets created
each day
 Airline jets collects 10 terabytes
of sensor data for every 30 mins
of flying time
 Data will grow 800% over next 5
years-Gartner
 text, Sensors data, audio, video,
click streams, RFID, GPS
devices, log files and more.
 80% of data is unstructured or
semi structured
How much data
How fast data is processed
Various types of data
 Facebook has an average of 3.2 billion likes and comments are posted every day
 575 photos uploaded,8500 likes and 7800 comments by Instagram users every
second

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Use cases
 Banking/insurance/finance
 Telecommunications
 Life science/Healthcare
 Government
 Retail
 Energy
 Media
 Manufacturing

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Players
 Global threat analytics
 Virus analysis
 intrusion detection and
prevention
 forensic analysis
 Customer sentiment
 Network analysis
Major credit card issuer
 Recommendation engine
 Fraud detection & prevention
Electronic manufacturer
 Click stream analysis
 Quality profiling
 DNA based
relationship discovery
 Recommendation
engine
Leading retailer
 Customer behavior analysis
 Brand monitoring
 Information retrieval and
extraction of research
project
 Large scale audio
feature analysis

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Traditional systems
 failed to analyze the un structured and semi structured data
 CPU cannot handle the Big data
 RDBMS handles schema based table like structure
 Reading or writing more amount of data to the system is very time
consuming

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Legacy Application architecture
Application server
Network
Database
Data transfer

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Research & Analysis
 There will be a shortage of talent necessary for organizations to take advantage of big
data. By 2018, the United States alone could face a shortage of 140,000 to 190,000
people with deep analytical skills as well as 1.5 million managers and analysts with the
know-how to use the analysis of big data to make effective decisions- McKinsey
 the digital universe will about double every two years-- Idc
 Big data investments in 2013 continue to rise, with 64 percent of organizations
investing or planning to invest in big data technology Gartner
 Global spending on big data by organizations will exceed $31 billion in 2013, finds a
new market forecast by ABI Research. The spending will grow at a CAGR of 29.6% over
the next five years, reaching $114 billion in 2018- ABI Research

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Role of Big Data in Financial Services

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Action plans
 Distributed File Systems
 No-SQL database
 parallel processing
 Schema on Read rather than schema on write
 Machine learning techniques
 implementation of Data analytic tools for unstructured data

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