This document discusses big data and provides an agenda for a presentation. It defines big data, outlines its key characteristics of volume, velocity and variety. A brief history of big data is given starting from individual data generation on social networks and sensors to Google's distributed file systems. Common use cases are listed across various industries. The challenges of traditional systems to handle big data are presented. Research findings around talent shortage and increasing data volume are summarized. Action plans are proposed to implement distributed file systems, NoSQL databases, and machine learning to extract value from large, diverse, and fast-growing unstructured data.

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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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