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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 speedStorage mechanisms implemented a lot

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Velocity

volumev

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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