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226-Sept-2013
326-Sept-2013
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
426-Sept-2013
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
526-Sept-2013
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
626-Sept-2013
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
726-Sept-2013
Use cases
 Banking/insurance/finance
 Telecommunications
 Life science/Healthcare
 Government
 Retail
 Energy
 Media
 Manufacturing
826-Sept-2013
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
926-Sept-2013
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
1026-Sept-2013
Legacy Application architecture
Application server
Network
Database
Data transfer
1126-Sept-2013
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
1226-Sept-2013
Role of Big Data in Financial Services
1326-Sept-2013
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
26-Sept-2013 14

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Big data seminar at Broadridge

  • 3. 326-Sept-2013 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
  • 4. 426-Sept-2013 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
  • 5. 526-Sept-2013 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
  • 6. 626-Sept-2013 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
  • 7. 726-Sept-2013 Use cases  Banking/insurance/finance  Telecommunications  Life science/Healthcare  Government  Retail  Energy  Media  Manufacturing
  • 8. 826-Sept-2013 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
  • 9. 926-Sept-2013 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
  • 10. 1026-Sept-2013 Legacy Application architecture Application server Network Database Data transfer
  • 11. 1126-Sept-2013 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
  • 12. 1226-Sept-2013 Role of Big Data in Financial Services
  • 13. 1326-Sept-2013 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