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Big Data Analytics
– Challenge and Opportunity
83x


6,000,000 users on Twitter         500,000,000 users on Twitter
   pushing out 300,000               pushing out 400,000,000
      tweets per day                         tweets per day
                                     1333x
Where is big data coming from?
                                                                           4.6
                                                   30 billion RFID
                                                                       billion
                         12+ TBs                       tags today
                                                                        camera
                        of tweet data                (1.3B in 2005)
                                                                        phones
                         every day                                       world
                                                                          wide

                                                                       100s of
                                                                      millions
           data every
? TBs of




                                                                       of GPS
              day




                                                                      enabled
                                                                        devices
                                                                           sold
                               25+ TBs                                 annually
                                    of                                      2+
                                 log data                              billion
                                every day                                people
                                            76 million smart meters      on the
                                             in 2009… 200M by 2014      Web by
                                                                       end 2011
The Characteristics of Big Data
Cost efficiently           Responding to the                     Collectively analyzing
processing the
                           increasing Velocity                   the broadening Variety
growing Volume
   50x           35 ZB                     30 Billion
                                           RFID                                 80%   of the
                                           sensors and                          worlds data is
                                           counting                             unstructured
 2010     2020



        Establishing the   By 2015, 80% of all available data will be uncertain
                           -   The number of networked devices will be double the entire
        Veracity of big        global population
        data sources       - The total number of social media accounts exceeds the entire
                             global population
Big Data is a Hot topic
- Because it is possible to Analyze ALL Available Data
• The percentage of available data an enterprise can analyze is decreasing proportionately to
  the available to that enterprise
– Quite simply, this means as enterprises, we are getting ―more naive‖ about our business over time
• Just collecting and storing “Big Data” doesn’t drive a cent of value to an organization’s
  bottom line
• Cost effectively manage and analyze ALL available data in its native form
  unstructured, structured, realtime streaming…….Internal and external

                                                    Data AVAILABLE to
                                                     an organization



                                                                                     Data an organization
                                                                                        can PROCESS
Business-centric Big Data Platform

                                • ―Big data‖ isn’t just a technology
                                  —it’s a business strategy for
                                  capitalizing on information resources

                                • Getting started is crucial

                                • Success at each entry point is
                                  accelerated by products within the
                                  Big Data platform

                                • Build the foundation for future
                                  requirements by expanding further
                                  into the big data platform



6
Different data workloads have different characteristics


                                   Database services that handle
                                   large volumes of transactions with
         System for Transactions   high availability, scalability and integrity

                                   Data Warehouse services for
         System for Analytics      complex analytics and reporting
         powered by                on data up to petabyte scale -
         Netezza technology        with minimal administration

                                   Operational Warehouse services for continuous
                                   ingest of operational data, complex analytics, and
         System for                a large volume
         Operational Analytics     of concurrent operational queries
Big Data Analytics – A national research
initiative
Big Data Analytics – A national research initiative

Daniel Gillblad
Research Group Leader, Senior Research Scientist
SICS, Swedish Institute of Computer Science
Background

• There is a very large potential, both societal and
  commercial, in the analysis, refinement, modeling,
  and visualization these data sets
• Capacity to store, transfer, and search is not enough -
  analytics is critical
Additional business value of Analytics

• Predict and optimize business outcomes
• New services and applications, both for end-users
  and industry
• New value chains, were different actors can create and
  exchange new analysis services
A national Big Data Analytics initiative

① A strategic nation-wide research and innovation agenda
   – Input from several sectors and application areas
   – Both new businesses built on analytics applications
     and traditional industry
   – Input from academia, both as developers and as users
② A national Big Data Analytics network
   – Open to all interested parties
   – Industry and academia with an active interest in Big Data Analytics
Focus areas

                     Control and planning


                     Visualization


   Focus areas
                 {   Analytics

                     Computation

                     Storage

                     Collection
Current constellation
Research and development challenges

• Huge businesses are built on Big Data Analytics today,
  but a large number of issues must be resolved to fully
  realize the potential

• Three examples
Example 1: Large-scale physics experimentation




• Challenges: Scale (storage, computation), scalable analytics
Example 2: Social network mining




• Challenges: Unstructured data, biased data, data access
Example 3: Access network pattern mining




• Challenges: Integrity issues, distributed
  mining, service frameworks
Long term trends

• Currently dominating approach will continue to be successful, but
  will be complemented due to
    – Too much data, unstructured data, noisy data
    – Limited access – security, integrity, legal, and business
    – Fast data generation, situation awareness
• The consequences are
    –   Analysis closer to data generation / collection
    –   No storage - Catching information on the fly
    –   Distributed analysis with incomplete data
    –   Real time collection, real time analytics
Research challenges

• Research challenges on different levels:
   –   The sensor/collection level
   –   The algorithmic/analytical level
   –   The system level
   –   The organisational level
Technical challenges, examples

•   Computational and storage framework development
•   Analysis of unstructured data
•   Distributed analysis
•   Efficient analysis algorithms
•   Stream mining
•   Managing sample bias
•   Managing uncertain and missing data
Platform and organisational challenges, examples

• Service and analytics frameworks, exchanging models and data
• API:s and standards

• Privacy, integrity, security, and legal
• Business models
Contacts

• If you are interested in the Swedish Big Data Analytics Network,
  feel free to contact


       Daniel Gillblad                Anders Holst
       dgi@sics.se                    aho@sics.se
       +46 8 633 15 68                +46 8 633 15 93
IBM Smarter Business 2012 - Big Data Analytics

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IBM Smarter Business 2012 - Big Data Analytics

  • 1. Big Data Analytics – Challenge and Opportunity
  • 2. 83x 6,000,000 users on Twitter 500,000,000 users on Twitter pushing out 300,000 pushing out 400,000,000 tweets per day tweets per day 1333x
  • 3. Where is big data coming from? 4.6 30 billion RFID billion 12+ TBs tags today camera of tweet data (1.3B in 2005) phones every day world wide 100s of millions data every ? TBs of of GPS day enabled devices sold 25+ TBs annually of 2+ log data billion every day people 76 million smart meters on the in 2009… 200M by 2014 Web by end 2011
  • 4. The Characteristics of Big Data Cost efficiently Responding to the Collectively analyzing processing the increasing Velocity the broadening Variety growing Volume 50x 35 ZB 30 Billion RFID 80% of the sensors and worlds data is counting unstructured 2010 2020 Establishing the By 2015, 80% of all available data will be uncertain - The number of networked devices will be double the entire Veracity of big global population data sources - The total number of social media accounts exceeds the entire global population
  • 5. Big Data is a Hot topic - Because it is possible to Analyze ALL Available Data • The percentage of available data an enterprise can analyze is decreasing proportionately to the available to that enterprise – Quite simply, this means as enterprises, we are getting ―more naive‖ about our business over time • Just collecting and storing “Big Data” doesn’t drive a cent of value to an organization’s bottom line • Cost effectively manage and analyze ALL available data in its native form unstructured, structured, realtime streaming…….Internal and external Data AVAILABLE to an organization Data an organization can PROCESS
  • 6. Business-centric Big Data Platform • ―Big data‖ isn’t just a technology —it’s a business strategy for capitalizing on information resources • Getting started is crucial • Success at each entry point is accelerated by products within the Big Data platform • Build the foundation for future requirements by expanding further into the big data platform 6
  • 7. Different data workloads have different characteristics Database services that handle large volumes of transactions with System for Transactions high availability, scalability and integrity Data Warehouse services for System for Analytics complex analytics and reporting powered by on data up to petabyte scale - Netezza technology with minimal administration Operational Warehouse services for continuous ingest of operational data, complex analytics, and System for a large volume Operational Analytics of concurrent operational queries
  • 8. Big Data Analytics – A national research initiative
  • 9. Big Data Analytics – A national research initiative Daniel Gillblad Research Group Leader, Senior Research Scientist SICS, Swedish Institute of Computer Science
  • 10. Background • There is a very large potential, both societal and commercial, in the analysis, refinement, modeling, and visualization these data sets • Capacity to store, transfer, and search is not enough - analytics is critical
  • 11. Additional business value of Analytics • Predict and optimize business outcomes • New services and applications, both for end-users and industry • New value chains, were different actors can create and exchange new analysis services
  • 12. A national Big Data Analytics initiative ① A strategic nation-wide research and innovation agenda – Input from several sectors and application areas – Both new businesses built on analytics applications and traditional industry – Input from academia, both as developers and as users ② A national Big Data Analytics network – Open to all interested parties – Industry and academia with an active interest in Big Data Analytics
  • 13. Focus areas Control and planning Visualization Focus areas { Analytics Computation Storage Collection
  • 15. Research and development challenges • Huge businesses are built on Big Data Analytics today, but a large number of issues must be resolved to fully realize the potential • Three examples
  • 16. Example 1: Large-scale physics experimentation • Challenges: Scale (storage, computation), scalable analytics
  • 17. Example 2: Social network mining • Challenges: Unstructured data, biased data, data access
  • 18. Example 3: Access network pattern mining • Challenges: Integrity issues, distributed mining, service frameworks
  • 19. Long term trends • Currently dominating approach will continue to be successful, but will be complemented due to – Too much data, unstructured data, noisy data – Limited access – security, integrity, legal, and business – Fast data generation, situation awareness • The consequences are – Analysis closer to data generation / collection – No storage - Catching information on the fly – Distributed analysis with incomplete data – Real time collection, real time analytics
  • 20. Research challenges • Research challenges on different levels: – The sensor/collection level – The algorithmic/analytical level – The system level – The organisational level
  • 21. Technical challenges, examples • Computational and storage framework development • Analysis of unstructured data • Distributed analysis • Efficient analysis algorithms • Stream mining • Managing sample bias • Managing uncertain and missing data
  • 22. Platform and organisational challenges, examples • Service and analytics frameworks, exchanging models and data • API:s and standards • Privacy, integrity, security, and legal • Business models
  • 23. Contacts • If you are interested in the Swedish Big Data Analytics Network, feel free to contact Daniel Gillblad Anders Holst dgi@sics.se aho@sics.se +46 8 633 15 68 +46 8 633 15 93

Editor's Notes

  1. An enormous amounts of data permeate societyBoth the data itself and how it is usedDeeper analysis of audio and video
  2. * A move from instance based to model based approaches