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Big Data
Wilfried Hoge, Software IT Architect Big Data
hoge@de.ibm.com

@wilfriedhoge
How is Big Data transforming the way
organizations analyze information and
generate actionable insights?
Paradigm shifts enabled by big data
Leverage more of the data being captured

TRADITIONAL APPROACH

All available
informat...
Paradigm shifts enabled by big data
Reduce effort required to leverage data

TRADITIONAL APPROACH

Small
amount of
careful...
Paradigm shifts enabled by big data
Data leads the way—and sometimes correlations are
good enough
TRADITIONAL APPROACH

BI...
Paradigm shifts enabled by big data
Leverage data as it is captured

TRADITIONAL APPROACH

BIG DATA APPROACH

Data

Analys...
How have most companies made
information available for decision
making across the enterprise?
Traditional enterprise data and analytics
environments
Typical enterprise data management environment
Data sources

Action...
How are leading companies transforming
their data and analytics environment
to provide faster, better insights
at reduced ...
Next generation architecture
Starts from the current data management environment …
Data sources

Actionable insight

Enter...
Next generation architecture
… adds new technologies and capabilities …
Data sources

Real-time analytics

Actionable insi...
Next generation architecture
… to enable new applications
Data sources

Real-time analytics

Actionable insight

Machine a...
Sample use cases that leverage the
new data management environment
capabilities
Dublin City Centre; Robust
and efficient citywide traffic
awareness system, enhance
rapid action on incidents
Need
• 

A b...
Architecture for traffic awareness system
Real-time analytics to enhance customer experience
Data sources

Real-time analy...
Constant Contact
Transforming Email Marketing
Campaign Effectiveness with
IBM Big Data
Need
•  Analyze 35 billion annual e...
Architecture for email marketing
Analyze to maximize response rates
Data sources

Real-time analytics

Actionable insight
...
Battelle, helping reduce
energy costs and enhancing
power grid reliability and
performance
Need
•  Assess the viability of...
Architecture for smart grid
Analytics to reduce costs and optimize grid
Data sources

Real-time analytics

Actionable insi...
Trust is the most important aspect of a
Big Data solution
Trust It
Be proactive about privacy, security and governance

Trust the facts

Ensure privacy
and security

Make risk
awar...
Big Data Privacy and Security
Protect a Wider Variety of Sources

•  Protec&on	
  is	
  a	
  pre-­‐requisite	
  for	
  
th...
Data volumes continue to grow fast.
New technologies will be needed in the
future
Big Data brings Technical Challenges
IBM invests in future solutions
Huge Volumes of Multimedia Data:
New Storage Requirem...
What are your activities to leverage Big
Data Analytics?
Your big data journey
IBM can help wherever you are
Educate	
  

25%

Focused	
  on	
  knowledge	
  
gathering	
  and	
  m...
THINK
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2013.12.12 big data heise webcast

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Presentation about BigData from a German Webcast: http://business-services.heise.de/it-management/big-data/beitrag/big-data-technologie-einsatzgebiete-datenschutz-160.html?source=IBM_12_2013_IT_Conn

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2013.12.12 big data heise webcast

  1. 1. Big Data Wilfried Hoge, Software IT Architect Big Data hoge@de.ibm.com @wilfriedhoge
  2. 2. How is Big Data transforming the way organizations analyze information and generate actionable insights?
  3. 3. Paradigm shifts enabled by big data Leverage more of the data being captured TRADITIONAL APPROACH All available information Analyze small subsets of Information BIG DATA APPROACH Analyzed information All available information analyzed Analyze all information
  4. 4. Paradigm shifts enabled by big data Reduce effort required to leverage data TRADITIONAL APPROACH Small amount of carefully organized information Carefully cleanse information before any analysis BIG DATA APPROACH Large amount of messy information Analyze information as is, cleanse as needed
  5. 5. Paradigm shifts enabled by big data Data leads the way—and sometimes correlations are good enough TRADITIONAL APPROACH BIG DATA APPROACH Hypothesis Question Data Exploration Answer Data Insight Correlation Start with hypothesis and test against selected data Explore all data and identify correlations
  6. 6. Paradigm shifts enabled by big data Leverage data as it is captured TRADITIONAL APPROACH BIG DATA APPROACH Data Analysis Data Repository Analysis Insight Insight Analyze data after it’s been processed and landed in a warehouse or mart Analyze data in motion as it’s generated, in real-time
  7. 7. How have most companies made information available for decision making across the enterprise?
  8. 8. Traditional enterprise data and analytics environments Typical enterprise data management environment Data sources Actionable insight Predictive analytics and modeling Transaction and application data Staging area Enterprise warehouse Archive Data mart Reporting and analysis
  9. 9. How are leading companies transforming their data and analytics environment to provide faster, better insights at reduced costs?
  10. 10. Next generation architecture Starts from the current data management environment … Data sources Actionable insight Enterprise Enterprise warehouse warehouse Transaction and application data Information ingestion and operational information Data mart Data mart Predictive analytics and modeling Analytic appliances Analytic appliances Reporting, analysis, content analytics Information governance SYSTEMS—SECURITY—STORAGE
  11. 11. Next generation architecture … adds new technologies and capabilities … Data sources Real-time analytics Actionable insight Machine and sensor data Cognitive Image and video Enterprise content Transaction and application data Information ingestion and operational information + + Social data Third-party data Exploration, landing and archive Enterprise Enterprise warehouse warehouse Decision management Data mart Data mart Predictive analytics and modeling Analytic appliances Analytic appliances Reporting, analysis, content analytics Information governance SYSTEMS—SECURITY—STORAGE Discovery and exploration
  12. 12. Next generation architecture … to enable new applications Data sources Real-time analytics Actionable insight Machine and sensor data Cognitive Image and video Enterprise content Transaction and application data Information ingestion and operational information + + Social data Third-party data Exploration, landing and archive Enterprise Enterprise warehouse warehouse Decision management Enhanced applications Customer experience New business models Financial performance Data mart Data mart Predictive analytics and modeling Analytic appliances Analytic appliances Reporting, analysis, content analytics Information governance SYSTEMS—SECURITY—STORAGE Risk Operations and fraud IT economics Discovery and exploration
  13. 13. Sample use cases that leverage the new data management environment capabilities
  14. 14. Dublin City Centre; Robust and efficient citywide traffic awareness system, enhance rapid action on incidents Need •  A budget effective solution to improve traffic awareness system. •  To bring accuracy in event detection, inferring traffic condition (road speed) and prediction of bus arrival. •  Challenge is to rightly analyze GPS data, which is typically high data throughput and difficult to capture Benefits •  Monitor 600 buses across 150 routes daily •  Analyzes 50 bus location updates per second , using InfoSphere Streams •  Collects, processes, and visualizes location data of all public transportation vehicles
  15. 15. Architecture for traffic awareness system Real-time analytics to enhance customer experience Data sources Real-time analytics Actionable insight Machine and Machine and sensor data sensor data Cognitive Image and video Enterprise content Transaction and application data Information ingestion and operational information + + Social data Third-party data Exploration, landing and archive Enterprise Enterprise warehouse warehouse Decision management Enhanced applications Customer experience New business models Financial performance Data mart Data mart Predictive analytics and modeling Analytic appliances Analytic appliances Reporting, analysis, content analytics Information governance SYSTEMS—SECURITY—STORAGE Risk Operations and fraud IT economics Discovery and exploration
  16. 16. Constant Contact Transforming Email Marketing Campaign Effectiveness with IBM Big Data Need •  Analyze 35 billion annual emails to guide customers on best dates & times to send emails for maximum response Benefits •  40 times improvement in analysis performance •  15-25% performance increase in customer email campaigns •  Analysis time reduced from hours to seconds
  17. 17. Architecture for email marketing Analyze to maximize response rates Data sources Real-time analytics Actionable insight Machine and sensor data Cognitive Image and video Enterprise content Transaction and application data Information ingestion and operational information + + Social data Third-party data Exploration, landing and archive Enterprise Enterprise warehouse warehouse Decision management Enhanced applications Customer experience New business models Financial performance Data mart Data mart Predictive analytics and modeling Analytic appliances Analytic appliances Reporting, analysis, Reporting, analysis, content analytics content analytics Information governance SYSTEMS—SECURITY—STORAGE Risk Operations and fraud IT economics IT economics Discovery and exploration
  18. 18. Battelle, helping reduce energy costs and enhancing power grid reliability and performance Need •  Assess the viability of one smart grid technique called transactive control Benefits •  Engages consumers and responsive assets throughout the power system to help optimize the system and better integrate renewable resources •  Provides the capability to analyze and gain insight from up to 10 PB of data in minutes •  Increases grid efficiency and reliability through system self-monitoring and feedback •  Enables a town to avoid a potential power outage
  19. 19. Architecture for smart grid Analytics to reduce costs and optimize grid Data sources Real-time analytics Actionable insight Machine and Machine and sensor data sensor data Cognitive Image and video Enterprise content Transaction and application data Information ingestion and operational information + + Social data Third-party data Exploration, landing and archive Enterprise Enterprise warehouse warehouse Decision management Enhanced applications Customer experience New business models Financial performance Data mart Data mart Predictive analytics and modeling Analytic appliances Analytic appliances Reporting, analysis, content analytics Information governance SYSTEMS—SECURITY—STORAGE Risk Operations and fraud IT economics Discovery and exploration
  20. 20. Trust is the most important aspect of a Big Data solution
  21. 21. Trust It Be proactive about privacy, security and governance Trust the facts Ensure privacy and security Make risk aware decisions Create foundation of trusted data Understand usage and monitor compliance Model exposure and understand variability
  22. 22. Big Data Privacy and Security Protect a Wider Variety of Sources •  Protec&on  is  a  pre-­‐requisite  for   the  fundamental  assump&on  of   big  data  –  sharing  data  for  new   insight   •  Automa&on  enables  protec&on   without  inhibi&ng  speed     •  Data  ac&vity  monitoring  of  NoSQL,   Hadoop,  and  Rela&onal  Systems   •  Masking  of  sensi&ve  data  used  in   Hadoop       •  Ensures  sensi&ve  data     is  protected,  encrypted  and  secure   RDBMS InfoSphere Guardium InfoSphere Optim Hadoop NoSQL Data Warehouses Application Data and Files
  23. 23. Data volumes continue to grow fast. New technologies will be needed in the future
  24. 24. Big Data brings Technical Challenges IBM invests in future solutions Huge Volumes of Multimedia Data: New Storage Requirements The Challenge of Moore’s Law: New Storage Methods Needed IBM’s Approach: Start at the Atomic Level how many atoms are needed to store 1-bit of data
  25. 25. What are your activities to leverage Big Data Analytics?
  26. 26. Your big data journey IBM can help wherever you are Educate   25% Focused  on  knowledge   gathering  and  market   observa&ons   Explore   47% Developing  strategy   and  roadmap  based  on   business  needs  and   challenges   Case  studies,                     Whitepapers  and   Value  Repors   ibmbigdatahub.com   IBM  Briefings,  Solu&on   Centers   Learning,  explora&on   downloads  &  test   BigDatauniversity.com,   YouTube  Big  Data  Channel   Engage   22% Pilo&ng  big  data   ini&a&ves  to  validate   value  and  requirements   Execute   6% Deployed  two  or  more   big  data  ini&a&ves  and   con&nuing  to  apply   advanced  analy&cs   Validate  and  realize  business  value                            IBM  Roadmap                          Workshop   • Priori&sed  business  use  cases   • Recommend  plaRorm              Solu&on  Design  &                Proof  of  Concept                    Enterprise-­‐wide                              big  data  ini&a&ves   • Stampede  –  exper&se   and  skills  to  get  value   straight  away   • Enterprise  data  plaRorm   • Validate  business  value   • Demonstrate  capabili&es   Learn  the  technology  &  gain  exper&se  
  27. 27. THINK

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