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Creating New Business Models
with Big Data & Analytics

Aki Balogh
www.linkedin.com/in/akibalogh
More resources: www.GenerationAnalytics.com   |2




Agenda
1.   What is Driving Big Data?
2.   What is Big Data?
3.   What is Analytics?
4.   What can you do with Big Data & Analytics?
5.   Example Architectures




                                                                                            © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   |3




Where is Big Data today?




                                                                              © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   |4




What is driving Big Data?

1.Rising volumes of data
2.Falling cost of data
  management tools
3.Rising number of Data




                                                                               © Diamond Management & Technology Consultants, Inc.
  Scientists
More resources: www.GenerationAnalytics.com   |5




#1: Data volumes are growing




                                                                                  © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   |6




#2: Data management tools like Hadoop are driving down cost




                                                                                   © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   |7




#3: Data Science as a discipline is growing




                                                                                     © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   |8




What is Big Data?




                                                                       © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   |9




Big Data is Turning data into insights to drive decision-making




                                                                                     © Diamond Management & Technology Consultants, Inc.
Source: Allen (1999)
More resources: www.GenerationAnalytics.com   | 10




A Simple Framework: 3 Vs of Big Data

• Volume
• Variety
• Velocity




                                                                                     © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   | 11




#1: Volume




                                                                                                               © Diamond Management & Technology Consultants, Inc.
Source: Christopher Bingham, Crimson Hexagon. “Better Algorithms from Bigger Data.”
More resources: www.GenerationAnalytics.com   | 12




#2: Variety
 Data can dramatically change the way marketers gain customer intelligence and
 measure campaign effectiveness.


 1. CRM Data + Web Data = Improve lead quality scoring
 2. Call-Center Data + Web data = Better analyze calls you should avoid
 3. Past Purchase Data + Web Data = Segment customers based on past buying
    behavior and target them on your website
 4. Campaign Data + Web Data = Understand multi-touch attribution and
    optimize your campaign mix




                                                                                                               © Diamond Management & Technology Consultants, Inc.
 5. Social Media Data + Web Data = Measure traffic to your website from social
    media campaigns




Source: “Why Web Analytics is Not Enough.” Quantivo. (Paraphrased)
More resources: www.GenerationAnalytics.com   | 13




#3: Velocity




                                                                                                              © Diamond Management & Technology Consultants, Inc.
Source: Guavus Reflex Platform. http://www.guavus.com/#/solutions/guavus-platform/
More resources: www.GenerationAnalytics.com   | 14




What is Analytics?




                                                                          © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   | 15




Five Common Analytics Objectives

Classify
• Clustering
• Unsupervised and supervised machine learning
• Fraud analytics
Trend
• Time-series analysis
Optimize
• Find the optimal outcome of an objective function (min/max)




                                                                                           © Diamond Management & Technology Consultants, Inc.
Predict
• Predict the outcome of a single event
Simulate
• Explore the consequences of different choices to help drive decision-
  making
• Open-ended: Scenario planning, DSS
More resources: www.GenerationAnalytics.com   | 16




What can you do with Big Data & Analytics?




                                                                                     © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   | 17




What does Big Data Analytics require?
Data: data availability + storage + integration + data management tools
+
Analytics: analytic formulas + statistical integrity + analytic applications
+
Interpretation: business problem + domain expertise + visualization +
decision-making


This typically requires a team of people with different skillsets.




                                                                                                 © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   | 18




What can you do with Big Data & Analytics?
1.   New revenue models
Ex: Rapleaf scraping the web, collecting contact information and selling full datasets


2.   New user experiences
Ex: Gmail recommendations for people to CC: on your email


3.   Cost optimization (i.e. deliver same product or service at less cost)
Ex: Give your financial advisors tools to help automate your investment decisions




                                                                                                      © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   | 19




Example Architectures




                                                                             © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   | 20




Combining Big Data and the Enterprise Data Warehouse




                                                                                    © Diamond Management & Technology Consultants, Inc.
More resources: www.GenerationAnalytics.com   | 21




Database Types and Examples

  Database Type      Example Database            Usage
  SQL Row DBMS       MySQL, PostgreSQL           Real-time
                                                 transactions on SQL
                                                 data
  SQL Column DBMS    Vertica, InfiniDB           Real-time analytics
                                                 on SQL data
  SQL In-Memory      MemSQL                      Real-time
                                                 transactions




                                                                                     © Diamond Management & Technology Consultants, Inc.
  Document-store     MongoDB                     JSON data
  Graph Database     Neo4j                       Social network
                                                 connections
  Hadoop             Hive, Hadapt,               Unstructured and
                     Accumulo                    semi-structured data
  Complex Event      Storm                       Real-time events
  Processing
  Math Package       R                           Analytic libraries
More resources: www.GenerationAnalytics.com   | 22




Redis




                                                                                                              © Diamond Management & Technology Consultants, Inc.
Source: http://redis.io/presentation/Redis_Cluster.pdf
More resources: www.GenerationAnalytics.com   | 23




MongoDB




                                                                                                                 © Diamond Management & Technology Consultants, Inc.
Source: http://www.slideshare.net/PhilippeJulio/big-data-architecture
More resources: www.GenerationAnalytics.com   | 24




HDFS




                                                                                                            © Diamond Management & Technology Consultants, Inc.
Source: http://gigaom.com/cloud/what-it-really-means-when-someone-says-hadoop/
More resources: www.GenerationAnalytics.com   | 25




Hadoop + R




                                                                                                               © Diamond Management & Technology Consultants, Inc.
Source: http://blog.revolutionanalytics.com/2011/09/slides-and-replay-from-r-and-hadoop-
webinar.html
More resources: www.GenerationAnalytics.com   | 26




Stream Processing + Column DBMS




                                                                                                              © Diamond Management & Technology Consultants, Inc.
Source: Guavus Reflex Platform. http://www.guavus.com/#/solutions/guavus-platform/
More resources: www.GenerationAnalytics.com   | 27




EDW + HDFS + NoSQL + CEP (Simplified)




                                                                                                              © Diamond Management & Technology Consultants, Inc.
Source: http://www.oracle.com/technetwork/topics/entarch/articles/oea-big-data-guide-1522052.pdf
More resources: www.GenerationAnalytics.com   | 28




EDW + Hadoop + Reporting




                                                                                                              © Diamond Management & Technology Consultants, Inc.
Source: http://www.oracle.com/technetwork/topics/entarch/articles/oea-big-data-guide-1522052.pdf
More resources: www.GenerationAnalytics.com   | 29




EDW + Data Science Sandboxes + CEP




                                                                                                                   © Diamond Management & Technology Consultants, Inc.
Source: Big Data Analytics: Profiling the Use of Analytical Platforms in User Organizations (SAS)
More resources: www.GenerationAnalytics.com   | 30




Appendix: 451 Group Big Data Landscape




                                                                                                            © Diamond Management & Technology Consultants, Inc.
Source: http://blogs.the451group.com/information_management/files/2012/11/DB_landscape.jpg

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Building new business models through big data dec 06 2012

  • 1. Creating New Business Models with Big Data & Analytics Aki Balogh www.linkedin.com/in/akibalogh
  • 2. More resources: www.GenerationAnalytics.com |2 Agenda 1. What is Driving Big Data? 2. What is Big Data? 3. What is Analytics? 4. What can you do with Big Data & Analytics? 5. Example Architectures © Diamond Management & Technology Consultants, Inc.
  • 3. More resources: www.GenerationAnalytics.com |3 Where is Big Data today? © Diamond Management & Technology Consultants, Inc.
  • 4. More resources: www.GenerationAnalytics.com |4 What is driving Big Data? 1.Rising volumes of data 2.Falling cost of data management tools 3.Rising number of Data © Diamond Management & Technology Consultants, Inc. Scientists
  • 5. More resources: www.GenerationAnalytics.com |5 #1: Data volumes are growing © Diamond Management & Technology Consultants, Inc.
  • 6. More resources: www.GenerationAnalytics.com |6 #2: Data management tools like Hadoop are driving down cost © Diamond Management & Technology Consultants, Inc.
  • 7. More resources: www.GenerationAnalytics.com |7 #3: Data Science as a discipline is growing © Diamond Management & Technology Consultants, Inc.
  • 8. More resources: www.GenerationAnalytics.com |8 What is Big Data? © Diamond Management & Technology Consultants, Inc.
  • 9. More resources: www.GenerationAnalytics.com |9 Big Data is Turning data into insights to drive decision-making © Diamond Management & Technology Consultants, Inc. Source: Allen (1999)
  • 10. More resources: www.GenerationAnalytics.com | 10 A Simple Framework: 3 Vs of Big Data • Volume • Variety • Velocity © Diamond Management & Technology Consultants, Inc.
  • 11. More resources: www.GenerationAnalytics.com | 11 #1: Volume © Diamond Management & Technology Consultants, Inc. Source: Christopher Bingham, Crimson Hexagon. “Better Algorithms from Bigger Data.”
  • 12. More resources: www.GenerationAnalytics.com | 12 #2: Variety Data can dramatically change the way marketers gain customer intelligence and measure campaign effectiveness. 1. CRM Data + Web Data = Improve lead quality scoring 2. Call-Center Data + Web data = Better analyze calls you should avoid 3. Past Purchase Data + Web Data = Segment customers based on past buying behavior and target them on your website 4. Campaign Data + Web Data = Understand multi-touch attribution and optimize your campaign mix © Diamond Management & Technology Consultants, Inc. 5. Social Media Data + Web Data = Measure traffic to your website from social media campaigns Source: “Why Web Analytics is Not Enough.” Quantivo. (Paraphrased)
  • 13. More resources: www.GenerationAnalytics.com | 13 #3: Velocity © Diamond Management & Technology Consultants, Inc. Source: Guavus Reflex Platform. http://www.guavus.com/#/solutions/guavus-platform/
  • 14. More resources: www.GenerationAnalytics.com | 14 What is Analytics? © Diamond Management & Technology Consultants, Inc.
  • 15. More resources: www.GenerationAnalytics.com | 15 Five Common Analytics Objectives Classify • Clustering • Unsupervised and supervised machine learning • Fraud analytics Trend • Time-series analysis Optimize • Find the optimal outcome of an objective function (min/max) © Diamond Management & Technology Consultants, Inc. Predict • Predict the outcome of a single event Simulate • Explore the consequences of different choices to help drive decision- making • Open-ended: Scenario planning, DSS
  • 16. More resources: www.GenerationAnalytics.com | 16 What can you do with Big Data & Analytics? © Diamond Management & Technology Consultants, Inc.
  • 17. More resources: www.GenerationAnalytics.com | 17 What does Big Data Analytics require? Data: data availability + storage + integration + data management tools + Analytics: analytic formulas + statistical integrity + analytic applications + Interpretation: business problem + domain expertise + visualization + decision-making This typically requires a team of people with different skillsets. © Diamond Management & Technology Consultants, Inc.
  • 18. More resources: www.GenerationAnalytics.com | 18 What can you do with Big Data & Analytics? 1. New revenue models Ex: Rapleaf scraping the web, collecting contact information and selling full datasets 2. New user experiences Ex: Gmail recommendations for people to CC: on your email 3. Cost optimization (i.e. deliver same product or service at less cost) Ex: Give your financial advisors tools to help automate your investment decisions © Diamond Management & Technology Consultants, Inc.
  • 19. More resources: www.GenerationAnalytics.com | 19 Example Architectures © Diamond Management & Technology Consultants, Inc.
  • 20. More resources: www.GenerationAnalytics.com | 20 Combining Big Data and the Enterprise Data Warehouse © Diamond Management & Technology Consultants, Inc.
  • 21. More resources: www.GenerationAnalytics.com | 21 Database Types and Examples Database Type Example Database Usage SQL Row DBMS MySQL, PostgreSQL Real-time transactions on SQL data SQL Column DBMS Vertica, InfiniDB Real-time analytics on SQL data SQL In-Memory MemSQL Real-time transactions © Diamond Management & Technology Consultants, Inc. Document-store MongoDB JSON data Graph Database Neo4j Social network connections Hadoop Hive, Hadapt, Unstructured and Accumulo semi-structured data Complex Event Storm Real-time events Processing Math Package R Analytic libraries
  • 22. More resources: www.GenerationAnalytics.com | 22 Redis © Diamond Management & Technology Consultants, Inc. Source: http://redis.io/presentation/Redis_Cluster.pdf
  • 23. More resources: www.GenerationAnalytics.com | 23 MongoDB © Diamond Management & Technology Consultants, Inc. Source: http://www.slideshare.net/PhilippeJulio/big-data-architecture
  • 24. More resources: www.GenerationAnalytics.com | 24 HDFS © Diamond Management & Technology Consultants, Inc. Source: http://gigaom.com/cloud/what-it-really-means-when-someone-says-hadoop/
  • 25. More resources: www.GenerationAnalytics.com | 25 Hadoop + R © Diamond Management & Technology Consultants, Inc. Source: http://blog.revolutionanalytics.com/2011/09/slides-and-replay-from-r-and-hadoop- webinar.html
  • 26. More resources: www.GenerationAnalytics.com | 26 Stream Processing + Column DBMS © Diamond Management & Technology Consultants, Inc. Source: Guavus Reflex Platform. http://www.guavus.com/#/solutions/guavus-platform/
  • 27. More resources: www.GenerationAnalytics.com | 27 EDW + HDFS + NoSQL + CEP (Simplified) © Diamond Management & Technology Consultants, Inc. Source: http://www.oracle.com/technetwork/topics/entarch/articles/oea-big-data-guide-1522052.pdf
  • 28. More resources: www.GenerationAnalytics.com | 28 EDW + Hadoop + Reporting © Diamond Management & Technology Consultants, Inc. Source: http://www.oracle.com/technetwork/topics/entarch/articles/oea-big-data-guide-1522052.pdf
  • 29. More resources: www.GenerationAnalytics.com | 29 EDW + Data Science Sandboxes + CEP © Diamond Management & Technology Consultants, Inc. Source: Big Data Analytics: Profiling the Use of Analytical Platforms in User Organizations (SAS)
  • 30. More resources: www.GenerationAnalytics.com | 30 Appendix: 451 Group Big Data Landscape © Diamond Management & Technology Consultants, Inc. Source: http://blogs.the451group.com/information_management/files/2012/11/DB_landscape.jpg