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Building new business models through big data dec 06 2012
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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
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What is driving Big Data?
1.Rising volumes of data
2.Falling cost of data
management tools
3.Rising number of Data
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Scientists
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#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
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5. Social Media Data + Web Data = Measure traffic to your website from social
media campaigns
Source: “Why Web Analytics is Not Enough.” Quantivo. (Paraphrased)
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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)
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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
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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.
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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
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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
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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
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Hadoop + R
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Source: http://blog.revolutionanalytics.com/2011/09/slides-and-replay-from-r-and-hadoop-
webinar.html
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Stream Processing + Column DBMS
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Source: Guavus Reflex Platform. http://www.guavus.com/#/solutions/guavus-platform/
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EDW + HDFS + NoSQL + CEP (Simplified)
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Source: http://www.oracle.com/technetwork/topics/entarch/articles/oea-big-data-guide-1522052.pdf
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EDW + Hadoop + Reporting
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Source: http://www.oracle.com/technetwork/topics/entarch/articles/oea-big-data-guide-1522052.pdf
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EDW + Data Science Sandboxes + CEP
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Source: Big Data Analytics: Profiling the Use of Analytical Platforms in User Organizations (SAS)
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Appendix: 451 Group Big Data Landscape
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Source: http://blogs.the451group.com/information_management/files/2012/11/DB_landscape.jpg