The document discusses creating new business models using big data and analytics. It provides an agenda that covers what is driving big data, definitions of big data and analytics, examples of what can be done with big data and analytics, and example architectures. Specifically, it describes how rising data volumes, falling costs of tools, and growing data science are driving big data. It defines big data using the 3Vs of volume, variety and velocity. It outlines common analytics objectives and provides examples of new revenue models, user experiences and cost optimization using big data and analytics. Finally, it shares several example architecture diagrams combining tools like Hadoop, MongoDB, Redis and event processing with data warehouses.

1. Creating New Business Models
with Big Data & Analytics
Aki Balogh
www.linkedin.com/in/akibalogh

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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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Where is Big Data today?
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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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#1: Data volumes are growing
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#2: Data management tools like Hadoop are driving down cost
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#3: Data Science as a discipline is growing
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What is Big Data?
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Big Data is Turning data into insights to drive decision-making
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Source: Allen (1999)

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A Simple Framework: 3 Vs of Big Data
• Volume
• Variety
• Velocity
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#1: Volume
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Source: Christopher Bingham, Crimson Hexagon. “Better Algorithms from Bigger Data.”

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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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#3: Velocity
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Source: Guavus Reflex Platform. http://www.guavus.com/#/solutions/guavus-platform/

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What is Analytics?
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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 can you do with Big Data & Analytics?
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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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Example Architectures
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Combining Big Data and the Enterprise Data Warehouse
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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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Redis
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Source: http://redis.io/presentation/Redis_Cluster.pdf

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MongoDB
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Source: http://www.slideshare.net/PhilippeJulio/big-data-architecture

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HDFS
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Source: http://gigaom.com/cloud/what-it-really-means-when-someone-says-hadoop/

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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