The document discusses how big data technologies can help organizations make better decisions by analyzing large, diverse datasets in real-time. It provides examples of use cases like fraud detection, predictive maintenance, and demand forecasting that have been revolutionized by big data solutions. The document also explains how big data has evolved from traditional data warehousing and how organizations can build on existing technology investments to implement big data strategies. Real-time analytics is said to get the right data in front of decision makers faster by consuming, analyzing, and visualizing historical and live data sources.

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Turning Big Data
into Big Decisions
Michael Ho
Vice President
March 26, 2014

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Define Your Goals

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Credit & Market Risk in Banks
Fraud Detection (Credit Card) & Financial Crimes (AML) in Banks
Event-based Marketing in Financial Services and Telecomms
Claims and Tax Fraud in Public Sector
Predictive Maintenance
in Aerospace
Demand Forecasting in
Manufacturing
Traditional Data
Warehousing
Social Media
Sentiment Analysis
Disease Analysis
on Electronic
Health Records
Video Surveillance
& Analysis
Household Scoring
Market Sizing
A/B Testing
Data
Velocity
Data Variety
Real-time
Batch
Structured Semi-structured Unstructured
Use cases revolutionized by big data solutions

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Credit & Market Risk in Banks
Fraud Detection (Credit Card) & Financial Crimes (AML) in Banks
Event-based Marketing in Financial Services and Telecomms
Claims and Tax Fraud in Public Sector
Predictive Maintenance
in Aerospace
Demand Forecasting in
Manufacturing
Traditional Data
Warehousing
Social Media
Sentiment Analysis
Disease Analysis
on Electronic
Health Records
Video Surveillance
& Analysis
Household Scoring
Market Sizing
A/B Testing
Data
Velocity
Data Variety
Real-time
Batch
Structured Semi-structured Unstructured
Use cases revolutionized by big data solutions

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Understand Your Data Landscape

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Big Data

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Big Data
When the amount of data is too large to utilize within its
window of relevance.

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Evolve with Technology

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Big data technologies are the natural evolution of
data collection and processing.
1970 1980 1990 2000 2010
1970s - First RDBMS and SQL developed
1991 – WWW becomes publicly available
1995 – GPS operationalized
1998 – Google founded
2003 – Wikipedia started
2005 – Hadoop created
2009 – Terracotta creates Enterprise Ehcache
2003 – Ehcache created

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DecisionTime
DataSize
1970 1980 1990 2000 2010
Big data technologies are the natural evolution of
data collection and processing.
ENTERPRISE
EHCACHE

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Build on Existing Investments

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A good big data solution builds upon the
solid technology foundation you have
already laid.
Evolving with your enterprise

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CostpergigabyteofRAM
1965 2010 2025
Cost Effectiveness

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Make Better, Faster Decisions

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Volume
VarietyVelocity
Historical
Data
Live Data
ENTERPRISE EHCACHE
Consume Analyze Visualize
A real-time data revolution
Big Data

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VisualizeAnalyzeConsume
Visual tools facilitate real time analysis of disparate data
User-
Driven
User-
Driven
Step 1 Step 2 Step 3

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Real-time Analytics allows you to
get the right data
in front of the right people
at the right time
in ways you couldn’t before.

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Real-time Analytics Use Case #1:
Situational Awareness

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Real-time Analytics Use Case #2:
Cyber Defense

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Real-time Analytics Use Case #3:
Financial Planning

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Real-time Analytics Use Case #4:
Data Transparency