This document discusses how predictive analytics using big data can lead to successful recommendations and revenue maximization. It describes trends in data growth, the value of data analytics exceeding hardware costs, and how a unified analytics cloud platform can simplify infrastructure and optimize resources. Sample predictive analytics applications are outlined for industries like ecommerce, mobile, advertising, gaming, and IT, with the goal of revenue maximization and user engagement through recommendation engines and targeted placements. The cloudification of predictive analytics as an analytics-as-a-service approach is presented as the logical conclusion to fully leverage big data.

1. Predictive Analytics Leads to Successful
Recommendations and Direct Revenue Maximization
Christos Tryfonas, Ph.D.
CTO, Cloud & Big Data Analytics
INSTANT INTELLIGENCE
Web:
www.cetas.net
Twi)er:
@CetasAnaly/cs
Blog:
www.cetas.net/blog
YouTube:
www.youtube.com/CetasAnaly/cs
© 2009 VMware Inc. All rights reserved

2. INSTANT INTELLIGENCE
Transform IT
+ Transform Business
+ Transform Yourself
Leverage Cetas to Transform
Real-Time and Predictive Analytics
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3. Infrastructure, Apps and now Data… INSTANT INTELLIGENCE
Build Run
Private
Public
Analyze Manage
Simplify Infrastructure Simplify App Platform Simplify Data
With Cloud Through PaaS with
(IaaS) Analytics
as-a-Service
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4. Trend: New Data Growing at 60% Y/Y INSTANT INTELLIGENCE
Exabytes of information stored 20 Zetta by 2015
1 Yotta by 2030
Yes, you are part
of the yotta
audio
generation…
digital
tv
digital
photos
camera
phones,
rfid
medical
imaging,
sensors
satellite
images,
logs,
scanners,
twi)er
cad/cam,
appliances,
machine
data,
digital
movies
Source: The Information Explosion, 2009
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5. Data Growth in the Enterprise INSTANT INTELLIGENCE
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6. Trend: Value from Data Exceeds Hardware Cost INSTANT INTELLIGENCE
§ Value from the intelligence of data analytics now outstrips the cost
of hardware
• Hadoop enables the use of 10x lower cost hardware
• Hardware cost halving every 18 months
Value
Big Iron:
$40k/CPU
Commodity
Cluster:
$1k/CPU
Cost
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7. The Big Data Problem INSTANT INTELLIGENCE
Velocity Volume Variety Value
$
10’s of Billions From Terabytes to Multi-Structured Business
of Daily Records Petabytes Insights
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8. Trend: Big Data Analytics – Driven by Real-World Benefit INSTANT INTELLIGENCE
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9. Big Data Use Cases INSTANT INTELLIGENCE
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10. Big Data Analytics ROI INSTANT INTELLIGENCE
Source: Nucleus Research
http://www.enterpriseappstoday.com/business-intelligence/the-more-pervasive-business-analytics-roi-big-data.html
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11. Big Data Technology Stack INSTANT INTELLIGENCE
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12. A Holistic View of a Big Data System INSTANT INTELLIGENCE
Real-Time
Streams
Real-Time
Processing
Analytics
Ingestion
& Big Query,
ETL Real Time
Search, Batch
Database
(HBase, Index Processing
Cassandra) (Google, Spunk,
Cetas)
Unstructured Data (HDFS)
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13. The Unified Analytics Cloud Platform INSTANT INTELLIGENCE
Mahout R Cetas/VMware
Analytics Tools
Splunk SAS or IBM SPSS
Map Reduce Developer Spring
PaaS
Python Frameworks Cloud Foundry
Cassandra HBase
HDFS Database/DataStores
Greenplum Voldemort
Hadoop
Data Platform Data PaaS
VMware /Cetas
vSphere Cloud Infrastructure
Private
Public
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14. A Unified Analytics Cloud Significantly Simplifies Infrastructure INSTANT INTELLIGENCE
§ Simplify
• Single Hardware Infrastructure
• Faster/Easier provisioning
SQL or NoSQL Cluster
Big SQL NoSQL Hadoop Analytics
Engine
Analytics L Cluster
Unified Analytics Infrastructure
Private
Public
Hadoop Cluster
§ Optimize
• Shared Resources = higher utilization
Decision Support Cluster
• Elastic resources = faster on-demand
access
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15. Predictive Analytics Objectives INSTANT INTELLIGENCE
§ Optimization of one or more target functions
• Example: Increase Customer Engagement
§ Vertical Specific
• Increase Customer Satisfaction
• Maximize Customer/User Engagement
• Minimize Churn Rate
• Maximize Revenue
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16. Sample Applications of Predictive Analytics INSTANT INTELLIGENCE
§ eCommerce
• Customer interaction
• Revenue Maximization Statistical /
• Inventory management Regression
§ Mobile Models
• Capacity Planning
§ Ad/Publishing
• Ad serving Machine
• Customer Retention Management Learning
(NN, Bayesian,
§ Gaming etc.)
• Ad serving
• Virtual Good Placement
• In-App Purchasing
Social Graph
§ IT
Algorithms
• Capacity Planning
• Alerting and Diagnosis
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17. Taking Predictive Analytics to logical conclusion INSTANT INTELLIGENCE
§ Currently predictive analytics is in Silos
• Data Scientists w/ SAS, SPSS, Custom models
• Localized/Sampled Datasets
• Building Models using Open Source Modeling Tools: Hadoop, Mahout, R
§ Actionable Analytics
• Recommendation Engines (Examples: Amazon, Netflix)
• Product Placement Engines
• Targeted Ad Placement Engines
§ Cloudification of Predictive Analytics
• Analytics-as-a-Service approach
• Big Data support
§ Overall Objectives
• Revenue Maximization
• Social Penetration and User Engagement
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18. Data is Being Stretched INSTANT INTELLIGENCE
Intelligent Data
§ Analytics Apps and
Algorithms
§ Discovery, Visibility,
Pattern Extraction
Big Data Fast Data
§ Streaming, real-time and
§ Petabytes vs.
Unpredictable
Gigabytes
§ Mobile app proliferation
§ Democratize
Analytics
§ Non-structured data
Flexible Data § Developer productivity
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19. Summary INSTANT INTELLIGENCE
§ Cloudification of Analytics is happening
§ Predictive Analytics follow the Big Data
and Social Web App trends
§ Direct Impact in Monetization from
Predictive Modeling and Analytics
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