1. Big Data Needs Big
Analytics
Deepak Ramanathan
Information Management and Analytics
Asia Pacific (North)
Copyright © 2012, SAS Institute Inc. All rights reserved.

2. OUR
PERSPECTIVE
Big Data is RELATIVE not ABSOLUTE
Big Data (Noun)
When volume, velocity and variety of data exceeds an
organization’s storage or compute capacity for accurate
and timely decision-making
Copyright © 2012, SAS Institute Inc. All rights reserved.

3. THE FUTURE OF ANALYTICS IS HERE
Copyright © 2012, SAS Institute Inc. All rights reserved.

4. Copyright © 2012, SAS Institute Inc. All rights reserved.

5. THE ANALYTICS LIFECYCLE
IDENTIFY /
FORMULATE
BUSINESS EVALUATE /
PROBLEM BUSINESS
MANAGER MONITOR DATA ANALYST
RESULTS PREPARATION
Domain Expert Data Exploration
Makes Decisions Data Visualization
Evaluates Processes and ROI Report Creation
DEPLOY
MODEL DATA
EXPLORATION
IT SYSTEMS / DATA MINER /
MANAGEMENT VALIDATE STATISTICIAN
MODEL TRANSFORM
Model Validation & SELECT Exploratory Analysis
Model Deployment BUILD
Descriptive Segmentation
Model Monitoring MODEL Predictive Modeling
Data Preparation
Copyright © 2012, SAS Institute Inc. All rights reserved.

6. CURRENT
BIG DATA MEETS
TRENDS IN BIG ANALYTICS
ANALYTICS
Copyright © 2012, SAS Institute Inc. All rights reserved.

7. • A flexible architecture that supports
many data types and usage patterns
Technology • Upstream use of analytics to optimize
data relevance
Checklist for
• Real-time visualization and advanced
Big Data analytics to accelerate understanding
Analytics and action
• Collaborative approaches to align
Business and IT executives
Copyright © 2012, SAS Institute Inc. All rights reserved.

8. • Leverage in-memory architecture via
a dedicated software and hardware
appliance
Big Data • Drive high-performance capabilities
Meets across the analytical lifecycle
Big Analytics • Achieve insights at breakthrough
speed before questions become
obsolete
• Offer a consistent interface for current
SAS analytic users
Copyright © 2012, SAS Institute Inc. All rights reserved.

9. SAS HIGH-
PEFORMANCE
ANALYTICS
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10. HIGH-
PERFORMANCE SAS® GRID COMPUTING
ANALYTICS
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11. HIGH-
PERFORMANCE SAS® IN-DATABASE
ANALYTICS
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12. IN-MEMORY
ACCELERATES UNDERSTANDING AND ACTION
ARCHITECTURE
Hadoop Teradata Aster
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13. SAS® VISUAL
BUSINESS VISUALIZATION: SAS® VISUAL ANALYTICS
ANALYTICS
SAS LASR Analytic Server
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14. SAS HIGH-PERFORMANCE ANALYTICS – HOW IT WORKS
DB
Appliance Node
SAS High-Performance Analytics Appliance
• Processing request parsed into multiple,
proc hplogistic data=GPLib.MyTable; parallel requests distributed to the nodes of
class A B C D ;
model y = a b c b*d x1-x100; the environment
output out=GPlib.logout pred=p; • Each node operates ‘independently’
run;
• Results consolidated and returned to client
Copyright © 2012, SAS Institute Inc. All rights reserved.

15. OUR
OUR HIGH-PERFORMANCE ANALYTICS
PERSPECTIVE DRIVES HIGH IMPACT RESULTS
PERSPECTIVE
Retention Campaigns 15% improvement Increase coupon redemption rate from
(SAS® Grid Manager) 10% to 25%
(SAS® Scoring Accelerator (In-DB))
Regression analysis from
167 hours (1 week) to 84 seconds! Recalculate entire risk portfolio from 18
(SAS® High-Performance Analytics) hours to 12 minutes
(SAS® High-Performance Risk)
270 million price points analyzed in 2 hrs. (from 30 hrs.)
(SAS® High-Performance Markdown Optimization)
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16. 16
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17. Deepak.Ramanathan@sas.com
Copyright © 2012, SAS Institute Inc. All rights reserved.