Big Data Forum at Salt River Fields (the spring training field for the Arizona Diamondbacks). Krishnan Parasuraman discusses how companies are using big data and analytics to transform their business.

1. IBM Big Data Forum
Salt River Fields, Phoenix, Arizona
16 May 2013
Krishnan Parasuraman

2. Talking Points
What is Big Data?1
What is the relationship between Big Data
and Analytics?
2
What does a Big Data Platform look like?3
What are the different entry points into Big Data?4
What is IBM’s strategy in Big Data?5

3. The number of organizations who see analytics as a
competitive advantage is growing.
2010 2011 2012
63%

4. IBM IBV/MIT Sloan Management Review Study 2011
Copyright Massachusetts Institute of Technology 2011
Studies show that organizations competing on
analytics substantially outperform their peers
1.6xRevenue
Growth 2.0x EBITDA
Growth2.5x Stock Price
Appreciation
4

5. Analytics (by itself) = Blah.
Analytics + Big Data = Kickass Competitive Advantage

6. Big Data Analytics Use Cases
Call Centers
Voice-to-text mining for understanding
customer sentiment
Healthcare
Genomics Analytics
Medical Record Analytics
E Commerce / Retail
Clickstream analytics
Analyze online behavior and buying patterns
Oil and Gas / Energy
Geospatial Analysis
Windmill placements

7. The Analytics Continuum…in Healthcare
Transaction
reporting
• Evidence-based medicine
• Personalized healthcare
• Dynamic fraud detection
• Patient, member behavior
• Enterprise-wide data
• Enterprise analytics
• Clinical outcomes reporting
• Unified data sources
• Clinical data repositories
• Departmental data marts
• Dashboards
• Spreadsheets
• Separate data sources
• Manual collation of data
• Basic reporting
Data integration
Data warehouse
Clinical
analytics
Advanced
analytics
• What are the key health indicators across my patient population?
• What are our quality scores ?
• What is the total cost of care?
• What is our productivity and resource utilization?

8. The Analytics Continuum…in Healthcare
Transaction
reporting
• Evidence-based medicine
• Personalized healthcare
• Dynamic fraud detection
• Patient, member behavior
• Enterprise-wide data
• Enterprise analytics
• Clinical outcomes reporting
• Unified data sources
• Clinical data repositories
• Departmental data marts
• Dashboards
• Spreadsheets
• Separate data sources
• Manual collation of data
• Basic reporting
Data integration
Data warehouse
Clinical
analytics
Advanced
analytics
• What are the main predictors for readmission?
• What patients are most at risk for a bad outcome?
• What patients require intervention for me to provide best care?
• What care programs lead to the best outcome for this patient?

11. So How many of these guys do you need to run
your analytics program?

12. Organizational issues cited as key barriers to
analytics adoption
Primary obstacles to widespread analytics adoption
Organizational
Data
Financial
Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. Copyright © Massachusetts
Institute of Technology 2010. Sample size Healthcare n= 116
Lack of understanding how to use
analytics to improve the business 34%
Lack of management bandwidth due to
competing priorities
25%
Lack of skills internally in the line of
business
23%
Ability to get the data 37%
Ownership of the data is unclear or
governance is ineffective 17%
Concerns with the data 3%
Perceived costs outweigh the projected
benefits
9%
No case for change 19%
Don’t know where to start 17%
Lack of executive sponsorship 24%
Culture does not encourage sharing information 35%

13. Business Users
Define what they want to analyze
IT Builds solutions
Traditional Model
IT Creates Big Data Platform
Big Data Model
Exploratory Analysis

14. How does this change your enterprise data
architecture?

15. © 2012 IBM Corporation15
•Workload optimized relational
systems
•SSOT, Operational, Analytic
•10-100’s of TB of data
•High Performance and thruput
Operational
Databases
Analytical
Data Marts
Data Warehouse
Execs, Category
Managers, Buyers
Analysts, Data Scientists,
Analytic Apps
Digital
CRM
Pharmacy
Supply
Chain
Call
Center
Source systems
Finance
Evolutionary Phase I

16. © 2012 IBM Corporation16
Operational
Databases
Analytical
Data Marts
Data Warehouse
Execs, Category
Managers, Buyers
Analysts, Data Scientists,
Analytic Apps
Social
Unstructured
Sources
Mobile
Apps
Twitter
Hadoop
• Unstructured data processing
• QueryableArchive
• Analytics Sandbox
• Low cost storage
Digital
CRM
Pharmacy
Supply
Chain
Call
Center
Source systems
Finance
Evolutionary Phase II

17. © 2012 IBM Corporation17
Operational
Databases
Analytical
Data Marts
Data Warehouse
Social
External
Sources
Mobile
Apps
Twitter
Real Time Decisioning
•Low latency analtyics
•Analyze data “in-motion”
•In-memory, Complex events
•Scale out model
Digital
CRM
Pharmacy
Supply
Chain
Call
Center
Source systems
Finance
Evolutionary Phase III

18. © 2012 IBM Corporation18
Social
External
Sources
Custom
Apps
Twitter
Big Data Platform
Systems
Management
Application
Development
Visualization
& Discovery
Analytic Accelerators
Information Integration & Governance
BI /
Reporting
Exploration /
Visualization
Functional
App
Industry
App
Predictive
Analytics
Content
Analytics
Analytic Applications
• Integration
• Visualization
• Development
• Security
• Governance
Hadoop
System
Stream
Computing
Data
Warehouse
Digital
CRM
Pharmacy
Supply
Chain
Call
Center
Source systems
Finance

19. Big Data Challenges Have Diverse Requirements
Manage and analyze
unstructured data3
Hadoop File System / MapReduce
Text Analytics
Analyze data in real time4 Stream Computing
Discover, explore and
navigate big data sources
Federated Discovery, Search and Navigation1
Extreme Performance –
run analytics closer to data2
Massively Parallel Processing
Analytic Engines
Rich library of analytical
functions and tools5
In-Database Analytics Libraries
Big Data visualization
Integrate and govern all
data sources
6
Integration, Data
Quality, Security, Lifecycle
Management, MDM

20. Each of these Use Cases Combine Multiple
Technologies
Pre-processing
Ingest and analyze unstructured data types
and convert to structured data
Combine structured and unstructured analysis
Augment data warehouse with additional external
sources, such as social media
Combine high velocity and historical analysis
Analyze and react to data in motion; adjust models
with deep historical analysis
Reuse structured data for exploratory analysis
Experimentation and ad-hoc analysis with structured
data

21. Big data adoption
When segmented into four groups based on current levels of big data activity, respondents showed significant consistency in
organizational behaviors Total respondents n = 1061
Totals do not equal 100% due to rounding
Organizations are adopting big data in phases

22. Solutions
IBM Big Data Platform
Analytics and Decision Management
Big Data Infrastructure
The IBM Big Data Platform

23. Solutions
IBM Big Data Platform
Analytics and Decision Management
Big Data Infrastructure
Delivers deep insight
with advanced in-
database analytics &
operational analytics
 PureData for
Analytics – expert
integrated systems to
make advanced
analytics faster
&simpler
Data
Warehouse
Data
Warehouse
The IBM Big Data Platform

24. Solutions
IBM Big Data Platform
Analytics and Decision Management
Big Data Infrastructure
Stream
Computing
Data
Warehouse
Analyze streaming data
and large data bursts
for real-time insights
 InfoSphere Streams
– software enabling
continuous analysis of
massive volumes of
streaming data with
sub-millisecond
response times
Stream
Computing
The IBM Big Data Platform

25. Solutions
IBM Big Data Platform
Analytics and Decision Management
Big Data Infrastructure
Hadoop
System
Stream
Computing
Data
Warehouse
Cost-effectively analyze
Petabytes of
unstructured and
structured data
 InfoSphere
BigInsights --
enterprise-grade
Hadoop system
enhanced with
advanced text
analytics, data
visualization, tools, &
performance features
for analyzing massive
volumes of structured
and unstructured
data.
Hadoop
System
The IBM Big Data Platform

26. Solutions
IBM Big Data Platform
Analytics and Decision Management
Big Data Infrastructure
Information Integration & Governance
Hadoop
System
Stream
Computing
Data
Warehouse
Govern data quality and
manage the information
lifecycle
 InfoSphere Information
Server –Cleanses data,
monitors quality and
integrates big data with
existing systems
 InfoSphere Optim –
manages business
information throughout its
lifecycle
 InfoSphere Master
Data Management –
manages and maintains
trusted views of master
and reference data
 InfoSphere Guardium–
real-time database
security and monitoring
Information Integration & Governance
The IBM Big Data Platform

27. Solutions
IBM Big Data Platform
Analytics and Decision Management
Big Data Infrastructure
Accelerators
Information Integration & Governance
Hadoop
System
Stream
Computing
Data
Warehouse
Speed time to value
with analytic and
application accelerators
 Analytic
Accelerators – text
analytics, geospatial,
time-series, data
mining
 Application
Accelerators –
financial services,
machine data, social
data, Telco event data
 Industry Models
- comprehensive data
models based on
deep expertise and
industry best practice
Accelerators
The IBM Big Data Platform

28. Solutions
IBM Big Data Platform
Analytics and Decision Management
Big Data Infrastructure
Accelerators
Information Integration & Governance
Hadoop
System
Stream
Computing
Data
Warehouse
Systems
Management
Application
Development
Visualization
& Discovery
Discover, understand,
search, and navigate
federated sources of
big data
 InfoSphere Data
Explorer – Discovery
and navigation
software that provides
real-time access and
fusion of big data with
rich and varied data
from enterprise
applications for
greater insight
Visualization
& Discovery
The IBM Big Data Platform