1. Martin Wildberger
IBM Big Data and Integration Portfolio Overview
Bringing Big Data to the Enterprise
© 2011 IBM Corporation

2. Martin Wildberger
Vice President, Information Management Development
IBM Software Group
2 © 2011 IBM Corporation

3. A Big Data Platform Addresses Big Data Use Cases …
Multi-channel customer
sentiment and experience a
analysis
Big Data Platform Detect life-threatening
conditions at hospitals in
time to intervene
Predict weather patterns to plan
optimal wind turbine usage, and
optimize capital expenditure on
asset placement
Make risk decisions based on
real-time transactional data
Identify criminals and threats
from disparate video, audio,
and data feeds
3 © 2011 IBM Corporation

4. …But Can’t Do It Alone
What does Big Data mean for your Information Architecture?
Data Warehouse Big Data Platform
Big Data will be a
permanent part of your
information architecture
It cannot be a silo – it
must be fully integrated
Enterprise
in order to leverage its Integration
value
It must be easy to
deploy and integrate
Traditional Sources New Sources
4 © 2011 IBM Corporation

5. IBM’s Big Data Platform Vision
Bringing Big Data to the Enterprise
Data
IBM Big Data Solutions Client and Partner Solutions Warehouse
InfoSphere
Warehouse
Warehouse
Appliances
Big Data User Environments Netezza
Developers End Users Administrators Master Data
Mgmt
InfoSphere MDM
INTEGRATION
AGENTS
Database
Big Data Enterprise Engines DB2
Content
Analytics
ECM
Information Server
Business
Analytics
Streaming Analytics Internet Scale Analytics
Cognos & SPSS
Marketing
Open Source Foundational Components
Unica
Hadoop HBase Pig Lucene Jaql Data Growth
Management
InfoSphere Optim
5 © 2011 IBM Corporation

6. One Example - The 360°Multi-Channel Customer Sentime nt Analysis
Business Processes
Master Data Campaign Cognos Consumer
Management Management Insight
Events and
Alerts
Big Data Platform
Web Traffic and
Social Media Insight
Website Logs
Social Media
Internet Scale Analytics
Information Data
Integration Warehouse
Call Detail Call Behavior and
Reports Experience Insight
(CDRs)
Streaming Analytics
6 © 2011 IBM Corporation

7. IBM’s Big Data Platform Addresses the Key Requirements
1. Platform for V3 – Variety, Velocity, Volume
Variety - manage data & content “As Is”
Handle any velocity - low-latency streams and large volume batch
Volume - huge volumes of at-rest or streaming data Big Data Platform
2. Analytics for V3
Analyze Sources in their native format - text, data, rich content
Analyze all of the data - not just a subset
Dynamic analytics - automatic adjustments and actions
3. Ease of Use for Developers and Users
Developer UIs, common languages & automatic optimization
End-user UIs & visualization
4. Enterprise Class
Failure tolerance, Security and Privacy
Scale Economically
5. Extensive Integration Capabilities
Integrate wide variety of sources
Leverage enterprise integration technologies
7 © 2011 IBM Corporation

8. 1. Platform for V3 – Addresses All 3 V’s
Analyze telemetry, fuel
consumption, schedule and
Variety
weather patterns to optimize
Big Data Platform shipping logistics.
Analyze 100k records/
Velocity second to address customer
satisfaction in real time
Optimize capital investments
Volume based on 6 Petabytes
of information
8 © 2011 IBM Corporation

9. 2. Analytics for V3 – Built-for-Purpose, Built-for-Variety
Leading analytics from IBM Research
Built-for-purpose to analyze data in its
native format
Text Statistics
Image & Video Mining
Acoustic Predictive
Financial Geospatial
Times Series Mathematical
IBM Differentiator – significant research investment in analytics;
designed for use with Big Data.
9 © 2011 IBM Corporation

10. 3. Ease of Use for Developers and Users
End-user Visualization Development Environment
Data exploration, crawling, and Familiar coding and tooling
analytics environment, testing, and optimization
10 © 2011 IBM Corporation

11. 4. Enterprise Class
High availability architecture
Failure
to support hardware or
Tolerance
application failure.
Big Data Platform
Runs on scalable hardware
Scale with the ability to
Economically dynamically add additional
nodes.
Security protection for
Security &
granular data access
Privacy
control.
11 © 2011 IBM Corporation

12. 5. Enterprise Integration
Data Warehouse Big Data Platform
Trusted Information &
Governance
– Companies need to
govern what comes in,
and the insights that
come out
Enterprise
Integration
Data Management
– Insights from Big Data
must be incorporated into
the warehouse
Traditional Sources New Sources
12 © 2011 IBM Corporation

13. Building with the Open Source Community
Big Data Platform
Leveraging …and
Open Source Giving
Innovation … Back
…Contributing…
jaql
PIG
ZooKeeper
13 © 2011 IBM Corporation

14. Announcing: InfoSphere BigInsights v 1.1
Platform for V3
Hadoop foundation
Large-scale indexing
Platform
BigInsights Enterprise
Analytics for V3 Edition
Integrated text analytics Licensed
Enterprise Class
DB2/RDBMS and Data Warehouse Integration
Usability
Provisioning and Advanced Security
Development Studio
BigInsights Job and workflow management
Admin console (incl. HDFS Basic Edition
explorer) Free download with Large Scale Indexing
Enterprise Class Apache 24 x 7 Web
Text Analytics
Tooling
Hadoop support
Provisioning, storage, and
advanced security
Integration Capabilities
Integrated install Hadoop POC Pilot Enterprise
Connectivity with DB2, Up-and-running Deployment
InfoSphere Warehouse and Deployment Sizes
IBM Smart Analytics
System.
14 © 2011 IBM Corporation

15. Internet-Scale Analytics in Action
Financial Services Utilities
Improved risk decisions Weather impact analysis on
Customer sentiment analysis power generation
AML Smart meter data analysis
Transportation IT
Weather and traffic Transition log analysis
impact on logistics and for multiple
fuel consumption transactional systems
Call Centers E Commerce
Voice-to-text mining for Analyze internet behavior
customer behavior and buying patterns
understanding Digital asset piracy
Telecommunications
Operations and failure
Multi-channel Integration
Integrated customer behavior
analysis from device, sensor,
modeling
and GPS inputs
15 © 2011 IBM Corporation

16. Announcing: InfoSphere Streams v 2.0
A Platform for V3
Runtime optimizations delivering performance improvements.
Improved Java™ support allows shared Java Virtual Machines for
better resource utilization and improved extensibility
Analytics & Usability
New toolkits that delivers more operators and functions out of the
InfoSphere Streams box
Analytics for text, data mining, statistics, among others
Enterprise Class
Improved monitoring capabilities and deployment flexibility to
enhance availability and simplify administration
Integration Capabilities
Connectivity is expanded to support Netezza TwinFin, Microsoft
SQLServer, and MySQL, in addition to DB2, Informix®, solidDB®,
and Oracle databases.
16 © 2011 IBM Corporation

17. Streaming Analytics in Action
Stock Market
Impact of weather on securities prices
Natural Systems Analyze market data at ultra-low latencies
Wildfire management
Water management Law Enforcement,
Defense & Cyber Security
Real-time multimodal surveillance
Transportation Situational awareness
Intelligent traffic Cyber security detection
management
Fraud Prevention
Detecting multi-party fraud
Real time fraud prevention
Manufacturing
Process control for
microchip fabrication
e-Science
Space weather prediction
Detection of transient events
Health & Life Sciences Synchrotron atomic research
Neonatal ICU monitoring
Epidemic early warning Other
system Telephony Smart Grid
Remote healthcare CDR processing Text analysis
monitoring Social analysis Who’s talking to whom?
Churn prediction ERP for commodities
Geomapping FPGA acceleration
17 © 2011 IBM Corporation

18. IBM clients have embraced the Big Data opportunity and are
stretching beyond the traditional frontiers of Business Intelligence
Derive a 360 degree view of Enable real-time customer Process and correlate large
customer behavior across all analysis that processes volumes of physiological
channels and Identify billions of records per day. data streams in conjunction
opportunities for more with persistent data, such
targeted marketing activities. Support IT and business as lab test results to
requirements for uncover hidden patterns
sophisticated analytics in in test results that would
real-time, with a focus on otherwise be very difficult to
churn prevention. identify.
Integration: Integration: Integration:
POS data sourced from Improve analytics Use data store to define rules
existing data warehouse. performance of warehouse by for streaming data analytics.
offloading record processing. Iteratively refine rules.
18 © 2011 IBM Corporation

19. Leading Organizations are Partnering with IBM for Big Data
Big Data Platform
IBM’s Big Data Platform
Broadest platform to bring Big Data to the Enterprise
A Platform for V3 – Analyzing the Variety, Velocity and
Volume of structured and unstructured data
Leveraging the Broader IBM
InfoSphere Information Integration and Governance portfolio
InfoSphere Warehouse, Netezza appliances and IBM Smart Analytics
System
Cognos Consumer Insight – Big Data social media analytics solution
ECM – content management and analytics
Tivoli – integrated service management
Smarter Computing – efficient and innovative IT infrastructure
GBS – Business Analytics and Optimization services
19 © 2011 IBM Corporation