Big Data at the Speed of Business - IBM Innovationen für eine neue

Big Data at the Speed of Business IBM Innovationen für eine neue Ära
Udo Hertz, Director of Information Management Development
IBM Deutschland
13. Juni 2013
© 2013 IBM Corporation
Agenda
1
4
IBM’s viewpoint
dataund
and Analytics
analytics
IBM’s
Sicht aufon
BigbigData
2
Fünf überzeugende Anwendungsfälle
3
IBM’s einzigartiger Beitrag zum Kundenerfolg
Empfehlungen für die ersten Schritte
© 2013 IBM Corporation
Auf einem Smarten Planeten verändern umwälzende
technologische Faktoren die Geschäfts- und IT-Welt
Cloud Computing
Mobile
Social Media
Internet of Things
“Data
is the
Oil” Rohstoff der Wirtschaft
Big Data
istNew
der neue
“We have for the first time an economy based on
a key resource [Information] that is not only renewable,
but self-generating. Running out of it is not a problem,
but drowning in it is.”
– John Naisbitt
Um einen Rohstoff zu nutzen, braucht es Mining, Refining und Delivering
4
4
IBM Confidential
Agenda
1
4
IBM’s Sicht auf Big Data und Analytics
2
Five
big data
use cases
Fünfcompelling
überzeugende
Anwendungsfälle
3
IBM’s einzigartiger Beitrag zum Kundenerfolg
Empfehlungen für die ersten Schritte
© 2013 IBM Corporation
Fünf Anwendungsfälle und ihre Erfolgszahlen
Enrich Your
Information Base with
Big Data Exploration
99%
Improve Customer
Interaction with
Enhanced 360º View
of the Customer
1,100
Reduction
In Time Required
For Analysis
42TB
Association
Publishing
Partnerships
Optimize Infrastructure
and Monetize Data with
Operations Analysis
60K
Metered
Customers
in Five States
Prevent Threat
and Fraud with
Security and
Intelligence Extension
Real-time
Acoustic
Data Analyzed
Gain IT efficiency
and scale with
Data Warehouse
Augmentation
40X
Gain in
Analysis
Performance
IBM Big Data Innovationen im 1. Halbjahr 2013
Enhanced
InfoSphere
Big Insights
& Streams
Big Data Platform advances in consumability and
performance
DB2
Speed of Thought Analytics
with BLU Acceleration
New
Model
System for
Analytics
System for
Hadoop
The fastest performance of Netezza Technology to
date
Explore and analyze Hadoop data with appliance
simplicity
Jede Branche kann Big Data sinnvoll einsetzen
• Optimizing Offers and
Cross-sell
• Customer Service and
Call Center Efficiency
• Fraud Detection &
Investigation
• Credit & Counterparty
Risk
• 360˚ View of Domain
or Subject
• Catastrophe Modeling
• Fraud & Abuse
• Producer Performance
Analytics
• Analytics Sandbox
• Pro-active Call Center
• Actionable Customer
Insight
• Customer Analytics &
Loyalty Marketing
• Merchandise
Optimization
• Predictive Maintenance
Analytics
• Dynamic Pricing
• Capacity & Pricing
Optimization
• Data Warehouse
Optimization
• Actionable Customer
Intelligence
• Business process
transformation
• Audience & Marketing
Optimization
• Multi-Channel
Enablement
• Digital commerce
optimization
• Shelf Availability
• Civilian Services
• Promotional Spend
Optimization
• Defense & Intelligence
• Measure & Act on
Population Health
Outcomes
• Merchandising
Compliance
• Tax & Treasury
Services
• Operational Surveillance,
Analysis & Optimization
• Data Warehouse
Consolidation, Integration
& Augmentation
• Big Data Exploration for
Interdisciplinary
Collaboration
• Engage Consumers in
their Healthcare
• Promotion Exceptions
& Alerts
!
% #
!
• Advanced Condition
Monitoring
• Location Based
Services
• Smart Meter Analytics
• Distribution Load
Forecasting/Scheduling
• Condition Based
Maintenance
• Create & Target
Customer Offerings
• Network Analytics
• Uniform Information
Access Platform
• Data Warehouse
Optimization
• Airliner Certification
Platform
• Advanced Condition
Monitoring (ACM)
"# $
• Customer/ Channel
Analytics
• Advanced Condition
Monitoring
• Increase visibility into
drug safety and
effectiveness
Agenda
1
4
IBM’s Sicht auf Big Data und Analytics
2
Fünf überzeugende Anwendungsfälle
3
IBM’s
einzigartiger
Beitrag
zum Kundenerfolg
IBM’s unique
value for
client success
Empfehlungen für die ersten Schritte
Die Vorteile der IBM Big Data Platform
Warum braucht es eine Plattform?
BIG
BIGDATA
DATA PLATFORM
PLATFORM
Systems
Application
Management Development
Discovery
Accelerators
Hadoop
System
Stream
Computing
Data
Warehouse
Information Integration & Governance
Die meisten Big Data-Anwendungsfälle
brauchen eine Kombination von Technologien
IBMs Big Data Platform verknüpft
etablierte Technologien mit Big Data
Innovationen und bildet damit die einzige
Big Data Plattform
Verschiedene Deployment-Modelle
Data
Media
Content
Machine
Social
IBM bietet einen ganzheitlichen und integrierten Ansatz
für Big Data und Analytics
ANALYTICS
Performance
Risk
Content
Decision
Management Analytics Management Analytics
Business Intelligence and Predictive Analytics
Visualization
Integration and
Model
Governance
Development and Exploration
Only IBM has expanded and evolved
Analytics for Big Data to…
• Fuel all decision-making with powerful
analytics
• Broaden analytic adoption without silos or
programming
• Analyze all data wherever it lives
• Accelerate business value with solutions that
have built-in analytics expertise
BIG DATA PLATFORM
…so organizations can find
what is business relevant in big data and
make it instantly actionable
IBM Big Data Innovationen im 1. Halbjahr 2013
Enhanced
InfoSphere
Big Insights
& Streams
Big Data Platform advances in consumability and
performance
DB2
Speed of Thought Analytics
with BLU Acceleration
New
Model
System for
Analytics
System for
Hadoop
The fastest performance of Netezza Technology to
date
Explore and analyze Hadoop data with appliance
simplicity
Innovationen im 1. Halbjahr 2013
Verbesserungen der Big Data Platform bei Handhabung und
Performance
InfoSphere BigInsights 2.1
For exploration, analysis & archiving
large volumes
and variety of data
InfoSphere Streams 3.1
For real-time analysis of
data in motion
Data at Rest
Data in Motion
Hadoop System
Stream Computing
• Big SQL
• GPFS-FPO
• High Availability
• Increased performance
• Developer enhancements
• Simplified large scale
deployments
• Enhanced integration
Innovationen im 1. Halbjahr 2013
Introducing BLU Acceleration
IBM Research & Development Lab Innovations
BLU Acceleration
• Dynamic In-Memory
In-memory columnar processing with
dynamic movement of unused data to storage
• Actionable Compression
Industry’s first data compression that preserves order
so that the data can be used without decompressing
• Parallel Vector Processing
Multi-core and SIMD parallelism
(Single Instruction Multiple Data)
• Data Skipping
Skips unnecessary processing of irrelevant data
Super
SuperFast,
Fast,Super
SuperEasy—
Easy—
Create,
Load
and
Create, Load andGo!
Go!
No
NoIndexes,
Indexes,No
NoAggregates,
Aggregates,
No
Tuning,
No
SQL
No Tuning, No SQLchanges,
changes,
No
schema
changes
No schema changes
Innovationen im 1. Halbjahr 2013
Die bisher beste Leistung der Netezza-Technologie
Accelerate Performance
Performance
Accelerate
of Analytic
Analytic Queries
Queries
of
$
#
!
e.g. 3X
3X faster
faster performance
performance11
e.g.
Increase Efficiency
Efficiency
Increase
of your
your Data
Data Center
Center
of
e.g. 50%
50% greater
greater data
data capacity
capacity per
per rack
rack22
e.g.
Simplicity and
and
Simplicity
Ease of
of Administration
Administration
Ease
e.g. improved
improved resilience
resilience
e.g.
1 Based
on a comparison of the IBM PureData System for Analytics N2001 to the IBM PureData System for Analytics
N1001. The performance speed refers to the query times on both macro-analytic and mixed workload tests as conducted
in IBM engineering lab benchmarks. The N2001 query times were an average of 3x faster than those of the N1001.
Individual results may vary.
2 Capacity of IBM PureData System for Analytics N2001 compared to previous generation IBM PureData System for
Analytics N1001.
New
Model
Innovationen im 1. Halbjahr 2013
Qualitativ beste Data Services für Big Data
zEnterprise
# $
for exploration & online archiving
% &!
!
for reporting & analytics
and operational analytics
% &
for SQL & NoSQL transactions
with enhanced Hadoop integration
in DB2 11 (beta)g
$
for highest performance transactions
with enhanced Hadoop integration
in IMS 13 (beta)
Innovationen im 1. Halbjahr 2013
Erfasst und analysiert Hadoop-Daten mit der Einfachheit einer
Appliance
Accelerate Big
Big Data
Data
Accelerate
Time to
to Value
Value
Time
e.g. Deploy
Deploy 8X
8X faster
faster than
than custom-built
custom-built11
e.g.
Simplify Big
Big Data
Data
Simplify
Adoption &
& Consumption
Consumption
Adoption
e.g. Single
Single system
system console
console
e.g.
Simplicity and
and
Simplicity
Ease of
of Administration
Administration
Ease
e.g. Only
Only integrated
integrated Hadoop
Hadoop system
system
e.g.
with built-in
built-in archiving
archiving tools
tools22
with
1 Based
on IBM internal testing and customer feedback. "Custom built clusters" refer to clusters that are
not professionally pre-built, pre-tested and optimized. Individual results may vary.
on current commercially available Big Data appliance product data sheets from large vendors. US
ONLY CLAIM.
2 Based
$
#
Agenda
1
4
IBM’s Sicht auf Big Data und Analytics
2
Fünf überzeugende Anwendungsfälle
3
IBM’s einzigartiger Beitrag zum Kundenerfolg
Empfehlungen
füron
diehow
ersten
Schritte
Recommendations
to get
started
Drei kritische Erfolgsfaktoren
20
STRATEGY & VALUE
What are the key
business issues or
opportunities that
Big Data can help me
to address?
TECHNOLOGY
What are the
essential capabilities
we need to ensure
we have in place?
PEOPLE & PROCESS
What skills and
processes do I need to
add or modify to be
successful?
Weitere Informationen
For additional information including whitepapers
and demos, please visit:
– Big Data Hub
– Smarter Analytics
Reference:
– Big Data for Smarter Decision Making by Colin
White
– Big Data Analytics - TDWI eBook
– IBV Study - www.ibm.com/2012bigdatastudy
Education:
– Social Media Analytics, YouTube video
– Understanding Big Data ebook
– Free online education at bigdatauniversity.com
Services:
– Develop your Big Data strategy with help from
IBM Global Business Services
ibm.com/bigdata
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