Weitere ähnliche Inhalte
Ähnlich wie Big data cloud cloud circle keynote_final laura colvine 8th november 2012 (20)
Big data cloud cloud circle keynote_final laura colvine 8th november 2012
- 1. Understanding the real impact when
#Big Data meets #Cloud
Real world experiences, observations and findings
Laura Colvine
Cloud Strategy Leader
IBM United Kingdom and Ireland @LauraColvine
©2012 IBM Corporation
- 3. Big Data? - hype or reality?
| ©2012 IBM Corporation
- 5. The need for Cloud based Big Data and insight is
accelerating
85% of Fortune 500
organisations will be unable to
exploit big data for competitive
advantage through 2015
Source: Gartner: predictions for 2012
60% potential 50% of Global 1000
companies will store customer-
increase in retail operating
margins with big data sensitive data in cloud by year-
Source: McKinsey Global Institute: Big data, The end 2016.
Next Frontier for innovation, competition and Source: Gartner: predictions for 2012
productivity, May 2011
| ©2012 IBM Corporation
- 6. ‘Three out of four organisations have big data activities
underway’…. and patterns of adoption are forming
Big data activities
Converging Data Architectures
Rebalancing data architecture portfolio,
blending compute and storage requirements
Context-based services
Where you are and what you are doing will
drive the next wave of digital services.
Consumable data services
The ability to share data will make it more
valuable--but only if it is managed differently.
Data for Insight and Impact
Visualisation and Discovery: Discover,
understand, search, and navigate federated
Respondents were asked to describe the state of sources of big data while leaving that data in
big data activities within their organisation. place.
Total respondents n = 1061
Totals do not equal 100% due to rounding
6 | ©2012 IBM Corporation
- 7. Key Finding 1: Customer Outcomes and Optimisation are
driving big data initiatives across industry groups.
Healthcare /
Consumer Goods Financial Services
Life Sciences
Customer-
centric
outcomes
Operational
optimisation
Risk / financial
Manufacturing Public Sector management
Telecommunications
New business
model
Employee
collaboration
7 | ©2012 IBM Corporation
- 8. Convergence of physical, human and business process
data for better outcomes
Enterprises
Human/ People
Serving Aspirations of
Domain
Intergenerational
Consumer
Consumers
Physical
Domain Operational
Domain
| ©2012 IBM Corporation
- 9. How are forward thinking organisations
using big data and cloud?
| ©2012 IBM Corporation
- 10. Organisations are Putting Big Data and Big Insights
to work
Creating scalable, efficient,
and trusted information,
systems
Optimising complex
decision making, spot
trends and anomalies,
predict outcomes
Using resilient architectures
either on premise or in the
cloud.
| ©2012 IBM Corporation
- 11. To Unlock the potential organisations master three
competencies to drive sustainable advantage
Align Anticipate Act
Organise, collaborate see, predict and shape with confidence to optimise
and connect the people, business outcomes service outcome
data and processes
| ©2012 IBM Corporation
- 12. Collaborating & Connecting People,
Align
Organisations, Data and Process
Connecting Healthcare in the Cloud
Ecosystems & Multi-Agency Stadium in the Clouds
| ©2012 IBM Corporation
- 13. Using Data and Cloud to Provide
Anticipate
Actionable Insight
Growing from
2.5 PB to 6 PB
of data
97%
Reduction in wind
forecast response time –
from weeks to hours.
Vestas
71 71
| ©2012 IBM Corporation
- 14. Act on Insights to improve service
Act outcome and customer satisfaction
50%
30%
Reduction in surgery-
savings in data
management costs .
30% improvement in
related hospitalisations transaction processing
Identify Genetic Patterns: efficiency
Enterprise Content Management
Banco de Crédito del Peru
Data Warehousing, Application
Infrastructure, Cloud and IT
Optimisation
Rizzoli Orthopaedic
1200%
Increase in speed of collecting traffic data.
Bucheon City, South Korea
| ©2012 IBM Corporation
- 16. Convergance will increase as cloud stretches above IT
commoditisation into business optimisation
Next Generation Cloud
Cloud-Scale Data Challenges
Easy to Use Tools for Big Insights
Cloud based Social & Collaboration tools
Server and Storage Optimisation
Cloud Workload Analysis
Data Center Lifecycle Cost Analysis Tool
Security Analytic services
IBM Big Data/ Cloud Overview
Cloud Smarter Smarter Social
Security Big Data & Sustainability
Computing Computing Commerce Business
Analytics
| ©2012 IBM Corporation
- 17. There are Snakes and Ladders in the Big Data and
Cloud Discussion
Security Cost
Compliance Business Innovation
Complexity Simplicity & Speed
Workload Optimisation Scalability
Legacy & Transition Collaboration
Skills & Culture Customer Experience /Outcome
| ©2012 IBM Corporation
- 18. Key Finding 2: Big data is dependent upon a scalable
and extensible foundation
Big data infrastructure
• Multitenant Data Platforms
• Solid information
foundation
• Scalable and extensible
• Data in the Cloud
• Platforms for Data Analysis
• Platforms for Update
intensive workloads
• Data Platforms for Large
Applications Respondents with
active big data efforts
were asked which
• Data Mash Ups platform components
were either currently
• Open Research Challenges in pilot or installed
within their
organization.
18 | ©2012 IBM Corporation
- 19. Engaging the Unengaged, Reducing risk and driving
revenue
In S ec
te u
lli rit
ge y
Enterprise nc
Embedded & Edge e
O
Automated
of Enterprise……
pt
Predictive Analytics
im
is
ed
Multi-Agency or
Prr
P
Ecosystems… Real-time,
offi
o
Responsive, Aware
ic i
ci
Single Issue or
en
en
Single Business
tt
Function at this
Ba
Manual
level
s
………Structured
ic
Data, or Unstructured
text data
Reactive Proactive
| ©2012 IBM Corporation
- 20. From Siloed to Connected bridging the line of business
and IT perspectives
Information Silos Typical Enterpri se Functional Silos
Example Enterprise Life Cycle Analytic Applications
BI / Exploration / Functional Industry Predictive Content
BI /
Initiation Phase
Strategy/Policy Reporting Visualization App App Analytics Analytics
Reporting
Plan
Definition silo silo silo silo silo silo Big Data Platform
Visualization Application Systems
Design & Discovery Development Management
Realization
Phase
silo silo silo silo silo silo
Contract Definition
silo silo silo silo silo silo
Realization/Build/Warranty
Analytics Accelerators
Operational
silo silo silo silo silo silo
Operation
Phase
Hadoop Stream Data
silo silo silo silo silo silo System Computing Warehouse
Maintenance/Modifications
Disposal
Efficiency loss/cost
estimated due loss or lack Efficiency loss/cost
Efficiency loss/cost estimated due loss or lack
estimated per life cycle of real-time information
phase/step due loss or lack integration between PA of real-time information
of information between (process automation) & OA integration between the Information Integration & Governance
phases/steps (office automation) different enterprise silos
| ©2012 IBM Corporation
- 21. Deployment Complexity combines with the conflicting
needs of multiple stakeholders, each with specific
requirements
Algorithm Composition and Invention
Data Evaluation and Fusion Testing and Execution Optimization
Streaming data
Data mining
Text data & statistics
Optimization
Multi-dimensional & simulation
Semantic
Time series
analysis
Fuzzy
Geo spatial matching
Video Solutions
& image Network
algorithms
Relational
New
algorithms
Social network
✔ Business Rules Engine Data & Analytic
Services
Data Models
Filtering and Composition and
Data Acquisition Core Analytics Deployment
Extraction Validation Packaging
Data & Analytic Runtimes Information Sources
| ©2012 IBM Corporation Workload Optimization
- 22. Workloads have specific characteristic that impact
Scalability, Optimisation and Resiliency design.
L a r g e n u m b e r o f d a t a f e e d s,
A n d / o r lo t s o f d a t a , a n d / o r
L o w / V e r y F a st
u n st r u ct u r e d d a t a
Telecom
Latency/Biz Decision Throughput
netw ork Deep Q&A Automated trading
security (DPI)
Capital market
Trade surveillance
desk Industrial process Sensor based
monitoring control w ater mgt Call center monitoring Risk analytics
Asset tracking (quality) Seismic
Real-time game platform
Data Complexity
monitoring Risk management in Processing
energy trading Card fraud
Intelligent Traffic Geospatial
Real-timedetection & tracking
Online hotel Systems Cross-sales Battlespace Inventory prevention
booking Telco QoS & SLA Shop floor command & Reservoir
monitoring monitoring control Optimization
Weather ModelingModeling
Liquidity Call center monitoring Fraud Early w arning system Astrophysic al data
management (cross sale)Manufacturing detection & for energy trading mining
system process control prevention
Clickstream
analysis CAD/CAE
Salesforce
L o w n u m b e r o f d a t a f e e d s,
Retail inventory Massive Social EDA
A n d / o r fe w e r d a t a , a n d / o r
enablement optimization Media Analysis
Health Climate
Telecom monitoring Lease Prediction
billing management
system Nuclear Energy
Baggage Health records
Hig h / S lo w
S t r u ct u r e d d a t a
Simulation
handling screening
D e gre e of A naly t ic C omple xit y
Simple Middle Road Complex
(Alerts) (Forecasting) (Simulation and Optimizat ion)
| ©2012 IBM Corporation
- 23. Cloud Systems of the future will be more data centric,
composable and scalable… but different data or
analytics workloads demand different system
characteristics
Predictive Analytics Text Analytics Optimisation
Modeling, Simulation Hadoop Workloads Sensitivity Analysis Future System
Cores SCM Cores SCM Cores SCM Cores SCM
General Purpose
+ +
Integrated Network
Integrated Processing
Integrated Storage
Network Storage Network Storage Network Storage Network Storage
Balanced, reliable, power efficient systems, with integrated software that scales seamlessly
Integrated analytics, modeling and simulation capabilities to address generation, management and analysis
of Big Data for Business Advantage
... there is NOT a 'one size fits all'
| ©2012 IBM Corporation
- 24. Determining your highly valuable data from commodity
data will affect how you span on-premise and off premise
data workloads
In-House Private cloud Hosted cloud Public cloud
for for for for
For Organisations High Value Data Commodity Data Data mashups,
with deep Big Data Sets that are sets with High Social Data,
skills Enterprise Unique Volume, High
Capacity
Beware the
economics of
data in cloud
| ©2012 IBM Corporation
- 25. The Emergence of the Data Scientist
Source: DJ Patil building the data and analytics groups at
Facebook and LinkedIn.
“Netflix, the movie-lender awarded $1m in
2009 to a team that improved the accuracy
of its recommendation algorithm”
| ©2012 IBM Corporation
- 26. In Conclusion What have we learnt from our Big Data
cloud journey?
Winners in the era of cloud and big data will be those who collaborate to unlock
data assets to drive innovation, make real-time decisions, and gain actionable
insights to be more competitive.
Plan an Align Apply
Information Your Outcome
Agenda Information Analytics
to align with your strategy and to govern the creation and to measure, anticipate and
priorities use of an integrated set of shape business outcomes
accurate and relevant
information
| ©2012 IBM Corporation