Big data cloud cloud circle keynote_final laura colvine 8th november 2012

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

The ‘art of the possible.’

| ©2012 IBM Corporation

Big Data? - hype or reality?


Big data characteristics in today’s digitised marketplace

Characteristics of big data

4 | ©2012 IBM Corporation

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


‘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


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


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

How are forward thinking organisations
using big data and cloud?


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.


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


Collaborating & Connecting People,
Align
Organisations, Data and Process

Connecting Healthcare in the Cloud

Ecosystems & Multi-Agency Stadium in the Clouds


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


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

“Are we there yet?”....


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

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


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.


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


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
Realization/Build/Warranty
Analytics Accelerators
Operational

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


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

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)


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'

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


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”


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


Thank You


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