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

©2012 IBM Corporation

Understanding the real impact when

#Big Data meets #CloudReal world experiences, observations and findings

@LauraColvineLaura Colvine

Cloud Strategy Leader

IBM United Kingdom and Ireland

©2012 IBM Corporation|

The ‘art of the possible.’


Big Data? - hype or reality?


Big data characteristics in today’s digitised marketplace

4

Characteristics of big data


The need for Cloud based Big Data and insight is

accelerating

60% potential

increase in retail operating

margins with big dataSource: McKinsey Global Institute: Big data, The Next Frontier for innovation, competition and

productivity, May 2011

of Global 1000

companies will store customer-

sensitive data in cloud by year-

end 2016.

50%

of Fortune 500

organisations will be unable to

exploit big data for competitive

advantage through 2015

85%

Source: Gartner: predictions for 2012

Source: Gartner: predictions for 2012


‘Three out of four organisations have big data activities

underway’…. and patterns of adoption are forming

6

Total respondents n = 1061Totals do not equal 100% due to rounding

Big data activities

Respondents were asked to describe the state of

big data activities within their organisation.

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

sources of big data while leaving that data in

place.


Key Finding 1: Customer Outcomes and Optimisation are

driving big data initiatives across industry groups.

7

Consumer Goods Financial ServicesHealthcare /

Life Sciences

Manufacturing Public Sector Telecommunications

Customer-

centric

outcomes

Operational

optimisation

Risk / financial

management

New business

model

Employee

collaboration


Convergence of physical, human and business process

data for better outcomes

Physical

Domain Operational

Domain

Human/ People

Domain

Consumers

Enterprises

Serving Aspirations of

Intergenerational

Consumer


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

with confidence to optimise

service outcomesee, predict and shape

business outcomes

Organise, collaborate

and connect the people,

data and processes


Align

Connecting Healthcare in the Cloud

Collaborating & Connecting People,

Organisations, Data and Process

Stadium in the CloudsEcosystems & Multi-Agency


AnticipateUsing Data and Cloud to Provide

Actionable Insight

Reduction in wind forecast response time –from weeks to hours.

Vestas

97%Reduction in wind forecast response time –from weeks to hours.

Vestas

97%

717171

Growing from

2.5 PB to 6 PB

of data


1200%Rizzoli Orthopaedic

30%Reduction in surgery-related hospitalisations

Identify Genetic Patterns: Enterprise Content Management

Data Warehousing, Application

Infrastructure, Cloud and IT

Optimisation

50%savings in data management costs .

Banco de Crédito del Peru

30% improvement in

transaction processing

efficiency

Increase in speed of collecting traffic data.

Bucheon City, South Korea

Act on Insights to improve service

outcome and customer satisfactionAct


“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

Social

BusinessSustainabilityBig Data &

Analytics

Cloud

Computing SecuritySmarter

ComputingSmarter

Commerce


There are Snakes and Ladders in the Big Data and

Cloud Discussion

Security

Compliance

Complexity

Workload Optimisation

Legacy & Transition

Skills & Culture

Cost

Business Innovation

Simplicity & Speed

Scalability

Collaboration

Customer Experience /Outcome


Key Finding 2: Big data is dependent upon a scalable

and extensible foundation

18

• 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

• Data Mash Ups

• Open Research Challenges

Big data infrastructure

Respondents with

active big data efforts

were asked which

platform components

were either currently

in pilot or installed

within their

organization.


Engaging the Unengaged, Reducing risk and driving

revenue

S ecurity

Intelligence

Proficient

Proactive

Automated

Manual

Reactive

Proficient

Basic

Optim

isedMulti-Agency or

Ecosystems… Real-time,

Responsive, Aware

Single Issue or

Single Business

Function at this

level

………Structured

Data, or Unstructured

text data

Enterprise

Embedded & Edge

of Enterprise……Predictive Analytics


From Siloed to Connected bridging the line of business

and IT perspectives

Example Enterprise Life Cycle

Strategy/Policy

Initia

tion

Ph

ase

Plan

Definition

Design

Re

aliz

atio

n

Ph

ase

Contract Definition

Realization/Build/Warranty

Operation

Op

era

tion

al

Ph

ase

Maintenance/Modifications

Disposal

Information Silos Typical Enterprise Functional Silos

Efficiency loss/cost estimated per li fe cycle phase/step due loss or lack of information between phases/steps

silo

silo

silo silo silo silo silosilo

silo silo silo silosilo

silo silo silosilosilosilo

silo

silo silo silo silosilo

silo silo silosilosilo

Efficiency loss/cost

estimated due loss or lack

of real-time information

integration between PA

(process automation) & OA

(office automation)

Efficiency loss/cost

estimated due loss or lack

of real-time information

integration between the

different enterprise silos

BI /

Reporting

BI /

Reporting

Exploration /

Visualization

Functional

App

Industry

App

Predictive

Analytics

Content

Analytics

Analytic Applications

BI /

Reporting

Exploration /

Visualization

Functional

App

Industry

App

Predictive

Analytics

Content

Analytics

Analytic Applications

Big Data Platform

Systems

Management

Systems

Management

Application

Development

Application

Development

Visualization

& Discovery

Visualization

& Discovery

Analytics AcceleratorsAnalytics Accelerators

Information Integration & GovernanceInformation Integration & Governance

Hadoop

System

Hadoop

System

Stream

Computing

Stream

Computing

Data

Warehouse

Data

Warehouse


Deployment Complexity combines with the conflicting

needs of multiple stakeholders, each with specific

requirements

Data Acquisition

Streaming data

Text data

Multi-dimensional

Time series

Geo spatial

Relational

Data mining & statistics

Optimization & simulation

Fuzzy

matching

Network

algorithms

Composition and

PackagingCore Analytics

Filtering and

Extraction Validation

Social network

Video & image

Semantic

analysis

Business Rules Engine

Data Evaluation and FusionAlgorithm Composition and Invention

Testing and Execution Optimization

✔Deployment

New algorithms

Solutions

Data & Analytic

Services

Workload Optimization

Data & Analytic Runtimes

Data Models

Information Sources


Workloads have specific characteristic that impact

Scalability, Optimisation and Resiliency design.

Late

ncy/B

iz D

ecis

ion T

hro

ug

hp

ut

D e gre e of A naly t ic C omple xit y

Hig

h/

Slo

wLo

w/

Ve

ry F

ast

Simple

(Alerts)Complex

(Simulation and Optimization)

Data

Co

mp

lexit

y

La

rge

nu

mb

er

of

da

ta f

ee

ds,

An

d/

or

lots

of

da

ta,

an

d/

or

un

str

uct u

red

da

ta

Lo

w

nu

mb

er

of

da

ta f

ee

ds,

An

d/

or

few

er

da

ta,

an

d/

or

Str

uctu

red

da

ta

Middle Road

(Forecasting)

Clickstream

analysis

Real-time game

monitoring

Cross-sales

Health

monitoring

Early w arning system

for energy trading

Health records

screening

Fraud

detection &

prevention

Capital market

surveillance

Card fraud

detection &

prevention

Lease

management

system

Retail inventory

optimization

Battlespace

command &

control

Call center monitoring

(cross sale)

Salesforce

enablement

Baggage

handling

Asset tracking

Telco QoS & SLA

monitoring

Trade

desk

monitoring

Liquidity

management

system

Manufacturing

process control

Shop floor

monitoring

Online hotel

booking

Call center monitoring

(quality)

Sensor based

water mgt

Intelligent Traffic

Systems

Industrial process

control

Telecom

billing

Real-time

Inventory

Optimization

Automated trading

Risk analytics

platform Seismic

Processing

Reservoir

Modeling

Astrophysical data

mining

Geospatial

tracking

Climate

Prediction

CAD/CAE

EDA

Weather Modeling

Telecom

netw ork

security (DPI)

Nuclear Energy

Simulation

Risk management in

energy trading

Massive Social

Media Analysis

Deep Q&A


Cloud Systems of the future will be more data centric,

composable and scalable… but different data or

analytics workloads demand different system

characteristics

... there is NOT a 'one size fits all'

Cores SCM

StorageNetwork

Cores SCM

StorageNetwork

Cores SCM

StorageNetwork

Cores SCM

StorageNetwork

+ +

Predictive Analytics

Modeling, Simulation

Text Analytics

Hadoop Workloads

Optimisation

Sensitivity Analysis Future System

� 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

General PurposeIntegrated Network

Integrated ProcessingIntegrated Storage


Determining your highly valuable data from commodity

data will affect how you span on-premise and off premise

data workloads

Private cloud

forHigh Value Data

Sets that are Enterprise Unique

Public cloud

forData mashups,

Social Data,

Hosted cloud

forCommodity Data

sets with High Volume, High

Capacity

Beware the

economics of

data in cloud

In-House

forFor Organisations

with deep Big Data skills


“Netflix, the movie-lender awarded $1m in 2009 to a team that improved the accuracy of its recommendation algorithm”

Source: DJ Patil building the data and analytics groups at

Facebook and LinkedIn.

The Emergence of the Data Scientist


In Conclusion What have we learnt from our Big Data

cloud journey?

Plan an Information Agendato align with your strategy and

priorities

AlignYour Informationto govern the creation and

use of an integrated set of

accurate and relevant

information

Apply Outcome Analyticsto measure, anticipate and

shape business outcomes

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.


Thank You

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

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