smarter planet and megatrends

© 2012 IBM Corporation

Smarter Planetmegatrends- Jørgen Floes, Chief Architect Nordic SO Delivery

Smarter Planet megatrends

Smarter Planet megatrends - © 2012 IBM Corporation 2

The IT world is changing - driven by shifts in market dynamics and client needs

Analytics2

Through 2015, more than 90% of business leaders contend information is a strategic asset, yet fewer than 10% will quantify its economic value

Big Data1

Through 2015, more than 85 percent of Fortune 500 organizations will fail to effectively exploit big data for

competitive advantage

Mobile Enterprise5

66% of CIOs ranked mobility as a top investment priority in 2012

Social Business3

By 2014, 20% of business users will replace email as the primary interpersonal communications with social networking

Security7

Through 2016, the financial impact of cybercrime will grow 10 percent per

year, due to the continuing discovery of new vulnerabilities

Cloud6

63% of companies already are using (Cloud) or expect to use it in 2012, up

from 49% last year

Growth Markets8

By 2015, IDC expects emerging markets to generate over 33% of all IT spending

Smarter Planet4

IBM Smarter industry solutions will grow 5x faster than total solutions

CustomerSets

Software

Technology

Services

Hardware

Business

Services



IBM’s transformation has gone through distinct phases in the past decade; the “smarter” phase of the journey is underway

2002

Sharing & partnering

2010

Making things smarter

� Instrumented, interconnected, intelligent

� Enable growth and productivity

� Optimize the whole system

2006

Globally integrating

� Right skills, right place, right cost

� Rationalize support functions for greater efficiency

� Radically simplify processes

� Consistent set of processes worldwide

� Leverage best practices

� Standardize and reduce waste

� Governance and performance discipline

Being essential

� New era of computing

� Changing client

� Evolving IBMer

2012


Global Technology Outlook



IBM Global Technology Outlook Objectives

GTO identifies significant technology trends

early. It looks for high impact disruptive

technologies leading to game changing

products and services over a 3-10 year

horizon.

Technology thresholds identified in a GTO

demonstrate their influence on clients,

enterprises, & industries and have high

potential to create new businesses.



GTO 2012 – The Genetic Map


Technology example: The Watson System



Future Systems – The Learning Paradigm

Training and Learning EnginesTo Build Models and Define Insight

Hypothesis EnginesTo Understand and Plan Actions

Policy EngineBusiness, Legal

and Ethical Rules

Verification Engines(e.g. Simulations)

Active Learning

(Natural Interfaces)

Outcome EngineActuation and Validation

Society Nature Institutions Archives



Watson – a Workload Optimized System

Hardware

� 90 x IBM Power 750 servers � 2880 POWER7 3.55 GHz cores

� 500 GBps on-chip bandwidth

� 15 Terabytes of memory

� 500 GB of data (in memory)

Software

� IBM DeepQA,

� UIMA and UIMA AS

� Apache Hadoop



Learning Systems Roadmap to Meet the Challenge

Greater Autonomy

Static Learning Systems

2010 2015 2020

Expert teams identify

features across

industries, create first

commercial learning

systems

Dynamic Learning Systems

Dynamic Data CorpusExpand Hypothesis Generation to different domains (leverage crowd-sourcing)

Add Scorers for Different Input Modalities: images, video, voice, environmental, biological;

leverage new devices & hardware acceleration

Deeper Reasoning: Allow higher-levels of semantic abstraction. Leverage new hardware.

Domain Adaptation Tools

Autonomous Learning Systems

Achieve understanding

of natural language,

images and other sensory

information. Hypothesis

and question generation

across arbitrary domains;

meta-heuristic to

automate algorithm

choices

Keyword Search

Delivers lists

based on

keywords &

human filters

1985

Biological Inspiration: Cognitive Process Understanding

Autonomously & accurately identify essential features across multiple domains.Learning systems must understand context to disambiguate.

Watson

2025


Big Data and Predictive GovernanceSpeaker: Steven Adler



Internet of Things Scenarios Show a Pattern of Customer Pain Points

� Collecting information to provide better service and work smarter

� Establishing the smarter planet!

Food SafetyWater Management Grid

TransportationHome HealthcareLogistics



Forecasting a hurricane(www.noaa.gov)

Fitting a curve to data

Model UncertaintyAll modeling is approximate

Process UncertaintyProcesses contain

“randomness”

Uncertainty of data arises from many sources

Uncertain travel times

Semiconductor yield

Intended Spelling Text Entry

Actual Spelling

GPS Uncertainty

??

?

RumorsContaminated?

{John Smith, Dallas}

{John Smith, Kansas}

Data UncertaintyData input is uncertain

Ambiguity

{Paris Airport}Testimony

Conflicting Data

???



Global Data Volume in Exabytes

Sensors

(Internet of Things)

Multiple sources: IDC,Cisco

100

90

80

70

60

50

40

30

20

10

Aggregate Uncertainty %

VoIP

9000

8000

7000

6000

5000

4000

3000

2000

1000

0

2005 2010 2015

By 2015, 80% of all available data will be uncertain

Enterprise Data

Data quality solutions exist for enterprise data like customer, product, and address data, but this is only a fraction of the

total enterprise data.

By 2015 the number of networked devices will be double the entire global population. All

sensor data has uncertainty.

Social Media

(video, audio and text)

The total number of social media accounts exceeds the entire global

population. This data is highly uncertain in both its expression and content.



Every Smarter Planet Solution has Big Data and Needs Big Analytics

Smarter Planet

Data at RestData in Motion

Deep AnalyticsReactive Analytics

Predictive ModelsReal-time Awareness

Deeper InsightsFaster Decisions

BigData



Up to 10,000 Times larger

Up to 10,000 times faster

Traditional Data Warehouse and Business Intelligence

Data Scale

Data Scale

yr mo wk day hr min sec … ms µs

Exa

Peta

Tera

Giga

Mega

Kilo

Decision FrequencyOccasional Frequent Real-time

Data in Motion

Data at Rest

New “Big Data” Brings New Opportunities, Requires New Analytics

Telco Promotions

100,000 records/sec, 6B/day

10 ms/decision

270TB for Deep Analytics

DeepQA

100s GB for Deep Analytics

3 sec/decision

Smart Traffic

250K GPS probes/sec

630K segments/sec

2 ms/decision, 4K vehicles

Homeland Security

600,000 records/sec, 50B/day

1-2 ms/decision

320TB for Deep Analytics


Smarter Computing: Expert Integrated SystemsSpeaker: Jean-Michel Rodriguez



Big Data Systems Require a Data-centric Architecture for Performance

Data lives on disk and tape

Move data to CPU as needed

Deep Storage Hierarchy

Data lives in persistent memory

Many CPU’s surround and use

Shallow/Flat Storage Hierarchy

Old Compute-centric Model New Data-centric Model

Massive ParallelismPersistent Memory

Largest change in system architecture since the System 360 Huge impact on hardware, systems software, and application design

Flash Phase Change

Manycore FPGA

input

output



HDD cost advantage continues, 1/10 SCM cost, but

SCM dominates in performance, 10,000x faster than HDD

Storage Class Memory - The Tipping Point for Data-centric Systems

1000000.1HDD

100015FLASH

101SCM

1100DRAM

Relative Latency

Relative Cost

Source: Chung Lam, IBM

FLASH

(Phase Change)

SCM in 2015$0.05 per GB$50K per PB$0.10 / GB

$0.01 / GB



2015 Vision: The Peta2 Data-centric System

� Goal– Data-centric, latency optimized, massive capacity system for long-running analytics and simulation applications

� Requirements– 1 Petaflop performance, massively parallel– 1 Petabyte BPRAM– Extremely low latency, high bandwidth interconnect (Xapt)

� Assumptions– Majority of data stays resident in memory for long periods, minority of data updated continually

– Persistent Memory becomes primary storage

This system can become the System 360 equivalent for the Era of Analytics

Petascale = Petaflop OR Petabyte

Petaflop x Petabyte = Peta2

2015 Peta2 Data-centric SystemPOWER 8/A2, BPRAM ITEs on Harrier

Peta2 Analytics

Appliance

+

Reactive+Deep Analytics

Platform

Relative SystemDensity per Rack

Peta2

1x

12x

1.4x2.3x

281x 336x

35.7x



Announcing the First Two Members of the IBM PureSystems Family

Infrastructure System: Expert at sensing andanticipating resourceneeds to optimize your

infrastructure

Platform System: Expert at optimally

deploying and runningapplications for rapid

time-to-value



Applications

Storage

Networking

Virtualization

ManagementCompute

Tools

Flexible and open choice in a fully integrated system

IBM PureFlex System is Integrated by design Expert

IntegratedSystems


Mobility: Enabling mobile workstylesand embrasing cloudSpeaker: Erik Pedersen



Advancing the Workplace of the Future

24



Mobile device adoption, focused on accelerating security


Social BusinessSpeaker: Stuart McRae



Adoption of Social Tools is Increasing Rapidly

Public

Companies plan to increase spending on social despite recessionGrowth in Adult Social Network Site Use, 2005-2009

Enterprise

Fortune Global 100 Companies

Companies with ...

65%

54% 50%

33%

TwitterAccounts

FacebookFan Pages

YouTubeChannels

CorporateBlogs

Twitter Accounts

65%72% 71%

40%

67%

Total US Europe Asia LatAm

Facebook Fan Pages

54%

69%

52%40%

33%


50%59%

52%

35% 33%


YouTube Accounts

US Mobile Subscribers Accessing Social Sites

Source:Burston-Marsteller

347%4,7001,051Twitter

-7%11,43912,338MySpace

112%25,13711,874Facebook

ChangeJan-10Jan-09

(Thousands)

Source:comScore MobiLens



Convergence of Social and Analytic Technologies Transform the Way Businesses Operate

Socially Synergistic Enterprise SolutionsNew top-line opportunities, better relationships with customers and partners,

enhanced talent pool, increased resiliency and efficiency

Data

� Data aggregation

� Smart filtering

� Meaning extraction

� Consumable analytics

� Process orchestration

� Stream processing

Analytics

� Customer Sentiment

� Unmet Needs

� Talent Discovery

� Reasoning and Decision Support

� Crowdsensing, Crowdsourcing

� Teaming, Incentives, Motivation

Society

Organizations

Teams

Individuals

Social

SocialData from and about People

PhysicalSensors & Streams

Enterprise

Business Process

Transformation



Socially Synergistic Enterprise Solutions Can Provide Differentiating Value

Physical Meets DigitalCustomer Care and Insight Workforce Optimization

Financial Operations Smarter Commerce Advanced Case Management



Thank you!