Extended deck around data phenomena from (big)data to Extended deck around data and digital...

@pieroleo www.linkedin.com/in/pieroleo


From Managing (Big)Data to Manage

Cogs

http://www.linkedin.com/in/pieroleo




Module 1: Big Data1 – Technological Factors 2 – Big Data Metaphors & IT Paradigm Shifts 3 – Business Factors 4 – Big Data Applications5 – Big Data IT Perspective6 – Human Factor!7 – Mining unstructured and non conventional data

Module 2: Big Data Applications8 – Customer Analytics9 – Capitalizing On Social Media Data Today 10 – Exploring an Enterprise Social Analytics Enviroment11 – Social Analytics 12 – Deep Dive on a Social Analytics Project

Module 3: Beyond Big Data13 – Cognitive Computing 14 – How IBM Watson works 15 – Cognitive Computing at Work16 – Cognitive Advisors17 – A Cognitive Ecosystem18 – Watson Developer Cloud19 – Computational Creativity20 – Search, Deep Analytics & Mining21 – Analytics for ALL!22 – Examples of advanced cognitive research areas

Topics

From Managing (Big)Data to Manage

Cogs





DATA





DATA is the new basis of

competitive advantage.....

.......and the engine of

Digital Transformation






competitive advantage............and the engine of


CAMSS Data as a Gravity New natural resource

New business models

Human FactorBig Data and IT Text Analytics MultiMedia

Analytics






competitive advantage............and the engine of


Capitalizing On Social Media

Customer Analytics Techniques Social Analytics

Cognitive Computing

Cognitive AdvisorsIBM Watson Watson Ecosystem

Customer Analytics





Big Data1 – Technological Factors




@pieroleo www.linkedin.com/in/pieroleoMagritte

Manet Dal Monte

Leonardo





Magritte

Manet Dal Monte

Leonardo

CLOUD ANALYTICS

SOCIAL MOBILE

Digital Transformationof individuals and

organizations




@pieroleo www.linkedin.com/in/pieroleoMagritte

Manet Dal Monte

Leonardo

CLOUD ANALYTICS

SOCIAL MOBILE

DIGITAL TRANSFORMATION =

(…..Big Data ......)





Data has a gravity!





Source: http://www.bloomberg.com/video/meet-the-world-s-most-connected-man-Vs~LzkbkR7yhjza~7nji1g.html

Meet the World's Most Connected Man

Video 1





Core Observations and why data value is emerging





Big Data2 – Big Data Metaphors & IT

Paradigm Shifts





15

>80% Unstructured Data

+ External Data“Untouched” Data+ Stream of Data

Enterprise Data Machine Data People Data

Big Data metaphor 1





Data is there and we need to make the best out of it

Big Data metaphor 2





We produce and consume Data for a specific purpose

Big Data metaphor 2





Surce: http://pennystocks.la/internet-in-real-time/

Big Data Faces: the Internet in Real-Time

Big Data metaphor 3





19

SocialData from and about People

PhysicalSensors & Streams

Terabytes to exabytes of existing data

to process

Streaming data, milliseconds to seconds to

respond

Structured, Semi-structured Unstructured,

text & multimedia

Uncertainty from inconsistency,

ambiguities, etc.

Volume

Velocity

Variety

Veracity

DataContent

>80%

<20%

Traditional Enterprise Data

Big data embodies new data characteristics created by today’s digitized marketplace

BiologicalDNA Sequencers





20 20

Glo

bal

Dat

a V

olu

me

in E

xab

ytes

Sens

ors

(Inte

rnet

of T

hing

s)

Multiple sources: IDC,Cisco

100

90

80

70

60

50

40

30

20

10

Agg

rega

te U

ncer

tain

ty %

VoIP

9000

8000

7000

6000

5000

4000

3000

2000

1000

0

2005 2010 2015

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

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.




@pieroleo www.linkedin.com/in/pieroleoParadigm shifts enabled by big data and analytics

TRADITIONAL APPROACH

Analyze small subsets of information

Analyzedinformation

All available

information

BIG DATA & ANALYTICS APPROACH

Analyze all information

All available

informationanalyzed

Leverage more of the data being captured

Data leads the way— discover new emerging properties

Reduce effort required to leverage data

Leverage data as it is captured


Carefully cleanse information before any analysis

Small amount of carefully organized information


Analyze information as is, cleanse as needed

Large amount of messy

information

Hypothesis Question

DataAnswer


Start with hypothesis andtest against selected data


Explore all data andidentify correlations

Data Exploration

CorrelationInsight

Repository InsightAnalysisData


Analyze data after it’s been processed and landed in a warehouse or mart

Data

Insight

Analysis


Analyze data in motion as it’s generated, in real-time





Big Data 3 – Business Factors





Source: http://datacoup..com

Value of Data

Pietro Leo's SecondIncome!





Just ONE Transaction path goes to the end in thousands and to complete that path tens of decision points were considered. Right now we store and analyze in our transactional systems just the transaction end points.

Buyer ….Win!!!

Buying Decision Labyrinth

Yes!

Big Data is the answer and the need of the new emerging sub-transactional era





It's an invitation-only loan product offered exclusively to Amazon Sellers. The Amazon loans offer very competitive 10.9 - 12.9% interest rates and no pre-payment penalty.

The power of a sub-transactional knowledge





The age of new competition: Alibaba

Sept. 29, 2014 1:56 a.m. ET

Source: http://online.wsj.com/articles/alibaba-affiliate-wins-approval-to-start-private-bank-1411970203Source: http://www.bloomberg.com/news/2014-09-23/alibaba-arm-aims-to-create-163-billion-loans-marketplace.html

Sep 24, 2014





For Science, Big Data is the microscope of the 21st century

Wine DNA Tracing





Source: Cornell University - Maize kernal infected with Aspergillus flavus, which produced aflatoxin.http://www.plantpath.cornell.edu/labs/milgroom/Research_aflatoxin.html And http://www.special-clean.com/special-clean/en/mold/mold-lexicon-1.php

For science, Big Data is the microscope of the 21st century



http://www.plantpath.cornell.edu/labs/milgroom/Research_aflatoxin.html



Source: A statue representing Janus Bifrons in the Vatican Museums

Big Data as a new Business Concept and as a new Technology Concept



30@pieroleo www.linkedin.com/in/pieroleo


Big Data as a new business concept: New values and opportunities for a number of stakeholders

Chief Marketing Officerhow to improve customer focus?...could predict the right offer for the right customer at the right time and improve customer value and intimacy or prevent churn?

Chief Product Designer...how we can innovste? … could

we improve our product channels/design offering??

Chief Finance Officer

...could streamline compliance and understand risk

exposure across businesses and

regions?

Chief Risk Officer...uses anti fraud predictive analytics to detect and prevent rapid fire anomalous transactions or wire transfers identified as high probability of fraud?

Chief Executive Officer...could make better business decisions using accurate data across all company/system dimensions and across time horizons: past, present and future?

Chief Information Officer ...could analyze oceans of machine generated logs to

predict which components or equipment in the datacenter are likely to fail and thereby avert a disruption

during critical quarter end? How we can support Zero high risks or manage crisis?

Big Data

Analytics





We need to combine internal and external data, utilized and under-utilized data, structured and unstructured data... and cross-link organization knowledge & data silos

CRM• emails• claims• call center scripts• Chats with customers• …

Transactional Info.:• Transactions• Orders• consultancies• …

Legal Info:• Contracts• Complaints• Reports• Legal Actions• Fraud Data• …

Knowledge Management•Manuals, wikis, couses•Projects Data•Market Analysis•RSS Business Feeds•Data feed: Bloomberg reuters• …

IT SystemsSystem LogsApplication logs: web, vending machines, mobileVideoSensor Networks, RFID• …

Social Media:• Global Social Networks: tweeter, facebook, etc.• Small communities: blogs, muros corporativos,• Internal Social Networks (employees)• News • … Big

DataAnalytics

Big Data as a new technology concept





“Big Data is the set of technical capabilities,

management processes and

skills for converting vast, fast, and varied data into Right Data to produce useful

knowledge”

Source: Definition discussed during the work of the Word Summit on Big Data and Organization Design Paris – 2013 and Adapted from: Beacon Report – Big Data Big Brains – 2013

In summary, what is Big Data?





New Organization Design: What is New and Different?

A lot more data and different kinds of data.Historically most data was structured data – rows and columns

Today it is unstructured data like aerial photos, audio from call centers, video from surveillance cameras, e-mails, texts, diagrams.

A shift in focus from data stocks to data flows.Historical information was stored in data warehouses and analyzed by data mining.

Streaming data arrives in real time allowing us to influence events as they happen. We can prevent some bad events from ever happening at all.

Shift in the power structure of the company. Many companies have analog establishments. We need to shift power to those who can draw valuable insights from data and analytics and implement them.

Shift from periodic to real time or continuous decision making. We need an increase in the clock speed of every process in the company.

There is a potential for “Big Data” to become a fundamental center for the company. Is it a new dimension of structure?

Organization Design IssuesTechnology Issues

Source: Jay R. Galbraith





Big Data4 – Big Data Applications





UtilitiesWeather impact analysis on power generationTransmission monitoringSmart grid management

Retail360° View of the CustomerClick-stream analysisReal-time promotions

Law EnforcementReal-time multimodal surveillanceSituational awarenessCyber security detection

TransportationWeather and traffic impact on logistics and fuel consumptionTraffic congestion

Financial ServicesFraud detectionRisk management360° View of the CustomerTelematics

ITSystem log analysisCybersecurity

TelecommunicationsCDR processingChurn predictionGeomapping / marketingNetwork monitoring

What can you do with Big Data?

Health & Life SciencesEpidemic early warningICU monitoringRemote healthcare monitoring





IBM Institute for Business Value and the Saïd Business School partnered to benchmark global big data activities

36

IBM Global Business Services, through the IBM Institute for Business Value, develops fact-based strategies and insights for senior executives around critical public and private sector issues.

Saïd Business School University of Oxford

IBM Institute for Business Value

The Saïd Business School is one of the leading business schools in the UK. The School is establishing a new model for business education by being deeply embedded in the University of Oxford, a world-class university, and tackling some of the challenges the world is encountering.

www.ibm.com/2012bigdatastudy





Big Data Analytics has evolved from business initiative to business imperative

63%

58%

37%

2012

2011

2010 70% increase

Source: 1 2010 and 2011 datasets © Massachusetts Institute of Technology. 2 Analytics: The real-world use of big data. 2012 Study conducted by IBM Institute for Business Value, in collaboration with Säid Business School at the University of Oxford.

3.6x

Likelihood of organizations competing on analytics to outperform their peers2

Percentage of respondents who cited a competitive advantage from the use

of information and analytics1,2





Three out of four organizations have big data activities underway; and one in four are either in pilot or production

38

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 organization.

Early days of big data era Almost half of all organizations surveyed

report active discussions about big data plans

Big data has moved out of IT and into business discussions

Getting underway More than a quarter of organizations have

active big data pilots or implementations Tapping into big data is becoming real

Acceleration ahead The number of active pilots underway

suggests big data implementations will rise exponentially in the next few years

Once foundational technologies are installed, use spreads quickly across the organization





Five key findings highlight how organizations are moving forward with big data

39

Big data is dependent upon a scalable and extensible information foundation2

The emerging pattern of big data adoption is focused upon delivering measureable business value5

Customer analytics are driving big data initiatives1

Big data requires strong analytics capabilities4

Initial big data efforts are focused on gaining insights from existing and new sources of internal data3





Key Findings: Customer analytics are driving Big Data initiatives

Big dataInfrastructure

Big dataSources

Analytics capabilitiesTotal respondents n = 1061

Big data objectives

Top functional objectives identified by organizations with active big data pilots or implementations. Responses have been weighted and aggregated.

Customer-centric outcomesOperational optimizationRisk / financial management

New business model

Employee collaboration

Big Data areas of work





Big data leadership shifts from IT to business as organizations move through the adoption stages

41

CIOs lead early efforts Early stages are driven by CIOs once

leadership takes hold to drive exploration

CIOs drive the development of the vision, strategy and approach to big data within most organizations

Groups of business executives usually guide the transition from strategy to proofs of concept or pilots

Business executives drive action Pilot and implementation stages are

driven by business executives – either a function-specific executive such as CMO or CFO, or by the CEO

Later stages are more often centered on a single executive rather than a group; a single driving force who can make things happen is critical

Leadership shifts

Respondents were asked which executive is most closely aligned with the mandate to use big data within their organization. Box placement reflects the degree to which each executive is dominant in a given stage.

Total respondents n = 1028





Big Data5 – Big Data IT Perspective





Data WarehouseOperational Analytics

Structured, analytical, logical

Big DataAd Hoc Analytics

Creative, holistic thought, intuition

Big Data is augmenting traditional IT investments

Hadoop &Streaming

Data

New Sources

UnstructuredExploratory

Iterative

StructuredRepeatable

Linear

Data Warehouse

TraditionalSources

Enterprise Integration

Customer data

Transaction data

3rd party data

Core system data

Web Logs, URLs

Social Data

Text Data: emails, chats

Log data

Contact Center notes

Geolocation data

Sensor Data and Imagery

RFID





Manage & store huge volume of any data

Hadoop File System

MapReduce

Manage Streaming Data

Stream Computing

Analyze Unstructured Data Text Analytics Engine

Data WarehousingStructure and control data

Integrate and govern all data sources

Integration, Data Quality, Security, Lifecycle Management, MDM

Understand and navigate federated big data sources

Federated Discovery and Navigation

From an IT perspective leveraging Big Data and Big Data Analytics requires multiple platform capabilities





Bg Data Foundations

Analytic Appliances

Analytic Appliances

Security, Governance and Business ContinuitySecurity, Governance and Business Continuity

Information Movement, Matching & Transformation


Landing, Exploration& Archive

Landing, Exploration& Archive Enterprise

WarehouseEnterprise Warehouse

Data MartsData Marts

Real-Time AnalyticsReal-Time Analytics

Data Sources

Structured Operational

Unstructured

ExternalSocial

SensorGeospatial

Time Series

Streaming

BI & Performance Management

Predictive Analytics & Modeling

Exploration & Discovery

Actionable Insights

Raw Data Structured Data Text Analytics Data Mining Entity Analytics Machine Learning

Video/AudioNetwork/Sensor

Entity AnalyticsPredictive

Q&R, OLAPDeep AnalyticsPredictive

High PerformaceAnalytics

High PerformaceQuery

ETL, Data Quality

Auditing, De-identification

CognitiveAdvisors

Master Data

Management

Master Data

Management

Big Data IT Approach





The IBM experience and PoV

Analytic Appliances

Analytic Appliances

Security, Governance and Business ContinuitySecurity, Governance and Business Continuity



Landing, Exploration& Archive

Landing, Exploration& Archive Enterprise

WarehouseEnterprise Warehouse

Data MartsData Marts

Real-Time AnalyticsReal-Time Analytics

Data Sources

Structured Operational

Unstructured

ExternalSocial

SensorGeospatial

Time Series

Streaming

BI & Performance Management

Predictive Analytics & Modeling

Exploration & Discovery

Actionable Insights

CognitiveAdvisors

Master Data

Management

Master Data

Management

Big Data IT Approach

IBM MDM

Watson Explor

Watson

Cognos

SPSS

Guardium, Optim

InfoSphere Data Click, Information Server, G2

InfoSphere BigInsights (Hadoop)

PureData for Analytics, IDAA

DB2 BLU,PureData for

Analytics

PureData for Analytics

InfoSphere Streams





Exploits all the business potential inherent in Big Data Analytics

Scientific Method

Visualization

Domain Expertise TOM

Hacker Mindset

MathData

Engineering

Advanced Computing

StatisticsData Scientist

A Data Scientist

Explores and examines data from multiple disparate sources

Sifts through all incoming data with the goal of discovering a previously hidden insight

Has strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge

Represents an evolution from the business or data analyst role

Has a solid foundation typically in computer science and applications, modeling, statistics, analytics and math.

The role of a Data Scientist





Big Data6 – Human Factor!





Sheryl Sandberg, COO, apologised for 'poor communication' of the study

Said Facebook never meant to upset users with the secret research

Was part of a study to see if people's moods are affected by content

Information Commissioner now investigating whether or not the site breached data regulations

Facebook has apologised to its users after a secret psychological experiment has sparked outrage in the online community

Facebook admitted it had manipulated the news feeds of nearly

700,000 users without their

knowledge as part of a psychology

experiment.

Source: http://www.forbes.com/sites/kashmirhill/2014/07/02/sheryl-sandberg-apologizes-for-facebook-emotion-manipulation-study-kind-of/

With Big Data #TRUST (plus #Securityplus #Privacy) matter





Source: http://www.ted.com/talks/sherry_turkle_alone_together

Sherry Turkle:Connected, but alone?

These days phones in our pockets are changing our minds and hearts offer us three gratifying fantasies and NEW challenges and risks for us:

1) We can put our attention where we want to be

2) We always be heard

3) We never left to be alone





Big Data7 – Mining unstructured and non

conventional data





Massive Unstructured is the biggest data wave of all

1990’s 2020’s

Video

Text

Exa

Peta

Tera

Giga

Da

ta V

olu

me

2000’s 2010’s

Structured data

Audio

Image

Med

High

Low

Co

mp

uta

tio

na

l N

ee

ds

So

ph

isti

ca

tio

n o

f A

na

lys

is

Ex

pre

ss

ive

ne

ss

Digital Marketing

10+% of video views

Wide Area Imagery

100’s TB per day72 video hrs/minute

Media

Source: IBM Market Insights based on composite sources

Safety / Security

Healthcare

Customer

1B camera phones

1B medical images/yr

10s millions cameras

Enterprise Video

Used by 1/3 of enterprises





Structured versus Unstructured Information: What does it mean?

Know this is the last name and this is their age

The information is unambiguous

The context of the information is known

Pre-defined and machine-readable





Structured versus Unstructured Information: What does it mean?

Office Location is unstructured

Address

City

Zip code….





Structured

The context of the

information is known

There is no pre-defined data

model and structure

- Library Catalogues (date, author, place, subject, etc)- Census records (Italian Istat record: birth, income, employment, place etc.)- Economic data (GDP, PPI, ASX etc.)- FaceBook like button (big-data collection)- Phone numbers (and the phone book)- Databases (structuring fields)…….

- A web-page- Word-precessed document- A Newspaper- Health records- Image on Pintrest- Movie-….

Of course in several cases they overlap!

Unstructured Information









The Enquire reported that the attractive, Ms Brown,

CEO of Textract Corp, had been recently spotted drunk at

Summit meeting in Zurich,…………At 42, Ms. Brown, is

the youngest CEO at the Summit,…

<Organization><Name>

<Title>

<Proper Name> <Occupation>

Example of Annotation of a Text – “construct meaning from free form text, include identification and labeling the text with specific meanings”

<Positive ><Negative >

Unstructured Information:The context of the information is not known and is interpreted by the computer using mathematical techniques

Unstructured Information:The context of the information is not known and is interpreted by the computer using mathematical techniques





Text Analytics: transforms UnStructured Information into Structured data

Before After

Concept/entity extractionRelationship extractionSentiment Analysis

Linguistic Analysis CategorizationClustering,

Text AnalyticsTasks

DocumentSummarization….





Automotive Quality Insight• Analyzing: Tech notes, call logs, online

media• For: Warranty Analysis, Quality

Assurance• Benefits: Reduce warranty costs, improve

customer satisfaction, marketing campaigns

Crime Analytics• Analyzing: Case files, police records, 911 calls…• For: Rapid crime solving & crime trend analysis• Benefits: Safer communities & optimized force deployment

Healthcare Analytics• Analyzing: E-Medical records, hospital

reports• For: Clinical analysis; treatment protocol

optimization• Benefits: Better management of chronic

diseases; optimized drug formularies; improved patient outcomes

Insurance Fraud• Analyzing: Insurance claims• For: Detecting Fraudulent activity & patterns

• Benefits: Reduced losses, faster detection, more efficient claims processes

Customer Care• Analyzing: Call center logs, emails, online

media• For: Buyer Behavior, Churn prediction• Benefits: Improve Customer satisfaction

and retention, marketing campaigns, find new revenue opportunities, recostruct life stages and life events

Social Media for Marketing• Analyzing: Call center notes, multiple

content repositories• For: churn prediction, product/brand

quality • Benefits: Improve consumer satisfaction,

marketing campaigns, find new revenue opportunities or product/brand quality issues

A first set of examples leveraging Text Mining / Analytics



Multimedia Analytics



A beautiful Vacation!

Checco

Greta

http://visual-recognition-demo.mybluemix.net/





An Example of a Multimedia Analytics Environment

http://mp7.watson.ibm.com/imars/




@pieroleo www.linkedin.com/in/pieroleoMultimedia Analytics flow: Feature extraction, modeling, and application of semantics and context are required to deliver insights

Labeled DataUnlabeled Data

K-means Bayes NetClustering

Markov Model

Decision Tree

Modeling

ColorSpectrum

Edges

Camera Motion

Feature Extraction

EnsembleClassifiers

Texture

Active Learning

Deep Belief Nets

Vehicle tracking Activity classificationSafe zone monitoring

Locations ActivitiesScenes

Safety/Security

Behaviors

Objects

PeopleEvents

Tracks

Moving Objects

Actions

Neural Net

classification

scoringSemantics

Multimedia

AdaBoost

Blobs

BackgroundSegmentation

Zero-crossings

Support Vector Machine

Gaussian Mixture Model

Hidden Markov Model

Frequencies





Video-based Appraisal: Goal: improve home, automobile,

or marine insurance process using supporting multimedia data

Use video by insurance policy holder to document insured items

Automatically turns the video into the basis for appraisals and claims

Insurance

Public Safety and Security: Goal: ensure safety and security

in transit system Detect suspicious activities, safety

concerns, and crowd conditions using camera-based analytics

Support real-time alerting and forensic search over video data

Transportation

In Store Video Analytics: Goal: use existing store cameras

to tell who is entering the store and demographics

Bring video to aisles to tell how long people look at products and ads, what they picked up, whether they placed in cart

Extend campaign management and customer analytics solutions with in-store analytics

Retail

Consumer Goods

Identify Logo Exposure: Goal: automatically annotate

videos with logo version and calculate exposure time

Identify multiple logo appearancesin the same frames

Identify distorted logos on clothing and promotional items

Many enterprises are investigating next generation multimedia analytics-based solutions





Big Data Applications8 – Customer Analytics





Chewing Gum Wall in California

Source: http://en.geourdu.co/buzz/bizarre-shocking/chewing-gum-wall-in-california/

San Luis Obispo





Portraits from New York

Stranger Visions

In Stranger Visions artist Heather Dewey-Hagborg creates portrait sculptures from analyses of DNA material collected in public places.

Source: http://deweyhagborg.com/strangervisions/




@pieroleo www.linkedin.com/in/pieroleoCustomer Analytics: Adding Value at Every Point of Interaction and leveraging customer Digital Footprints

Systems of Record Systems of Engagement

Customer Customer AnalyticsAnalytics

Big Data Analytics





69

All perspectivesPast (historical, aggregated)

Present (real-time, scenarios)

Future (predictive, prescriptive)

At the pointof impact

All decisionsMajor and minor;

Strategic and tactical;Routine and exceptions;Manual and automated

All informationTransaction/POS data

Social data Click streams

SurveysEnterprise content

External data (competitive, environmental, etc.)

All peopleAll departments

Front line, back officeExecutives, managers

EmployeesSuppliers, customers and

consumersPartners Customer

Analytics

Challenge: Consider all data points





What are people saying?

How do people feel about my brand?

Who is this individual like?Who does she influence/follow?

What are her preferences?What words/offers will engage her?

Customer AnalyticsPractical CHALLENGES





360°Integrated Customer View

!Customer Analytics challenge: build a 360°Integrated Customer View … and more





SINGLE VIEWBusiness Data,

Social Data, Interactive data


Marketing

Cust. Care

Sales

Risk, Fraud

Customer Analytics challenge: build a 360°Integrated Customer View … and more





SINGLE VIEWBusiness Data,

Social Data, Interactive data


Marketing

Cust. Care

Sales

Risk, Fraud

How?How?Why?Why?

Who?Who? What?What?

Customer Analytics challenge: build a 360°Integrated Customer View … and more





Monitoring and Reporting

Analytics of Aggregates Analytics of Individuals &

specific groups

ListeningListening

EngagementEngagement

DemographicsDemographics

PublishingPublishingMeasurement Net Promoter

Network Topology

Sentiment AnalysisSentiment Analysis

Brand AnalysisBrand Analysis

Identity AnalysisIdentity AnalysisPredictive AnalysisPredictive Analysis

SNASNA Pattern DetectionPattern Detection

Intrinsic PreferencesIntrinsic Preferences

Social GenomeSocial GenomeMicro-SegmentationMicro-Segmentation

Next Best OfferNext Best OfferMessaging/campaigns

Face Recognition Visual Recognition

Age Detection

Image TaggingGender Recognition

Identity Recognition





Com

p le xi ty

Techniques

CapabilitiesCognos - Big Insights – SMA - SPSS – Watson Explorer – Adv. Analytics & Cognitive Services

From CHALLENGES to TechniquesAnd Capabilities





CustomerAnalytics & TRUST

“Trust men and they will be true to you; treat them greatly and they will show themselves great.”

Ralph Waldo Emerson




@pieroleo www.linkedin.com/in/pieroleoConsumers are open to share their personal information, with the exception of financial data, when there is perceived benefit

Consumer Maintains Control of DataWhat is your willingness to provide information in exchange for something relevant to you (non-monetary)?

Source: IBV Retail 2012 Winning Over the Empowered Consumer Study n= 28527 (global) P04: What is your willingness to provide information for each of the following items if [pipe primary retailer] provided something relevant to you in exchange?

25% 27%41% 41% 44% 46%

63%30% 30%

28% 29% 28% 28%

21%45% 43%33% 30% 28% 26%

15%

0%

20%

40%

60%

80%

100%

Media Usage(e.g. Mediachannels)

Demographic (e.g. age,ethnicity)

Identification(name,

address)

Lifestyle (# ofcars, homeownership)

LocationBased

Medical Financial

Completely Disagree Neutral Completely willing





Big Data Applications9 – Capitalizing On Social Media Data

Today





Social Data is not a SINGLE and omogeneos source: it is a complex aggregate of content that we can leverage in dependance of well defined

Business Use Cases.

General Rule for Social Data





Examples of Social Media Outlets

More than 1 billion unique users visit Youtube each month watching over 6 billion hours of video

More than 388 million people view more than 12.7 billion blog pages each month

There are 500 million tweets daily – that’s 5,700 per second

50% of Facebook users check it daily – there are more than 1 billion users world wide

79





Gartner “Must Sees: The Social Marketing Ops Neighborhood”

80

SOURCE: Gartner’s Adam Sarner Blog : Must Sees In The Social Marketing Ops Neighborhood In 2014

“Listening” Moves To Predictive or Prescriptive Recommendations in 2014





81

Da

ta

So

urc

es

Organizational Maturity & Sophistication

Quantify & Operationalize

Integrate Transparently

Tactical Monitor & Respond

Mainstream/Limited Social Media

Monitor & Engage Lightweight “Domain-

Specific” Analytics

SaaS-Only

Identify & Track KPIs Qualitatively Improve

Marketing Decisions Open-up Social

Media Marketing Channel

Identify & Measure ROI Operationalize Insight

via Business Processes Quantitatively Improve

Marketing Decisions

Ca

pa

bil

itie

sB

us

ine

ss

Ou

tco

me

s

Predict & Improve Outcomes With Continuous Feedback

Quantitatively Optimize Decisions Across Functions

Limited Governance

Limited sentiment Network & influencer

analysis Limited back-end

process integration

SaaS & On Premise

Business Intelligence

Broad Public Social Media Sourcing (“Big Data”)

Enterprise CRM & Transactional Data

Private & Public Communities

Full Sentiment Geo-Spatial Analysis Platform Analysis Predictive Modeling SaaS & On Premise

Seamless Integration of Internal, Extranet & Public Social Media Analysis & Action

Systemic Governance

Predict & Integrate

Complete Back-End Sourcing: ERP, HR, etc

3rd-Party Datasets OEM-Level Sourcing

of “Big Data”

Partner / Ecosystem Datasets

Embedded Social Analytics

“Targeted Crowd Sourcing”

Social Analytics Maturity Curve





Smart Organizations Think Beyond “Likes”

82

Analytics drives strategies across more than just marketing so you can:

Understand attitudes, opinions and evolving trends in the market Change course faster than competitors Identify primary influencers in social media segments Predict customer behavior Improve customer satisfaction Develop competitive human resource strategies

What do “likes” or “tweets” really tell you?





Social Media Challenges For Marketing Teams and Other Business Functions

How do we know what is being said about us across all social media channels?

There are so many social media outlets and new ones emerging rapidly, how can we possibly monitor it all?

Wouldn’t it be great to use social media data to refine our strategies, business plans, messaging and more?

83





CMOs are Underprepared for New Market Dynamics

84





85

Businesses are ‘Zeroing In’ On Customers Through Social Channels

Getting closer to customer

People skills

Insight and intelligence

Enterprise model changes

Risk management

Industry model changes

Revenue model changes

88%

81%

76%

57%

55%

54%

51%

CEO Focus Over Next 5 Years

Enhance customer loyalty/advocacy 67%

Design experiences for tablet / mobile

Use social media as a key channel

Use integrated software to managecustomers

Monitor the brand via social media

57%

56%

56%

51%

Measure ROI of digital technologies

Analyze online / offline transactions

47%

45%

CMO 5 Year Focus Toward Digital

Sources: IBM’s 2011 Global CMO Study: From Stretched to Strengthened (2011) & IBM’s 2010 Global CEO Study – Capitalizing on Complexity

IBM C-Suite studies show significant focus on social media.





8686

Marketing is Driving The Conversation but Other LOB Functions are also Employing Social Activities

Top functions applying social approaches

Marketing

Public relations

Human resources

Sales

Customer Service(call center)

IT

67%

54%

48%

46%

41%

38%

75%

64%

62%

60%

54%

53%

Today Next two years

29%

30%

42%

26%

19%

12%

Percentage growth from base

Source: Institute for Business Value, Business of Social Business Study, Q1. Which functions within your company are applying social business practices today and which are planning to apply them within the next two years? Global (n = 1161)





Source: http://www.businessinsider.com/huge-social-media-manager-does-all-day-2014-5?IR=T

We Got A Look Inside The 45-Day Planning Process That Goes Into Creating A Single Corporate Tweet

24 may 2014

After 1 Month!

A risky job !





Source: http://www.businessinsider.com/huge-social-media-manager-does-all-day-2014-5?IR=T

We Got A Look Inside The 45-Day Planning Process That Goes Into Creating A Single Corporate Tweet

13 Mar 2015

After 1 year!

A risky job !





Big Data and Social Analytics13 – Customer Analytics

Techniques

A cura di: Pietro Leo





UtilitiesWeather impact analysis on power generation

Transmission monitoringSmart grid management

Retail360° View of the CustomerClick-stream analysisReal-time promotions

Law EnforcementReal-time multimodal surveillanceSituational awarenessCyber security detection

TransportationWeather and traffic impact on logistics and fuel consumption

- Traffic congestion- 360° View of the Customer

Financial ServicesFraud detectionRisk management360° View of the Customer

ITSystem log analysisCybersecurity

TelecommunicationsCDR processingChurn predictionGeomapping / marketingNetwork monitoring- 360° View of the Customer

Mining unstructured and non conventional

data around “customers”

Health & Life SciencesEpidemic early warningICU monitoringRemote healthcare monitoring







specific groups

ListeningListening




Network Topology









Age Detection







Com

p le xi ty

Techniques

CapabilitiesCognos - Big Insights – SMA - SPSS – Watson Explorer – Adv. Analytics & Cognitive Services

From CHALLENGES to TechniquesAnd Capabilities





Big Data Applications10 – Exploring an Enterprise Social Analytics

Enviroment







specific groups

ListeningListening




Network Topology









Age Detection







Co

mp

lex ity

Cognos - Big Insights – SMA - SPSS – Watson Explorer – Adv. Analytics & Cognitive Services

Techniques

Capabilities

CustomerAnalyticsPractical CHALLENGES





Social Media Analytics a best in breed solution from IBM

94

IBM Social Media Analytics

Employs IBM Research assets for demographic, geographic, and behavioral analytics that are light-years’ ahead

Leverages Big Data capabilities

Integrates with advanced analytics for best in class sentiment analysis and segmentation (SPSS)

Available in 8 distinct sentiment languages:English, German, French, Chinese, Spanish & Dutch, Russian and Brazilian Portuguese

User-friendly, easy-to-edit pre-built dashboards

Deployment options: On premise or SaaS





IBM SMA overall Framework

Social Media Impact

Social Media RelationshipsSocial Media Discovery

Social Media SegmentationARE WE MAKING THE RIGHT INVESTMENTS IN PRODUCTS/SERVICES, MARKETS,CAMPAIGNS

EMPLOYEES, PARTNERS?

ARE WE REACHING THE INTENDED AUDIENCES - AND ARE

WE LISTENING?

WHAT NEW IDEAS CAN WE DISCOVER?

WHAT IS DRIVING SOCIAL MEDIA ACTIVITY, BEHAVIOR

AND SENTIMENT?

• Share of Voice

• Reach• Sentiment

• Geographics, Demographics

• Influencers, Recommenders, Detractors

• Users, Prospective Users

• Affinity• Association• Cause

• Topics• Participants• Sentiment

95




@pieroleo www.linkedin.com/in/pieroleoIBM Social Media Analytics provides rich information for Actionable Insights

Demographics

Affinity

Evolving Topics

Influencer Scoring and Sentiment

Behavioural Analytics Geographics

IBM Social Media Analytics

Video 9





Segment: Author Demographics

97





Assess Social Media Impact: Are we successful? Where can we do better?

Situation Examples:

• Improve brand reputation with customers, employees, partners

• Assess investment in marketing campaigns, employee programs

• Understand impact of product features

Measures:

• Share of voice: Relative volume• Reach: Distribution across sources• Influencer analysis• Sentiment: Distribution by sentiment• Geographical differences

Actions:• Improve message to market•Change marketing mix•Update employee programs• Introduce new product features•Target new suppliers

98




@pieroleo www.linkedin.com/in/pieroleoSegment Social Media Audiences: Are we hitting target audience? Have we identified potential new target?

Situation:

• Enter new market or grow target market share

• Improve market/sales effectiveness• Recruit top talent• Identify Supply Chain disruptions

Measures:

• Demographics - context• Influencer impact • Author behavior patterns • Geographic differences

Actions:

• Improve targeted programs• Move to second supplier• Change marketing mix • Plan new recruitment strategies99




@pieroleo www.linkedin.com/in/pieroleoIdentify Relevant Relationships: Is there strong grouping of negative or positive terms to drive new approaches?

Situation:

• Grow market share vs. competition• Improve employee satisfaction• Select new vendors

Measures:

• Product Feature Affinity • Employee Sentiment Affinity• Vendor Reputation Affinity• Competitive analysis

Actions:• Better target messaging• Change marketing mix• Partner risk identification• Update employee programs• Introduce new features

100




@pieroleo www.linkedin.com/in/pieroleoDiscover new ideas…and risks: What we did not know about our model What are my next steps?

Situation:

• Expand product lines • Understand the “market” voice• Identify brand risks• Learn what don’t we know

Measures:

• Emerging topics – share of voice• Emerging topics – sentiment • Emerging topics – reach• Emerging topics – geography

Actions:

• Identify new market, product etc.• Improve market positioning • Change marketing mix• Update model• Introduce new features

101





IBM Social Analytics on Cloud – Technical Architecture Overview

Data Sources Analysis DistributionDeliver

y Media

Stakeholders

Blogs, forums,News,Communities

Social Media

Other Sources*

Client Supplied Information (sites, feeds)

Client Supplied Information (Databases)

Adhoc analysisFlat Files

Analytics EngineSMA/SPSS

SPSS Modeler

Glimpse

Sentiment Analytics

TextAnalytics

Key Influencer Mapping

Affinity Analytics

Event Detection

Deep Sentiment

MiningTargeted Influencer Analytics

Unstructured Entity Integration

Customer Segmentation

Customer Analytics

Social Media Warehouse

IBM DB2

Reporting

Adhoc ReportsInteractiveDashboards

SMA/SPSS

Cognos Event Studio

Command Center

Text & Predictive Analytics

Intelligence customer

profile

Unica/CRM

Client Side Business Users

Customers & customer facing agents through mobile apps, web sites

and personalized messaging

RESTservic

e

Research Differentiating Capabilities (DC)

Act

ion

ab

leIn

sigh

ts





Big Data Applications11 – Social Analytics Advanced Techniques







specific groups

ListeningListening




Network Topology









Age Detection







Co

mp

lex ity


Techniques

Capabilities





@pieroleo www.linkedin.com/in/pieroleoText, text, text.... text




@pieroleo www.linkedin.com/in/pieroleoExtracts Consumer Attributes from text fragments:Identity Analytics Challege

Personal Attributes• Identifiers: name, address, age, gender, occupation…• Interests: sports, pets, cuisine…• Life Cycle Status: marital, parental

Personal Attributes• Identifiers: name, address, age, gender, occupation…• Interests: sports, pets, cuisine…• Life Cycle Status: marital, parental

Products Interests • Personal preferences of products• Product Purchase history• Suggestions on products & services

Products Interests • Personal preferences of products• Product Purchase history• Suggestions on products & services

Life Events• Life-changing events: relocation, having a baby, getting married, getting divorced, buying a house…

Life Events• Life-changing events: relocation, having a baby, getting married, getting divorced, buying a house…

Monetizable intent to buy products Life Events

Location announcementsIntent to buy a house

I'm thinking about buying a home in Buckingham Estates per a recommendation. Anyone have advice on that area? #atx #austinrealestate #austin

I'm thinking about buying a home in Buckingham Estates per a recommendation. Anyone have advice on that area? #atx #austinrealestate #austin

Looks like we'll be moving to New Orleans sooner than I thought.Looks like we'll be moving to New Orleans sooner than I thought.

College: Off to Stanford for my MBA! Bbye chicago!College: Off to Stanford for my MBA! Bbye chicago!

I'm at Starbucks Parque Tezontle http://4sq.com/fYReSjI'm at Starbucks Parque Tezontle http://4sq.com/fYReSj

I need a new digital camera for my food pictures, any recommendations around 300?

I need a new digital camera for my food pictures, any recommendations around 300?

What should I buy?? A mini laptop with Windows 7 OR a Apple MacBook!??!

What should I buy?? A mini laptop with Windows 7 OR a Apple MacBook!??!

Timely Insights• Intent to buy various products • Current Location• Sentiment on products, services, campaigns• Incidents damaging reputation• Customer satisfaction/attrition

Timely Insights• Intent to buy various products • Current Location• Sentiment on products, services, campaigns• Incidents damaging reputation• Customer satisfaction/attrition

Relationships• Personal relationships: family, friends and roommates…• Business relationships: co-workers and work/interest network…

Relationships• Personal relationships: family, friends and roommates…• Business relationships: co-workers and work/interest network…

http://syss071.pok.ibm.com:8080/smarc_web/





Identity Analytics Models

Strong Weak

Big Match






Strong Weak

Big Match





109

AMEX Example: Business Models based on connecting Virtual and Real Words model

American ExpressSmart Offer

A portal that collects special offers and discounts from retailers and detail about the customer segment that is target

Marketing segmentation engine that evaluate customer profiles and select the best coupon to propose

Moble app and connection with Twitter, Facebook e Foursquare to communicate with the customers and enable viral effects

Just virtual Coupons are managed! Customers activate the coupon and receive on montly basis on the credit card account the equivalent of the coupon discounts after that transactions were registred

API









What Data AMEX Sync acquires from Facebook data......?





Let's zoom on Piero Leo Facebook profile....

I authorized AMEX... for

I authorized AMEX... for






Strong Weak

Big Match





Maybe our politicians should take a playbook out of the rivalry between duke/unc and take it to the courts http://ity.com/wfUsir

Maybe our politicians should take a playbook out of the rivalry between duke/unc and take it to the courts http://ity.com/wfUsir

I'm at Mickey's Irish Pub Downtown (206 3rd St, Court Ave, Raleigh) w/ 2 others http://4sq.com/gbsaYR

I'm at Mickey's Irish Pub Downtown (206 3rd St, Court Ave, Raleigh) w/ 2 others http://4sq.com/gbsaYR

@silliesylvia good!!! U shouldnt! Think about the important stuff, like ur 43rd birthday ;) btw happy birthday Sylvia ;)

@silliesylvia good!!! U shouldnt! Think about the important stuff, like ur 43rd birthday ;) btw happy birthday Sylvia ;)

Location

Intent to consume

@silliesylvia I <3 your leather leggings!! Its so katniss!!

@silliesylvia I <3 your leather leggings!! Its so katniss!!

Age

Personal Attributes• Sylvia Campbell, Female, In a

Relationship• 32 years old, birthday on 7/17• Lives near Raleigh, NC• College graduate; Income of 80-120k

Buzz/Sentiment• Retweets BF’s comments• Interest in BBC shows: Downton Abbey,

Sherlock, Fringe, (P&P?)• Sherlock Holmes, Robert Downey, Jr.• Hunger Games, Katniss/J. Lawrence

Interests/Behavior• Watch movies, tv shows• Romance plots, “hero types”, strong

women• Uses iPad 3, Redbox, Hulu• Shopping , interest in sales/deals• Duke/ UNC basketball

@silliesylvia $10 dollars says matthew & mary get married next season :) #downtownabbey

@silliesylvia $10 dollars says matthew & mary get married next season :) #downtownabbey

Behavior

Interest

@bamagirl can’t wait to watch sherlock with you! Oh, robert downey jr, I still love you but bbc is so amazing

@bamagirl can’t wait to watch sherlock with you! Oh, robert downey jr, I still love you but bbc is so amazing

OMG OMG. just dropped my new ipad3 crappola!!!

OMG OMG. just dropped my new ipad3 crappola!!!

Interest

Consumption

Prediction

dear redbox please have kings speech for my new tv colin firth movie marathon

dear redbox please have kings speech for my new tv colin firth movie marathon

360 degree profile

Intent to consume

Consumption

Recostruct a virtual User Interest Profile



http://twitter.com/silliesylvia










Social MediaConsumer Profiles

Social MediaConsumer Profiles

CustomerModels

CustomerModels

Entity Integration

Entity Integration

Predictive Analytics

Predictive Analytics

Data Ingest & prep.

Data Ingest & prep.

Text Analytics: Timely InsightsText Analytics: Timely Insights

Entity Integration:

Profile Resolution

Entity Integration:

Profile Resolution

Predictive Analytics:

Action Determination

Predictive Analytics:

Action Determination

Social Media Data

Social Media Data

Full Example of a pipeline from social media datas

Online Flow: Data-in-motion analysis

Text Analytics

Text Analytics

Offline Flow: Data-at-rest analysis

Timely

Decisions

Large-scale data-at-rest analysis Large-scale data-in-motion analysis Advanced text analysis, entity integration, and predictive modeling using common analytics

infrastructure

Large-scale data-at-rest analysis Large-scale data-in-motion analysis Advanced text analysis, entity integration, and predictive modeling using common analytics

infrastructure

Social Media Data

CustomerDatabase

CustomerDatabase

ConsumerLists

ConsumerLists

Customer & Prospect

profiles

Customer & Prospect

profiles

EntityIntegration

EntityIntegration





© 2014 IBM Corporation116

C. Johnson123 Main Street

512-545-1234

CRMSupply Chain

FulfillmentSupport Ticketing

External Sources

3rd Party

Chris Johnston123 Main Street

512-554-1234Shipping:

456 Pine Ave

Christine. Johnson123 Main Street

Call lengthSemi-structured notes

Satisfaction

C. JohnsonMain Street

512-554-1234

C. Johnson125 Main Street

512-554-1234

ChrisJohnson65“Likes” Clothes, Camping Gear @ChristyJohnson65 Christy65

Circle / Network data

Order Mgmt.

Internal / Structured

External / Unstructured

Web

[email protected]

Big Match

Big Match matches all

these records

Big Match combines the MDM probabilistic matching engine & pre-built algorithms & BigInsights for customer matching in a native BigInsights application

Increased Value of Customer only if…

Christine JohnsonMarried1 child4/15/74

Christy65Mail Order responder

Specialty ApparelPartner Sales data

VIP: GoldCustomer Sat: 80%

Influence Score: 8/10

IBM Internal Use Only





Big Data Applications11 – Social Analytics

Advanced Techniques (part b)







specific groups

ListeningListening




Network Topology









Age Detection







Co

mp

lex ity


Techniques

Capabilities






Personality Insights from my Twitter Stream





Personality traits

Values and Needs

When I talk





Intrinsic traits e Network Potenziale

360°Integrated

Customer View

“Omni-Profile”

External traits +

Several semantic layers can be recostructed: Psycholinguistic Analytics

“I love food, .., with … together we … in… very…happy.”

Word category: Inclusive

Agreeableness

Performs complex linguistic analytics

http://systemudemo.almaden.ibm.com:9080/systemu/login





http://your-celebrity-match.mybluemix.net/

Examples of Systems that uses Personality Insights

http://usermodeling-ao15.mybluemix.net/systemu/home#findmymatch





Personality Insight as a service

http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/personality-insights.html





http://1001loveletters.com/Cartas.aspx?Id=589

My beloved (name)

I love and adore you. Ever since I first laid eyes on you I was certain they would never again picture sweeter image.Your beauty and finesse seduced me right away. Your voice reached my ears like the sweetest melody, beating the lustful pulse of my aching heart.Ever since that first glance my life shifted as a whole, because in an instant I understood what love really is, because I understood that when love and joy are shared, move intense they become, and that grief and hardship are a lesser burden when faced with clarity and trust.Loving you makes me feel safer and more alive. Bring me the courage to search, in purest spring, the water that will quench our trust, the strength to reach for the ripest fruit that insisted in growing in the highest branch, energy to overcome each and every obstacle and to have a forever open chest and a willing heart to keep you warm, body and soul, always.I will always be aware of this love and a constant readiness to review this feeling is a promise, of a truthful worship I have towards you.Have absolute certainty that my biggest fulfillment is knowing that I can make you the happiest woman and the most beloved in this earth, because I dedicate my seconds to this goal.Receive this with all my love!

Since the first instant

Experiencing Personality Insight as a service







PersonalityTraits






You are social and sentimental.

You are appreciative of art: you enjoy beauty and seek out creative experiences. You are emotionally aware: you are aware of your feelings and how to express them. And you are empathetic: you feel what others feel and are compassionate towards them.

Your choices are driven by a desire for modernity.

You consider both independence and taking pleasure in life to guide a large part of what you do. You like to set your own goals to decide how to best achieve them. And you are highly motivated to enjoy life to its fullest.



Summary of the Personality






You weren’t honest with me

I don’t want you to think that I am writing to ask you to reconsider and come back to me. Nor that I ever wished it would happen some day. Because of the way you did things, you would never deserve my trust again.This letter has just one purpose: to ask you to examine your conscience carefully and assess if the way you behaved is really worthy of someone who calls himself a man of truth. In my view, true men do not act as childish and with such hypocrisy as you did, and would not throw away all this time (as you’ve called it so many times) of love.Tell me something: were the things you said to me and all the affection you devoted me nothing but lies? Or are you so childish to the point of not knowing what you really want? Listen, time is passing by and you are not a kid anymore… be careful, you hear? People like you don’t usually manage it, they usually end up alone and miserable, be sure of that.I think that you should show a little respect for others, especially those you’ve shared moments of intimacy. Life, be it yours or others, is not a game. So, I really hope that you give what you did a good thought. And after having done that, I hope you star planning well your next steps, so that you life doesn’t turn into a big succession of mistakes.!









PersonalityTraits






You are boisterous, unpretentious and can be perceived as dependent.

You are assertive: you tend to speak up and take charge of situations, and you are comfortable leading groups. You are sociable: you enjoy being in the company of others. And you are intermittent: you have a hard time sticking with difficult tasks for a long period of time.

Your choices are driven by a desire for discovery.

You consider taking pleasure in life to guide a large part of what you do: you are highly motivated to enjoy life to its fullest. You are relatively unconcerned with tradition: you care more about making your own path than following what others have done.



Summary of the Personality







specific groups

ListeningListening




Network Topology









Age Detection







Co

mp

lex ity


Techniques

Capabilities






Big Data Applications11 – Social Analytics

Advanced Techniques (part c)




@pieroleo www.linkedin.com/in/pieroleoImages, Imanges, Images... Images

Images Followers of a Brand





Extracts Consumer Attributes from Images and Videos





69%13%

7.8%

3.8% 3.1%

2.4%

Travel & SceneryGoing out

Sports interests

Shopping

60%6.1%

1.8%

1.6%

MultimediaAnalytics

SkyScenery

Rural Scenery

Urban Scenery

Water Scenery

Performance

Zoo

Sport venue

Parade

Outdoor Market

Indoor Store

24%

1.5%

Travel & Scenery

LeisureScenery

Airplane - 12.5%

Blue sky - 8.9%

Sunset - 2.4%

Fireworks – 0,5

To

p Travel &

Scen

eryT

op S

cen

eryT

op L

eisure

Source: IBM Visual Analytics

Analytics to extract insights from images and videos

BrandFollowers





Examples of Semantic classifiers for images and video

Automatic recognition of sports and activity categories




@pieroleo www.linkedin.com/in/pieroleoCustomer Visual Attributes:Spans Multiple Facets and Complements TraditionalData Sources




@pieroleo www.linkedin.com/in/pieroleoBig Data enabled doctors from University of Ontario to apply neonatal infant monitoring to predict infection in ICU 24 hours in

advance

Performing real-time analytics using physiological data from neonatal babies

Continuously correlates data from medical monitors to detect subtle changes and alert hospital staff sooner

Early warning gives caregivers the ability to proactively deal with complications

“Customer Analytics” in

some Industry means safe life





Big Data Applications12 – Deep Dive on a Social

Analytics Project









Brand ClusterAcquiredAcquired Emerging Revenue Innvation Ready for IPO IPO





A week in a Shoppingwindow

InterviewsInterviewsInterviews

Expert/SMEs Invoved

Isolated and extracted around 200 “key concepts”





ThemesTechnology, Internationalization, e-commerce, Fashion & Art, Sharing Economy, Sustainability, Novelties, Materials, Colors, Traditional Shopping Spaces, Styles, Celebrities, Events









Content shareAcquired

Acquired

Emerging

Revenue

Innvation

Ready for IPO

IPO





Season EffectJeans and hand bags dominate discussions





“Associations”

Acquired

Emerging

Revenue

Innvation

Ready for IPO

IPO

Brands into the “acquired” cluster have a stronger associationsWith the Sustainability theme, Emerging brands look at foreign markets





Made in Italy?





Sustainability!

This theme emerged among others as one of the main contributors to increase brand reputation

7% of the Italian comments were referring to a

“Sustainability”

Acquired

Emerging

Revenue

Ready for IPO

IPO





(positive comments in green, negative in red)

Sentiment & Fashion

Fashion & Art, e-commerce, Sustainability, Technology,

Novelties, Materials, Styles, Traditional Shopping Spaces, Sharing EconomyColorsCelebrities, Internationalization, Events





Celebrities Opportunistics E-Commerce Official Brands Magazines Fashion Bloggers Others

Influencers











Psycho-Profile of Individuals

Individual’s network potential

Enterprise Customer Data

Enhanced digital profiles of individuals to tailor and time messages and offers

via the preferred channel

Multi-dimensional analytics of individuals

+Augment

Personality Insights





Who are your followers?





versus





versus





versus





Beyond Big Data13 – Cognitive Computing





What's NEXT?We could manage new complexity of digital transformation





Programmable Systems Era

Tabulating Systems Era

Co

mp

ute

r In

telli

gen

ce

1900

Cognitive Systems Era

Cognitive: of/or pertaining to the mental processes perception, memory, judgment, learning and reasoning

1950 Nowdays

Big Data

Systems of Insight

Big Data is just the starting point of a new era of computing. . .





Big Data enables us to see with new eyes....Salvador Dalì - Impresiones de África y Afgano invisible con aparición sobre la playa del rostro de García Lorca en forma de frutero con tres higos, 1938





...but you need your ANALYTICS & COGNITIVE abilities to benefit from them

Salvador Dalì - Impresiones de África y Afgano invisible con aparición sobre la playa del rostro de García Lorca en forma de frutero con tres higos, 1938

Head / HillMuzzel / River

Collar / Bridge

Fruit Bowl / Waterfall

Table / Beach

Nose-Mouth / Back Woman

Hair / Fruit / Dog Back

Eye / Shell





Perception:understand the world as we do: it interprets sensory input beyond traditional data

Reasoning:think through complex problems: it deepens our analysis and inspires creativity

Relating:understand how we communicate, and personalizes its interactions with each of us

Learning:learn from every interaction, scaling our ability to build experience

162

Understands

Language

Generates andevaluates hypotheses

Adaptsand learns

Cognitive Computing can fuel digital transformation

Dimensions we need





IBM Deep Blue, 1997 IBM Watson, 2011





Question

Answer & Confidence

Watson

What is Watson?

An Open-Domain question-answering (QA)

system beat the two highest ranked players in a

nationally televised two-game Jeopardy!





The Jeopardy! Challenge: 5 Key Dimensions to drive Question Answering

Broad/Open Domain

Broad/Open Domain

Complex LanguageComplex Language

High Precision

High Precision

Accurate ConfidenceAccurate

Confidence

High SpeedHigh Speed

$600In cell division, mitosis

splits the nucleus & cytokinesis splits this liquid cushioning the

nucleus

$600In cell division, mitosis

splits the nucleus & cytokinesis splits this liquid cushioning the

nucleus

$200If you're standing, it's the direction you should look

to check out the wainscoting.

$200If you're standing, it's the direction you should look

to check out the wainscoting.

$2000Of the 4 countries in the world that the U.S. does

not have diplomatic relations with, the one

that’s farthest north

$2000Of the 4 countries in the world that the U.S. does

not have diplomatic relations with, the one

that’s farthest north

$1000The first person

mentioned by name in ‘The Man in the Iron

Mask’ is this hero of a previous book by the

same author.

$1000The first person

mentioned by name in ‘The Man in the Iron

Mask’ is this hero of a previous book by the

same author.

What is down?Who is

D’Artagnan?

What is cytoplasm?

What is North Korea?

Start





Video 1





•Power every process •Fuel every interaction•Drive every decision

Systems of Engagement

Systems of Insight Systems

of Record

#DataEconomy and #InsightEconomy

From a process-centric to an insight-centric organizations

Analytics has evolved from a business initiative to a business imperative





What is our revenue by country? What products are selling best?

Clarity as to where an organization stands related to defined business measures

Descriptive What will be our revenue for Q4? What combination of products will sell best?

Analyze current and historical data to predict future events and business outcome

Predictive

Prescriptive

Cognitive

In order to foster a certain product to sell, we need to promote through

15% discounts.Take advantage of a future opportunity or risk and show the implication of each decision option

What is driving our revenue? Answer: X & Y are driving revenue and here are three identified areas to help future growth.

The system suggests a refined recommendation to a question with a ranked confidence level based on interactions with end users.

System of Insight analytics methods are evolving

168

Systems of Insight

Thomas H. Davenport, 2007

https://hbr.org/2013/12/analytics-30https://hbr.org/2006/01/competing-on-analytics





Beyond Big Data14 – How IBM Watson works





….English Slot Grammar parserPredicate-Argument StructureNamed entity recognizerEntity disambiguation and matchingCo-reference resolutionRule-based relation extractionStatistical relation detectioHidden associations and implicit relationships identificationClassificationRule-based Pattern-MatchingSource AcquisitionSource TransformationSource ExtensionKnowledge-base inductionDocument SearchPassage SearchCandidate Answer GenerationAnswer LookupStructured SearchGame strategy (Simulation, learning, andoptimization techniques)….100 different analytic componentsUIMA-AS (Asynchronous Scaleout)400 processes deployed across 71 IBM POWER 750 – 32CPU (2,300 CPU)….

Question

Answer & Confidence

Watson

Technologies behind IBM Watson challenge





Video 2





Informed Decision Making: Search vs. Expert Q&A

Decision Maker

Search Engine

Finds Documents containing KeywordsFinds Documents containing Keywords

Delivers Documents based on PopularityDelivers Documents based on Popularity

Has QuestionHas Question

Distills to 2-3 KeywordsDistills to 2-3 Keywords

Reads Documents, Finds Answers

Reads Documents, Finds Answers

Finds & Analyzes EvidenceFinds & Analyzes EvidenceExpert

Understands QuestionUnderstands Question

Produces Possible Answers & EvidenceProduces Possible Answers & Evidence

Delivers Response, Evidence & ConfidenceDelivers Response, Evidence & Confidence

Analyzes Evidence, Computes ConfidenceAnalyzes Evidence, Computes Confidence

Asks NL QuestionAsks NL Question

Considers Answer & EvidenceConsiders Answer & Evidence

Decision Maker





More than keyword match …

celebrated

India

In May 1898

400th anniversary

arrival in

Portugal

India

In May

Garyexplorer

celebrated

anniversary

in Portugal

Keyword MatchingKeyword Matching





arrived in

In May, Gary arrived in India after he celebrated his anniversary in Portugal.

In May 1898 Portugal celebrated the 400th anniversary of this explorer’s arrival in India.

Evidence suggests “Gary” is the answer BUT the system must learn that keyword matching may be weak relative to other types of evidence





On 27th May 1498, Vasco da Gama landed in Kappad Beach


celebrated

May 1898 400th anniversary

arrival in

In May 1898 Portugal celebrated the 400th anniversary of this explorer’s arrival in India

Portugal

landed in

27th May 1498

Vasco da Gama

Temporal ReasoningTemporal

Reasoning

Statistical Paraphrasing

Statistical Paraphrasing

GeoSpatial ReasoningGeoSpatial Reasoning

explorer


On the 27th of May 1498, Vasco da Gama landed in Kappad Beach

Kappad Beach

Para-phrase

s

Geo-KB

DateMath

India

Stronger evidence can be much harder to find and score

The evidence is still not 100% certain

Search Far and Wide

Explore many hypotheses

Find Judge Evidence

Many inference algorithms

Why Semantics? Deeper Evidence





Popularity is not the only way to go …

Clue: Chile shares its longest land border with this country.Clue: Chile shares its longest land border with this country.

Positive EvidencePositive Evidence

Negative EvidenceNegative Evidence

Bolivia is more Popular due to a commonly discussed border dispute. But Watson learns that Argentina has better evidence.





In 2007, we committed to making a Huge Leap!

What It Takes to compete against Top Human Jeopardy!TM Players

Winning Human Performance

Winning Human Performance

2007 QA Computer System2007 QA Computer System

Grand Champion Human Performance

Grand Champion Human Performance

Each dot – actual historical human Jeopardy! gamesEach dot – actual historical human Jeopardy! games

More ConfidentMore Confident Less ConfidentLess Confident





Baseline

12/2007

8/2008

5/2009

10/2009

11/2010

12/2008

Compare Experiments

5/2008

4/2010

Pre

cisi

on




@pieroleo www.linkedin.com/in/pieroleoDeepQA: Technology Behind WatsonMassively Parallel Probabilistic Evidence-Based Architecture over structured and unstructured data

. . .

Answer Scoring

Models

Answer & Confidence

Question

Evidence Sources

Models

Models

Models

Models

ModelsPrimarySearch

CandidateAnswer

Generation

HypothesisGeneration

Hypothesis and Evidence Scoring

Final Confidence Merging & Ranking

Synthesis

Answer Sources

Question & Topic

Analysis

QuestionDecomposition

EvidenceRetrieval

Deep Evidence Scoring

HypothesisGeneration

Hypothesis and Evidence Scoring

Learned Modelshelp combine and

weigh the Evidence

DeepQA uses an extensible collection of Natural Language Processing, Machine Learning, Information Retrieval and Reasoning Algorithms





Question

Answer & Confidence

Watson


http://clic.humnet.unipi.it





Question

Answer & Confidence

Watson






2004 2012

1. http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5386742

2. http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6177717

Unstructured Information Management

2013

3. http://www.amazon.com/Smart-Machines-Cognitive-Computing-Publishing/dp/023116856X

Referece Materials

Before Watson After





Beyond Big Data15 – Cognitive Computing at

Work




@pieroleo www.linkedin.com/in/pieroleoPutting Watson at work to address the world’s pressing issues

R&D

Demonstration

Commercialization

Cross-industry Applications

IBMResearch Project (2006 – )

Jeopardy!Grand

Challenge(Feb 2011)

Watson for

Healthcare(Aug 2011 –)

Watson Family

(2012 – )

Watson for Financial

Services(Mar 2012 – )

Expansion





© 2014 International Business Machines Corporation

Transforming industries and professions

Contact Center

Healthcare Financial Services

Government

Diagnostic/treatment assistance, evidenced-based insights, collaborative medicine

Investment and retirement planning, institutional trading and decision support

Call center and tech support, enterprise knowledge management, consumer insight

Public safety, improved information sharing, security

RetailThe shopping experience, Merchandising and supply networks, Sales operations

Accelerated Research

Research Assistant, information collection, filtering and new insights generation





# OF USERS

“Establish”Bespoke engagements

“Extend” High volume

“Embed”Massive volume

IBM Watson Family: Products, Offerings & Solutions

Watson EcosystemWatson

Engagement AdvisorWatson

Oncology Advisor

SC

AL

E

10s1,000s

1,000,000s

Big Data Analytics Stack

Watson Foundations & Products

WatsonDiscovery Advisor

Watson Emerging Technology

Watson Explorer Watson Developer Cloud Watson Analytics





# OF USERS






Oncology Advisor

SC

AL

E

10s1,000s

1,000,000s



Watson Emerging Technology General: (Watson Chef – Psycolinguistic Analysis) – H&L: (Clinical Trial

Matching – Clinical Paths)

Automates customer question & answer interaction to increase customer engagement

Enables anyone to uncover visual answers in their data through natural language

Enables physicians to make evidence-based treatment decisions to improve care

Enables analysts to investigate the tough problems that have never been answered before

Helps organizations discover, understand & virtually integrate their data into a unified view

Allowing direct developer participation in the era of cognitive systems

The Watson Ecosystem empowers development of “Powered by IBM Watson” applications.

Watson Explorer(+ Adv Edition WCA)

Watson Developer Cloud Watson Analytics






Beyond Big Data16 – Cognitive Advisors





# OF USERS






Oncology Advisor

SC

AL

E

10s1,000s

1,000,000s



















Oncologist Chef CustomerAgent BiologyResearcher





Challenges

medical knowledge is doubling every 5 years.

deaths associated with preventable harm to patients.just n US

physicians spend <5 hours per month reading medical journals

81%

400.000+

5 years

is the potential research space size for looking for ideas for new recipes by

combining available ingredients

1023

order of magnitude of the number of recipes listedin the largest recipe repositories (e.g.

http://cookpad.com, 1.5M).

106

new scientific research papers published every year

1.000.000+

for a promising pharmaceutical treatment to progress from the initial research stage into

practice

10-15 years

clinical trials are ongoing just at Mayo Clinic only

3-5% of patients are involved

8.000

calls made annually to call center costing $600B

10x

270B

4.6%

spent by loyal customers over their lifetime

market value gain from a single point customer sat gain

Oncologist Chef

CustomerAgent BiologyResearcher



http://cookpad.com/



Published KnowledgePublished Knowledge

Knowledge-Driven Method Data-Driven Method

Observational Data

Observational Data

• Longitudinal records• Claims, Rx, Labs• Patient reported data

• Scientific papers• Books• Guidelines

Closing the translational knowledge gap Personalized Insights from institutional data

From population averages … To insights for individual patient!

Watson for healthcare and life sciences spans all aspects of knowledge and data





Helps oncologists make better, more personalized treatment decisions by ranking treatment plans based on national guidelines, published literature, and expert insight

newOncologistVideo 3





Enables researchers to connect DOTS in large research data sets: in biosciences, uncover new insights into relationships between genes, proteins, pathways, phenotypes and diseases

newResearcherAccelerating drug discovery and development through supporting:•Target Identification and validation•Compound Evaluation and Optimization•Safety & Toxicology Predictive Analysis•Drug Repurposing / Competitive Intelligence

Source: http://www.youtube.com/watch?v=qry_zGZFjOc Video 5





Helps direct customer self-service as well as customer agents with clients by personalized responses to questions and give users actionable insight with supporting evidence and confidence to help create the experiences customers expect.

newCustomerAgent

http://www.youtube.com/watch?v=lPgp4A1vxls

Video 6 Video 6b

Banking Assistant Sales Assistant





Beyond Big Data17 – A Cognitive Ecosystem





# OF USERS






Oncology Advisor

SC

AL

E

10s1,000s

1,000,000s



















Delivering the cognitive experience to the masses

engaged innovators million equity investments

subject matter experts

Watson Developer

Cloud

Watson Content

Store

Watson TalentHub

+ +

4000+ 500+$100

© 2014 International Business Machines Corporation 197





Application Partner

Talent Partner

Content Partner

Watson Content Store

Watson Developer Cloud

Watson Platform & Tools

Enhance client experience

Watson Ecosystem: opening the platform to the World Creativity





Three examples of our application partners

Guided selling powered by Watson is a solution that allows shoppers to find the ideal product through the use of a non-linear, user-driven conversation that provides personalized responses using several data inputs

Powered by Watson solution that allows medical institutions to make informed medical device procurement decisions quickly based on comprehensive, evidence-based analysis of unbiased information

The solution will provide intelligent interactions with individuals regarding healthcare prevention and wellness via a Watson dialog powered by the Welltok Eco-System “Corpus”

Speed, flexibility, and cost savings without Watson-based solution, work must be done via a manual process (i.e. consulting), which is by nature slow, subject to biases, and prohibitively expensive for many hospitals

Scalability with this solution, MD Buyline can scale offering type to totally new customer segments (i.e. students)

The world without this Watson-powered app is impersonal and driven by the choices retailers make, not the consumers

The solution could totally disrupt how consumers make product decisions by giving them access to a digital conversation on their questions

Health Care Providers want to increase brand affinity and decrease member attrition by increasing engagement

Health care consumers and providers want to reduce health care costs, increase focus on preventive care but need to find a way to engage consumers, create incentives and change behavior

Ap

p g

oal

Ch

alle

ng

e ad

dre

ssed

Video 6





Example of Watson Ecosystem Companies

As a CLIA and CAP accredited global clinical laboratory and forward-thinking healthcare company.

Provide physicians and patients with an array of actionable genetic tests that can identify a person’s genetic risk for cancer, cardiac conditions, inherited diseases, nutrition and exercise response, and drug response for medications, specifically those used in pain management and mental health.

A consumer will be able to ask the Pathway Panorama app questions

based on their DNA, like:“How much exercise should I do today?”“How much coffee can I drink on

Monday?” The cognitive app answers and provides options based on the millions of healthcare-related evidence-based data, provided by Pathway Genomics, ingested by Watson and on the individual’s biomarker, vital signs (wearables), DNA, electronic health records, and other information.

- 6 years company- $80 million funded start-up- 12 top company from Inc 500





Video 1Video 3

CogniToys





Beyond Big Data18 – Watson Developer Cloud





# OF USERS






Oncology Advisor

SC

AL

E

10s1,000s

1,000,000s



















IBM Watson Services

Source: https://console.ng.bluemix.net/home





Beyond Big Data19 – Computational Creativity





# OF USERS






Oncology Advisor

SC

AL

E

10s1,000s

1,000,000s



















Source: AARON, Harold Cohen - http://www.aaronshome.com/aaron/index.html

AARON: Computational Creativity Example





IBM Chef Watson.

Inspire your recipes with Cognitive Cooking

Cognitive Cooking

208

Cognitive Computing approach to Computational Creativity





Food Knowledge Database

Combinatorial Designer

Cognitive Assessor

Dynamic Planner

Peer Produced Inspiration Set

Novel Customized Recipe

Cognitive Cooking System

209

How does Cognitive Cooking work?

Raw Data- Recipes- Recipes contexts- Chemical/Flavour Data- Hedonic psychophysics- Background knowledge (e.g. Wikipedia for regional cuisines, etc)...

- Bayesian surprise- Flavor Pleasantness...

Data-drivenDecisions

106 >1015-23





Stimulates creativity by helping chefs to combine ingredients, styles and invent recipes and produce new dishes

newChef

Ingredients: potato, watercress, scallion, ginger, black peppercorns, vegetable oil, canola oil, oregano, thyme, buttermilk, dark brown sugar, mayonnaise

This Potato Salad dish is the result of the combined efforts of IBM Watson's Cognitive Cooking program and Bon Appétit (chef) readers.

Source: http://www.youtube.com/watch?v=mr-1JAnairs Video 6





211





212





213





214





215





216





217

Seasoning/Spice

Cheese

Vegetable

Cured Meat

Spicy Vegetable

Cured Meat

Oil/Fat

Egg Product

Meat

Dash chipotieCoriander seed

MozzarellaGoat cheese

AvocadoCornBacon

African bird pepper

Coconut oil

Egg

Chicken breastChicken breast

Ground black peppercornsWhole fennel seed

CheddarParmesan cheese

AvocadoStalk celery

Bacon

Piquillo peppers

Vegetable oil

Egg

African Chicken Frittata

Classic

Unique





Beyond Big Data20 – Search, Deep Analytics &

Mining





# OF USERS






Oncology Advisor

SC

AL

E

10s1,000s

1,000,000s












Watson Explorer(+ Adv Edition )







© 2014 International Business Machines Corporation

Watson Explorer

A visualization exploration engine to help people understand what’s intheir data

220

• Find, extract and deliver content regardless of format or where the data resides

• Helps improve the return on all types of information including:

• Structured data

• Unstructured content

• Semi-structured

MultiviewNational Library of MedicineKunnskapssebteret





221

Highly relevant, personalized

results

Access across many sources

Dynamic categorization

Leveraging Structured and

unstructured content

Enhancedby social

collaboration

Organize contentinto virtual folders

Refinements basedon structuredinformation

221

Expertise location

Watson Explorer Front-End




@pieroleo www.linkedin.com/in/pieroleoFusion of data from multiple systems enables deeper insights—not just facts

© 2013 IBM Corporation

222

WikiExperts

Social Media

Fulfillment

Support Ticketin

g

External Sources

CRM

Supply Chain

Email

Content Mgt.

DBMS

Fusion of data from multiple systems enables deeper insights—not just facts

Who is best able to help this customer?

What is her view of our company?

Where else has she worked?

Who is this customer?

What is available inventory?

How is her company using our products?

What products has she purchased?

What issues has this customer had in the past?





IBM Big Data & Analytics Overview

Deep Text Analytics and MiningBusiness Intelligence on unstructured data

Fehlerbericht vom10.08.2013Autor: Peter Müller

Ich habe Bremsprobleme mit meinem Toyota Prius.

Beim Fahren über ein großes Schlagloch hat die Bremse 2 Sekunden nicht funktioniert.

Fehlerbericht vom10.08.2013Autor: Peter Müller

Ich habe Bremsprobleme mit meinem Toyota Prius.

Beim Fahren über ein großes Schlagloch hat die Bremse 2 Sekunden nicht funktioniert.

DocType Fehlerbericht

Date 10.08.2013

Author Peter Müller

Brand Toyota

Model Prius

Component Bremse

Problem 1 funktioniert nicht

Trigger Schlagloch

Activity Fahren

Duration 2 Sekunden

Adjekcive groß

Verbs fahren, funktionieren

visualize

„Ma

chine

rea

d“





Document Analysis Facets

Time Series

Deviations / Trends

Dashboard

Facet PairsConnections

Sentiment

© 2014 International Business Machines Corporation - IBM Confidential

Examples of analytics component capabilities





Beyond Big Data21 – Analytics for ALL!





# OF USERS






Oncology Advisor

SC

AL

E

10s1,000s

1,000,000s












Watson Explorer(+ Adv Edition )







227

Visual Analytics Example: Understands the language of business

Visual, simple and intuitive

Simply type in a question and get

meaningful insights

immediately

Visual, simple and intuitive

Automatically suggests graphs and

visuals to communicate

findings

INSIGHTContext

Automatically presents related

facts and insights to guide discovery

insight

insight

insight

insight

insight

insight

insight

You and your business data

IBM Watson Analytics https://www.analyticszone.com/homepage/web/displayNeoPage.action





Credits: Dashon Goldson Gallery

TOUCHDOWN!





RUSHING TD

FUMBLES

PASSING TD

1

2

3













SEGMENTO 2









SEGMENTO 2









SEGMENTO 3





IBM Watson Analytics

Watson Analytics

Communication & Collaboration

Visualization & Storytelling

AnalyticsDescriptive, Diagnostic, Predictive, Prescriptive, Cognitive

Data Access & Refinement

CloudCloud

Operations

HR

ITFinance

Sales

Marketing

Mobile ReadyMobile Ready SecureSecure

Value:•Put analytics in the hands of everyone•Make access to data easy for refinement and use •Deliver through the cloud for agility and speed

PrioritizingAccounts

Receivable

Identifying andRetaining Key

Employees

HelpdeskCase

Analysis

CampaignPlanning and ROI

WarrantyAnalysis

Customer Retention

Finance HRITMarketing OperationsSalesExamles

Experience at www.watsonanalytics.com





Beyond Big Data22 – Examples of advanced

cognitive research areas





Transform ing the way we see

Medical Sieve – smart decision support system for radiologists. Performs visual anomaly identification and diagnostic analysis on X-rays, MRIs, PET and CAT scans, sonograms, and echo-cardiograms(http://researcher.ibm.com/researcher/view_project.php?id=4384)



http://researcher.ibm.com/researcher/view_project.php?id=4384





Advanced Interaction and Reasoning

Video 8





Building a Society of Cognitive Agents

Watson

Cognitive Agent to

Agent

Outage Model

Consequence Table

Smart Swaps

Lighting

Critical Sites

Objective Identificatio

n

Sensitivity

Analysis

Sentiment Analysis

Systems of cognitive agents that collaborate effectively with one another

Cognitive agents that collaborate effectively with people through natural user interfaces

A nucleus from which an internet-scale cognitive computing cloud can be built

Personal Avatar

Deep Thunder

Crew Scheduler

News

Human to Human

Cognitive Agent to Human

Video 9





Build a Neurosynaptic chip

A new chip with a brain-inspired computer architecture.It is the largest chip IBM has ever built at 5.4 billion transistors, and has an on-chip network of 4,096 neurosynaptic cores. It only consumes 70 milliwatts real-time operation — orders of magnitude less energy than traditional chips

http://www.research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=shXA9deOPD0

Video 10



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