Extended deck around data phenomena from (big)data to Extended deck around data and digital evolution phenomena from (big)data to cognititive computingcomputing

@pieroleo www.linkedin.com/in/pieroleo
From Managing
(Big)Data to Manage
Cogs

Module 1: Big Data
1 – Technological Factors
2 – Big Data Metaphors & IT Paradigm Shifts
3 – Business Factors
4 – Big Data Applications
5 – Big Data IT Perspective
6 – Human Factor!
7 – Mining unstructured and non conventional data
Module 2: Big Data Applications
8 – Customer Analytics
9 – Capitalizing On Social Media Data Today
10 – Exploring an Enterprise Social Analytics Enviroment
11 – Social Analytics
12 – Deep Dive on a Social Analytics Project
Module 3: Beyond Big Data
13 – Cognitive Computing
14 – How IBM Watson works
15 – Cognitive Computing at Work
16 – Cognitive Advisors
17 – A Cognitive Ecosystem
18 – Watson Developer Cloud
19 – Computational Creativity
20 – Search, Deep Analytics & Mining
21 – Analytics for ALL!
22 – Examples of advanced cognitive research areas
Topics
From Managing
(Big)Data to Manage
Cogs

DATA

DATAis the new basis of
competitive advantage.....
.......and the engine of
Digital Transformation

CAMSS Data as a Gravity New natural
resource
New business
models
Human Factor
Big Data and IT Text Analytics MultiMedia
Analytics

Capitalizing
On Social Media
Customer Analytics
Techniques Social Analytics
Cognitive
Computing
Cognitive AdvisorsIBM Watson Watson Ecosystem
Customer Analytics

Big Data
1 – Technological Factors

@pieroleo www.linkedin.com/in/pieroleoMagritte
Manet Dal Monte
Leonardo

Magritte
Manet Dal Monte
Leonardo
CLOUD ANALYTICS
SOCIAL MOBILE
of individuals and
organizations

Magritte
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 Data
2 – 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
Social
Data from and about People
Physical
Sensors & 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
Data
Content
>80%
<20%
Traditional
Enterprise Data
Big data embodies
new data characteristics created
by today’s digitized marketplace
Biological
DNA Sequencers

20 20
GlobalDataVolumeinExabytes
Sensors
(InternetofThings)
Multiple sources: IDC,Cisco
100
90
80
70
60
50
40
30
20
10
AggregateUncertainty%
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.

Paradigm shifts enabled by big data
and analytics
TRADITIONAL APPROACH
Analyze small subsets
of information
Analyzed
information
All
available
information
BIG DATA & ANALYTICS APPROACH
Analyze
all information
All
available
information
analyzed
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 and
test against selected data
Explore all data and
identify 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
Second
Income!

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-1411970203
Source: 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

Source: A statue representing Janus Bifrons in the Vatican Museums
Big Data as a new Business Concept and as a new
Technology Concept

30
Big Data as a new business concept:
New values and opportunities for a number of
stakeholders
Chief Marketing Officer
how 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 Systems
System Logs
Application logs: web, vending machines,
mobile
Video
Sensor Networks, RFID
• …
Social Media:
• Global Social Networks: tweeter,
facebook, etc.
• Small communities: blogs, muros
corporativos,
• Internal Social Networks
(employees)
• News
• …
Big
Data
Analytics
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 Data
4 – Big Data Applications

Utilities
Weather impact analysis on
power generation
Transmission monitoring
Smart grid management
Retail
360° View of the Customer
Click-stream analysis
Real-time promotions
Law Enforcement
Real-time multimodal surveillance
Situational awareness
Cyber security detection
Transportation
Weather and traffic
impact on logistics and
fuel consumption
Traffic congestion
Financial Services
Fraud detection
Risk management
Telematics
IT
System log analysis
Cybersecurity
Telecommunications
CDR processing
Churn prediction
Geomapping / marketing
Network monitoring
What can you
do with Big Data?
Health & Life Sciences
Epidemic early warning
ICU monitoring
Remote 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 = 1061
Totals 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 data
Infrastructure
Big data
Sources
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
outcomes
Operational
optimization
Risk / 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 Data
5 – Big Data IT Perspective

Data Warehouse
Operational Analytics
Structured, analytical, logical
Big Data
Ad Hoc Analytics
Creative, holistic thought, intuition
Big Data is augmenting
traditional IT investments
Hadoop &
Streaming
Data
New
Sources
Unstructured
Exploratory
Iterative
Structured
Repeatable
Linear
Data
Warehouse
Traditional
Sources
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
Transformation
Landing,
Exploration
& Archive
Landing,
Exploration
& Archive
Enterprise
Warehouse
Enterprise
Warehouse
Data MartsData Marts
Real-Time AnalyticsReal-Time Analytics
Data
Sources
Structured
Operational
Unstructured
External
Social
Sensor
Geospatial
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/Audio
Network/Sensor
Entity Analytics
Predictive
Q&R, OLAP
Deep Analytics
Predictive
High
Performace
Analytics
High
Performace
Query
ETL, Data Quality
Auditing, De-identification
Cognitive
Advisors
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
Transformation
Transformation
Landing,
Exploration
& Archive
Landing,
Exploration
& Archive
Enterprise
Warehouse
Enterprise
Warehouse
Data MartsData Marts
Real-Time AnalyticsReal-Time Analytics
Data
Sources
Structured
Operational
Unstructured
External
Social
Sensor
Geospatial
Time Series
Streaming
BI & Performance
Management
Predictive Analytics
& Modeling
Exploration &
Discovery
Actionable
Insights
Cognitive
Advisors
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
Visualizatio
n
Domain
Expertise TOM
Hacker
Mindset
Math
Data
Engineering
Advanced
Computing
Statistics
Data
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 Data
6 – 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 #Security
plus #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 Data
7 – 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
DataVolume
2000’s 2010’s
Structured data
Audio
Image
Med
High
Low
ComputationalNeeds
SophisticationofAnalysis
Expressiveness
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 extraction
Relationship extraction
Sentiment Analysis
Linguistic Analysis
Categorization
Clustering,
Text Analytics
Tasks
Document
Summarization
….

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

A beautiful Vacation!
Checco
Greta
http://visual-recognition-demo.mybluemix.net/

An Example of a Multimedia
Analytics Environment
http://mp7.watson.ibm.com/imars/

Multimedia 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
Color
Spectrum
Edges
Camera
Motion
Feature Extraction
Ensemble
Classifiers
Texture
Active
Learning
Deep
Belief Nets
Vehicle tracking Activity classificationSafe zone monitoring
Locations Activitie
sScenes
Safety/Security
Behaviors
Objects
PeopleEvents
Tracks
Moving
Objects
Actions
Neural
Net
classification
scoringSemantics
Multimedia
AdaBoost
Blobs
Background
Segmentation
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 appearances
in the same frames

Identify distorted logos on clothing
and promotional items
Many enterprises are investigating
next generation multimedia
analytics-based solutions

Big Data Applications

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 RecordSystems of
Engagement
CustomerCustomer
AnalyticsAnalytics
Big Data Analytics

69
All perspectives
Past (historical, aggregated)
Present (real-time,
scenarios)
Future (predictive,
prescriptive)
At the point
of impact
All decisions
Major and minor;
Strategic and tactical;
Routine and exceptions;
Manual and automated
All information
Transaction/POS data
Social data
Click streams
Surveys
Enterprise content
External data (competitive,
environmental, etc.)
All people
All departments
Front line, back office
Executives, managers
Employees
Suppliers, customers and
consumers
Partners 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 Analytics
Practical CHALLENGES

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

SINGLE VIEW
Business Data,
Social Data,
Interactive data
360°Integrated
Customer View
Marketing
Cust. Care
Sales
Risk, Fraud
… and more

SINGLE VIEW
Business Data,
Social Data,
Interactive data
360°Integrated
Customer View
Marketing
Cust. Care
Sales
Risk, Fraud
How?How?Why?Why?
Who?Who? What?What?
… and more

Monitoring
and Reporting
Analytics of Aggregates
Analytics of Individuals &
specific groups
ListeningListening
EngagementEngagement
DemographicsDemographics
PublishingPublishing
Measurement 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 Tagging
Gender Recognition
Identity Recognition
How do people feel
about my brand?
Complexity
Techniques
CapabilitiesCognos - Big Insights – SMA - SPSS –
Watson Explorer – Adv. Analytics & Cognitive Services
From CHALLENGES to Techniques
And Capabilities

CustomerAnalytics &
TRUST
“Trust men and they will be true
to you; treat them greatly and
they will show themselves
great.”
Ralph Waldo
Emerson

Consumers are open to share their
personal information, with the exception of
financial data, when there is perceived
benefit
Consumer Maintains Control of Data
What 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. Media
channels)
Demographic
(e.g. age,
ethnicity)
Identification
(name,
address)
Lifestyle (# of
cars, home
ownership)
Location
Based
Medical Financial
Completely Disagree Neutral Completely willing

9 – 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
Data
Sources
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
CapabilitiesBusinessOutcomes

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 manage
customers
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 Analytics
Techniques
A cura di: Pietro Leo

Utilities
Weather impact analysis on
power generation
Transmission monitoring
Smart grid management
Retail
Click-stream analysis
Real-time promotions
Law Enforcement
Real-time multimodal surveillance
Situational awareness
Cyber security detection
Transportation
Weather and traffic impact on
logistics and fuel consumption
- Traffic congestion
- 360° View of the Customer
Financial Services
Fraud detection
Risk management
IT
System log analysis
Cybersecurity
Telecommunications
CDR processing
Churn prediction
Geomapping / marketing
Network monitoring
- 360° View of the Customer
Mining unstructured and non conventional
data around “customers”
Health & Life Sciences
Epidemic early warning
ICU monitoring
Remote healthcare monitoring

10 – Exploring an Enterprise Social Analytics
Enviroment

Monitoring
and Reporting
specific groups
ListeningListening
Network Topology
Face Recognition
Visual Recognition
Age Detection
Image Tagging
Gender Recognition
How do people feel
about my brand?
Complexity
Cognos - Big Insights – SMA - SPSS –
Techniques
Capabilities
CustomerAnalytics

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 Relationships
Social Media Discovery
Social Media Segmentation
ARE 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

IBM 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

Segment 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 strategies
99

Identify 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
• 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
• Update model
• Introduce new features
101

IBM Social Analytics on Cloud
– Technical Architecture Overview
Data Sources Analysis Distribution
Deliver
y
Media
Stakeholders
Blogs, forums,News,
Communities
Social Media
Other Sources*
Client Supplied
Information
(sites, feeds)
Client Supplied
Information
(Databases)
Adhoc
analysis
Flat Files
Analytics
Engine
SMA/SPSS
SPSS Modeler
Glimpse
Sentiment
Analytics
Text
Analytics
Key
Influencer
Mapping
Affinity
Analytics
Event
Detection
Deep
Sentiment
Mining
Targeted
Influencer
Analytics
Unstructured
Entity Integration
Customer
Segmentation
Customer
Analytics
Social Media
Warehouse
IBM DB2
Reporting
Adhoc Reports
Interactive
Dashboards
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
REST
servic
e
Research Differentiating
Capabilities (DC)
Actionable
Insights

Advanced Techniques

Monitoring
and Reporting
specific groups
ListeningListening
Network Topology
Face Recognition
Visual Recognition
Age Detection
Image Tagging
Gender Recognition
How do people feel
about my brand?
Complexity
Cognos - Big Insights – SMA - SPSS –
Techniques
Capabilities
Customer Analytics

Text, text, text.... text

Extracts 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 announcements
Intent 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/fYReSj
I'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…

Identity Analytics
Models
Strong Weak
Big Match

109
AMEX Example: Business Models
based on connecting Virtual and Real Words model
American Express
Smart 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

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

Social Media
Consumer
Profiles
Social Media
Consumer
Profiles
Customer
Models
Customer
Models
Entity
Integration
Entity
Integration
Predictive
Analytics
Predictive
Analytics
Data Ingest
& prep.
Data Ingest
& prep.
Text Analytics:
Timely Insights
Text 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
Customer
Database
Customer
Database
Consumer
Lists
Consumer
Lists
Customer
& Prospect
profiles
Customer
& Prospect
profiles
Entity
Integration
Entity
Integration

116
© 2014 IBM Corporation116
C. Johnson
123 Main Street
512-545-1234
CRM
Supply
Chain
Fulfillment
Support
Ticketing
External
Sources
3rd
Party
Chris Johnston
123 Main Street
512-554-1234
Shipping:
456 Pine Ave
Christine. Johnson
123 Main Street
Call length
Semi-structured notes
Satisfaction
C. Johnson
Main Street
512-554-1234
C. Johnson
125 Main Street
512-554-1234
ChrisJohnson65
“Likes” Clothes,
Camping Gear @ChristyJohnson65 Christy65
Circle / Network data
Order
Mgmt.
Internal / Structured
External / Unstructured
Web
Chris.johnson@cj.net
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 Johnson
Married
1 child
4/15/74
Christy65
Mail Order responder
Specialty Apparel
Partner Sales data
VIP: Gold
Customer Sat: 80%
Influence Score: 8/10
IBM Internal Use Only

Advanced Techniques (part b)

Personality Insights from my Twitter Stream

Personality
traits
Values and
Needs
When I talk

121
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

122
http://your-celebrity-match.mybluemix.net/
Examples of Systems
that uses Personality Insights
http://usermodeling-ao15.mybluemix.net/systemu/home#findmymatch

123
Personality Insight as a service
http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/personality-
insights.html

124
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

125
Since
the
first
instant
as a service
Personality
Traits

126
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.
Since
the
first
instant
as a service
Summary of the
Personality

127
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.!
You
weren’t
honest
with me
as a service

128
You
weren’t
honest
with me
as a service
Personality
Traits

129
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.
You
weren’t
honest
with me
as a service
Summary of the
Personality

Advanced Techniques (part c)

@pieroleo www.linkedin.com/in/pieroleoImages, Imanges, Images... Images
Images Followers
of a Brand

133
Extracts Consumer
Attributes from Images and Videos

69%
13%
7.8%
3.8% 3.1%
2.4%
Travel & Scenery
Going 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
Leisure Scenery
Airplane - 12.5%
Blue sky - 8.9%
Sunset - 2.4%
Fireworks – 0,5
TopTravel&SceneryTopSceneryTopLeisure
Source: IBM Visual Analytics
Analytics to
extract insights
from images
and videos
Brand
Followers

135
Examples of Semantic
classifiers for images and video
Automatic
recognition of
sports and
activity
categories

136
Customer Visual Attributes:
Spans Multiple Facets and
Complements TraditionalData Sources

Big 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

12 – Deep Dive on a Social
Analytics Project

Brand Cluster
AcquiredAcquired Emerging Revenue Innvation Ready for IPO IPO

A week in a Shopping
window
InterviewsInterviewsInterviews
Expert/SMEs
Invoved
Isolated and
extracted
around 200
“key concepts”

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

Content share
Acquired
Acquired
Emerging
Revenue
Innvation
Ready for IPO
IPO

Season Effect
Jeans and hand bags dominate discussions

“Associations”
Acquired
Emerging
Revenue
Innvation
Ready for IPO
IPO
Brands into the “acquired” cluster have a stronger associations
With 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 Economy
Colors
Celebrities,
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

Beyond Big Data
13 – Cognitive Computing

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

Programmable
Systems Era
Tabulating
Systems Era
ComputerIntelligence
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. . .

160
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

161
...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 / Hill
Muzzel / 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
and
evaluates
hypotheses
Adapts
and 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
Language
Complex
Language
High
Precision
High
Precision
Accurate
Confidence
Accurate
Confidence
High SpeedHigh Speed
$600
In cell division, mitosis
splits the nucleus &
cytokinesis splits this
liquid cushioning the
nucleus
$600
In cell division, mitosis
splits the nucleus &
cytokinesis splits this
liquid cushioning the
nucleus
$200
If you're standing, it's the
direction you should look
to check out the
wainscoting.
$200
If you're standing, it's the
direction you should look
to check out the
wainscoting.
$2000
Of the 4 countries in the
world that the U.S. does
not have diplomatic
relations with, the one
that’s farthest north
$2000
Of the 4 countries in the
world that the U.S. does
not have diplomatic
relations with, the one
that’s farthest north
$1000
The first person
mentioned by name in
‘The Man in the Iron
Mask’ is this hero of a
previous book by the
same author.
$1000
The 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 Data
14 – How IBM Watson works

….
English Slot Grammar parser
Predicate-Argument Structure
Named entity recognizer
Entity disambiguation and matching
Co-reference resolution
Rule-based relation extraction
Statistical relation detectio
Hidden associations and implicit relationships identification
Classification
Rule-based Pattern-Matching
Source Acquisition
Source Transformation
Source Extension
Knowledge-base induction
Document Search
Passage Search
Candidate Answer Generation
Answer Lookup
Structured Search
Game strategy (Simulation, learning, and
optimization techniques)
….
100 different analytic components
UIMA-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 Evidence
Expert
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
Reasoning
Temporal
Reasoning
Statistical
Paraphrasing
Statistical
Paraphrasing
GeoSpatial
Reasoning
GeoSpatial
Reasoning
explorer
On the 27th
of May 1498, Vasco da
Gama landed in Kappad Beach
Kappad Beach
Para-
phrase
s
Geo-
KB
Date
Math
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
Precision

@pieroleo www.linkedin.com/in/pieroleoDeepQA: Technology Behind Watson
Massively Parallel Probabilistic Evidence-Based Architecture
over structured and unstructured data
. . .
Answer
Scoring
Models
Answer &
Confidence
Question
Evidence
Sources
Models
Models
Models
Models
ModelsPrimary
Search
Candidate
Answer
Generation
Hypothesis
Generation
Hypothesis and
Evidence Scoring
Final Confidence
Merging &
Ranking
Synthesis
Answer
Sources
Question &
Topic
Analysis
Question
Decomposition
Evidence
Retrieval
Deep
Evidence
Scoring
Hypothesis
Generation
Hypothesis and Evidence
Scoring
Learned Models
help 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
Technologies behind IBM
Watson challenge
http://clic.humnet.unipi.it

Question
Answer &
Confidence
Watson
Technologies behind IBM
Watson challenge

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 Data
15 – Cognitive Computing at
Work

Putting Watson at work to address the
world’s pressing issues
R&D
Demonstration
Commercialization
Cross-industry
Applications
IBM
Research
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
Retail
The 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 Ecosystem
Watson
Engagement Advisor
Watson
Oncology Advisor
SCALE
10s
1,000s
1,000,000s
Big Data Analytics Stack
Watson Foundations & Products
Watson
Discovery Advisor
Watson Emerging Technology
Watson Explorer Watson Developer Cloud Watson Analytics

# OF USERS
“Establish”
Bespoke engagements
“Extend” High volume
“Embed”
Massive volume
Watson Ecosystem
Watson
Engagement Advisor
Watson
Oncology Advisor
SCALE
10s
1,000s
1,000,000s
Watson Foundations & Products
Watson
Discovery Advisor
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
IBM Watson Family:
Products, Offerings & Solutions

Beyond Big Data
16 – Cognitive Advisors

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 listed
in 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
CustomerAgentBiologyResearcher

Published
Knowledge
Published
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
newOncologist

Enables researchers to
connect DOTS in large
research data sets: in
biosciences, uncover
new insights into
relationships between
genes, proteins,
pathways, phenotypes
and diseases
newResearcher
Accelerating 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 Data
17 – A Cognitive Ecosystem

Delivering the cognitive experience to
the masses
engaged innovators million equity
investments
subject matter
experts
Watson
Developer
Cloud
Watson
Content
Store
Watson
Talent
Hub
+ +
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

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