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@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
From Managing (Big)Data to Manage
Cogs
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo 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
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@pieroleo www.linkedin.com/in/pieroleo
DATA
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@pieroleo www.linkedin.com/in/pieroleo
DATA is the new basis of
competitive advantage.....
.......and the engine of
Digital Transformation
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
DATA is the new basis of
competitive advantage............and the engine of
Digital Transformation
CAMSS Data as a Gravity New natural resource
New business models
Human FactorBig Data and IT Text Analytics MultiMedia
Analytics
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
DATA is the new basis of
competitive advantage............and the engine of
Digital Transformation
Capitalizing On Social Media
Customer Analytics Techniques Social Analytics
Cognitive Computing
Cognitive AdvisorsIBM Watson Watson Ecosystem
Customer Analytics
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@pieroleo www.linkedin.com/in/pieroleo
Big Data1 – Technological Factors
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleoMagritte
Manet Dal Monte
Leonardo
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
Magritte
Manet Dal Monte
Leonardo
CLOUD ANALYTICS
SOCIAL MOBILE
Digital Transformationof individuals and
organizations
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleoMagritte
Manet Dal Monte
Leonardo
CLOUD ANALYTICS
SOCIAL MOBILE
DIGITAL TRANSFORMATION =
(…..Big Data ......)
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Data has a gravity!
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@pieroleo www.linkedin.com/in/pieroleo
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
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Core Observations and why data value is emerging
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Big Data2 – Big Data Metaphors & IT
Paradigm Shifts
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15
>80% Unstructured Data
+ External Data“Untouched” Data+ Stream of Data
Enterprise Data Machine Data People Data
Big Data metaphor 1
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Data is there and we need to make the best out of it
Big Data metaphor 2
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We produce and consume Data for a specific purpose
Big Data metaphor 2
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Surce: http://pennystocks.la/internet-in-real-time/
Big Data Faces: the Internet in Real-Time
Big Data metaphor 3
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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
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@pieroleo www.linkedin.com/in/pieroleo
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.
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@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
TRADITIONAL APPROACH
Carefully cleanse information before any analysis
Small amount of carefully organized information
BIG DATA & ANALYTICS APPROACH
Analyze information as is, cleanse as needed
Large amount of messy
information
Hypothesis Question
DataAnswer
TRADITIONAL APPROACH
Start with hypothesis andtest against selected data
BIG DATA & ANALYTICS APPROACH
Explore all data andidentify correlations
Data Exploration
CorrelationInsight
Repository InsightAnalysisData
TRADITIONAL APPROACH
Analyze data after it’s been processed and landed in a warehouse or mart
Data
Insight
Analysis
BIG DATA & ANALYTICS APPROACH
Analyze data in motion as it’s generated, in real-time
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Big Data 3 – Business Factors
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@pieroleo www.linkedin.com/in/pieroleo
Source: http://datacoup..com
Value of Data
Pietro Leo's SecondIncome!
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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
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@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
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
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For Science, Big Data is the microscope of the 21st century
Wine DNA Tracing
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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
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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
@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
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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
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“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?
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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
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Big Data4 – Big Data Applications
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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
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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
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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
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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
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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
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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
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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
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Big Data5 – Big Data IT Perspective
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@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
Bg Data Foundations
Analytic Appliances
Analytic Appliances
Security, Governance and Business ContinuitySecurity, Governance and Business Continuity
Information Movement, Matching & Transformation
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
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@pieroleo www.linkedin.com/in/pieroleo
The IBM experience and PoV
Analytic Appliances
Analytic Appliances
Security, Governance and Business ContinuitySecurity, Governance and Business Continuity
Information Movement, Matching & Transformation
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
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
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@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
Big Data6 – Human Factor!
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@pieroleo www.linkedin.com/in/pieroleo
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
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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
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Big Data7 – Mining unstructured and non
conventional data
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@pieroleo www.linkedin.com/in/pieroleo
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
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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
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Structured versus Unstructured Information: What does it mean?
Office Location is unstructured
Address
City
Zip code….
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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
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@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
Text Analytics: transforms UnStructured Information into Structured data
Before After
Concept/entity extractionRelationship extractionSentiment Analysis
Linguistic Analysis CategorizationClustering,
Text AnalyticsTasks
DocumentSummarization….
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@pieroleo www.linkedin.com/in/pieroleo
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
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A beautiful Vacation!
Checco
Greta
http://visual-recognition-demo.mybluemix.net/
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An Example of a Multimedia Analytics Environment
http://mp7.watson.ibm.com/imars/
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@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
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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
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Big Data Applications8 – Customer Analytics
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Chewing Gum Wall in California
Source: http://en.geourdu.co/buzz/bizarre-shocking/chewing-gum-wall-in-california/
San Luis Obispo
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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/
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@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
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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
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@pieroleo www.linkedin.com/in/pieroleo
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
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360°Integrated Customer View
!Customer Analytics challenge: build a 360°Integrated Customer View … and more
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@pieroleo www.linkedin.com/in/pieroleo
SINGLE VIEWBusiness Data,
Social Data, Interactive data
360°Integrated Customer View
Marketing
Cust. Care
Sales
Risk, Fraud
Customer Analytics challenge: build a 360°Integrated Customer View … and more
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@pieroleo www.linkedin.com/in/pieroleo
SINGLE VIEWBusiness Data,
Social Data, Interactive data
360°Integrated Customer View
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
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@pieroleo www.linkedin.com/in/pieroleo
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
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?
Com
p le xi ty
Techniques
CapabilitiesCognos - Big Insights – SMA - SPSS – Watson Explorer – Adv. Analytics & Cognitive Services
From CHALLENGES to TechniquesAnd Capabilities
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@pieroleo www.linkedin.com/in/pieroleo
CustomerAnalytics & TRUST
“Trust men and they will be true to you; treat them greatly and they will show themselves great.”
Ralph Waldo Emerson
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@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
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Big Data Applications9 – Capitalizing On Social Media Data
Today
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@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
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
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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
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@pieroleo www.linkedin.com/in/pieroleo
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
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Smart Organizations Think Beyond “Likes”
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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?
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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?
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CMOs are Underprepared for New Market Dynamics
84
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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.
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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)
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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 !
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@pieroleo www.linkedin.com/in/pieroleo
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 !
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@pieroleo www.linkedin.com/in/pieroleo
Big Data and Social Analytics13 – Customer Analytics
Techniques
A cura di: Pietro Leo
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@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
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
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?
Com
p le xi ty
Techniques
CapabilitiesCognos - Big Insights – SMA - SPSS – Watson Explorer – Adv. Analytics & Cognitive Services
From CHALLENGES to TechniquesAnd Capabilities
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
Big Data Applications10 – Exploring an Enterprise Social Analytics
Enviroment
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@pieroleo www.linkedin.com/in/pieroleo
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
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?
Co
mp
lex ity
Cognos - Big Insights – SMA - SPSS – Watson Explorer – Adv. Analytics & Cognitive Services
Techniques
Capabilities
CustomerAnalyticsPractical CHALLENGES
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
Social Media Analytics a best in breed solution from IBM
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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
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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
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@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
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Segment: Author Demographics
97
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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
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@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
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@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
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@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
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@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
Big Data Applications11 – Social Analytics Advanced Techniques
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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
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?
Co
mp
lex ity
Cognos - Big Insights – SMA - SPSS – Watson Explorer – Adv. Analytics & Cognitive Services
Techniques
Capabilities
Customer AnalyticsPractical CHALLENGES
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleoText, text, text.... text
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@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/
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Identity Analytics Models
Strong Weak
Big Match
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Identity Analytics Models
Strong Weak
Big Match
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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
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@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
What Data AMEX Sync acquires from Facebook data......?
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Let's zoom on Piero Leo Facebook profile....
I authorized AMEX... for
I authorized AMEX... for
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@pieroleo www.linkedin.com/in/pieroleo
Identity Analytics Models
Strong Weak
Big Match
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
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
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© 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
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
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Big Data Applications11 – Social Analytics
Advanced Techniques (part b)
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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
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?
Co
mp
lex ity
Cognos - Big Insights – SMA - SPSS – Watson Explorer – Adv. Analytics & Cognitive Services
Techniques
Capabilities
Customer AnalyticsPractical CHALLENGES
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
Personality Insights from my Twitter Stream
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Personality traits
Values and Needs
When I talk
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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
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http://your-celebrity-match.mybluemix.net/
Examples of Systems that uses Personality Insights
http://usermodeling-ao15.mybluemix.net/systemu/home#findmymatch
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Personality Insight as a service
http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/personality-insights.html
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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
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Since the first instant
Experiencing Personality Insight as a service
PersonalityTraits
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http://1001loveletters.com/Cartas.aspx?Id=589
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
Experiencing Personality Insight as a service
Summary of the Personality
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http://1001loveletters.com/Cartas.aspx?Id=239
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
Experiencing Personality Insight as a service
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You weren’t honest with me
Experiencing Personality Insight as a service
PersonalityTraits
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@pieroleo www.linkedin.com/in/pieroleo
http://1001loveletters.com/Cartas.aspx?Id=239
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
Experiencing Personality Insight as a service
Summary of the Personality
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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
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?
Co
mp
lex ity
Cognos - Big Insights – SMA - SPSS – Watson Explorer – Adv. Analytics & Cognitive Services
Techniques
Capabilities
Customer AnalyticsPractical CHALLENGES
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
Big Data Applications11 – Social Analytics
Advanced Techniques (part c)
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@pieroleo www.linkedin.com/in/pieroleoImages, Imanges, Images... Images
Images Followers of a Brand
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Extracts Consumer Attributes from Images and Videos
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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
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Examples of Semantic classifiers for images and video
Automatic recognition of sports and activity categories
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@pieroleo www.linkedin.com/in/pieroleoCustomer Visual Attributes:Spans Multiple Facets and Complements TraditionalData Sources
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@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
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Big Data Applications12 – Deep Dive on a Social
Analytics Project
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Brand ClusterAcquiredAcquired Emerging Revenue Innvation Ready for IPO IPO
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A week in a Shoppingwindow
InterviewsInterviewsInterviews
Expert/SMEs Invoved
Isolated and extracted around 200 “key concepts”
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ThemesTechnology, Internationalization, e-commerce, Fashion & Art, Sharing Economy, Sustainability, Novelties, Materials, Colors, Traditional Shopping Spaces, Styles, Celebrities, Events
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@pieroleo www.linkedin.com/in/pieroleo
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Content shareAcquired
Acquired
Emerging
Revenue
Innvation
Ready for IPO
IPO
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Season EffectJeans and hand bags dominate discussions
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“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
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Made in Italy?
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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
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(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
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@pieroleo www.linkedin.com/in/pieroleo
Celebrities Opportunistics E-Commerce Official Brands Magazines Fashion Bloggers Others
Influencers
Celebrities Opportunistics E-Commerce Official Brands Magazines Fashion Bloggers Others
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Celebrities Opportunistics E-Commerce Official Brands Magazines Fashion Bloggers Others
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@pieroleo www.linkedin.com/in/pieroleo
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
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Who are your followers?
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versus
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versus
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versus
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Beyond Big Data13 – Cognitive Computing
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What's NEXT?We could manage new complexity of digital transformation
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@pieroleo www.linkedin.com/in/pieroleo
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. . .
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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
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...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
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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
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IBM Deep Blue, 1997 IBM Watson, 2011
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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!
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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
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Video 1
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•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
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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
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Beyond Big Data14 – How IBM Watson works
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….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
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Video 2
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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
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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
Keyword MatchingKeyword Matching
Keyword MatchingKeyword Matching
Keyword MatchingKeyword Matching
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
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@pieroleo www.linkedin.com/in/pieroleo
On 27th May 1498, Vasco da Gama landed in Kappad Beach
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 27th May 1498, Vasco da Gama landed in Kappad Beach
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
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@pieroleo www.linkedin.com/in/pieroleo
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.
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@pieroleo www.linkedin.com/in/pieroleo
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
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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/pieroleo
@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
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@pieroleo www.linkedin.com/in/pieroleo
Question
Answer & Confidence
Watson
Technologies behind IBM Watson challenge
http://clic.humnet.unipi.it
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@pieroleo www.linkedin.com/in/pieroleo
Question
Answer & Confidence
Watson
Technologies behind IBM Watson challenge
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@pieroleo www.linkedin.com/in/pieroleo
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
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Beyond Big Data15 – Cognitive Computing at
Work
@pieroleo www.linkedin.com/in/pieroleo
@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
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@pieroleo www.linkedin.com/in/pieroleo
© 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
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@pieroleo www.linkedin.com/in/pieroleo
# 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
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
# OF USERS
“Establish”Bespoke engagements
“Extend” High volume
“Embed”Massive volume
Watson EcosystemWatson
Engagement AdvisorWatson
Oncology Advisor
SC
AL
E
10s1,000s
1,000,000s
Watson Foundations & Products
WatsonDiscovery 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
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
Beyond Big Data16 – Cognitive Advisors
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
# OF USERS
“Establish”Bespoke engagements
“Extend” High volume
“Embed”Massive volume
Watson EcosystemWatson
Engagement AdvisorWatson
Oncology Advisor
SC
AL
E
10s1,000s
1,000,000s
Watson Foundations & Products
WatsonDiscovery 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
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
Oncologist Chef CustomerAgent BiologyResearcher
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
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
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Helps oncologists make better, more personalized treatment decisions by ranking treatment plans based on national guidelines, published literature, and expert insight
newOncologistVideo 3
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@pieroleo www.linkedin.com/in/pieroleo
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
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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
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@pieroleo www.linkedin.com/in/pieroleo
Beyond Big Data17 – A Cognitive Ecosystem
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
# OF USERS
“Establish”Bespoke engagements
“Extend” High volume
“Embed”Massive volume
Watson EcosystemWatson
Engagement AdvisorWatson
Oncology Advisor
SC
AL
E
10s1,000s
1,000,000s
Watson Foundations & Products
WatsonDiscovery 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
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
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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@pieroleo www.linkedin.com/in/pieroleo
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
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oal
Ch
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ng
e ad
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ssed
Video 6
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@pieroleo www.linkedin.com/in/pieroleo
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
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Video 1Video 3
CogniToys
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@pieroleo www.linkedin.com/in/pieroleo
Beyond Big Data18 – Watson Developer Cloud
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
# OF USERS
“Establish”Bespoke engagements
“Extend” High volume
“Embed”Massive volume
Watson EcosystemWatson
Engagement AdvisorWatson
Oncology Advisor
SC
AL
E
10s1,000s
1,000,000s
Watson Foundations & Products
WatsonDiscovery 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
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
IBM Watson Services
Source: https://console.ng.bluemix.net/home
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@pieroleo www.linkedin.com/in/pieroleo
Beyond Big Data19 – Computational Creativity
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
# OF USERS
“Establish”Bespoke engagements
“Extend” High volume
“Embed”Massive volume
Watson EcosystemWatson
Engagement AdvisorWatson
Oncology Advisor
SC
AL
E
10s1,000s
1,000,000s
Watson Foundations & Products
WatsonDiscovery 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
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
Source: AARON, Harold Cohen - http://www.aaronshome.com/aaron/index.html
AARON: Computational Creativity Example
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IBM Chef Watson.
Inspire your recipes with Cognitive Cooking
Cognitive Cooking
208
Cognitive Computing approach to Computational Creativity
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@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
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
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@pieroleo www.linkedin.com/in/pieroleo
211
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
212
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@pieroleo www.linkedin.com/in/pieroleo
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@pieroleo www.linkedin.com/in/pieroleo
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@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
215
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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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
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
Beyond Big Data20 – Search, Deep Analytics &
Mining
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
# OF USERS
“Establish”Bespoke engagements
“Extend” High volume
“Embed”Massive volume
Watson EcosystemWatson
Engagement AdvisorWatson
Oncology Advisor
SC
AL
E
10s1,000s
1,000,000s
Watson Foundations & Products
WatsonDiscovery 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 )
Watson Developer Cloud Watson Analytics
IBM Watson Family: Products, Offerings & Solutions
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
© 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
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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
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@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
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?
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@pieroleo www.linkedin.com/in/pieroleo
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“
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@pieroleo www.linkedin.com/in/pieroleo
Document Analysis Facets
Time Series
Deviations / Trends
Dashboard
Facet PairsConnections
Sentiment
© 2014 International Business Machines Corporation - IBM Confidential
Examples of analytics component capabilities
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@pieroleo www.linkedin.com/in/pieroleo
Beyond Big Data21 – Analytics for ALL!
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
# OF USERS
“Establish”Bespoke engagements
“Extend” High volume
“Embed”Massive volume
Watson EcosystemWatson
Engagement AdvisorWatson
Oncology Advisor
SC
AL
E
10s1,000s
1,000,000s
Watson Foundations & Products
WatsonDiscovery 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 )
Watson Developer Cloud Watson Analytics
IBM Watson Family: Products, Offerings & Solutions
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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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
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@pieroleo www.linkedin.com/in/pieroleo
Credits: Dashon Goldson Gallery
TOUCHDOWN!
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RUSHING TD
FUMBLES
PASSING TD
1
2
3
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@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
SEGMENTO 2
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
SEGMENTO 2
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
SEGMENTO 3
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
Beyond Big Data22 – Examples of advanced
cognitive research areas
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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)
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
Advanced Interaction and Reasoning
Video 8
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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
@pieroleo www.linkedin.com/in/pieroleo
@pieroleo www.linkedin.com/in/pieroleo
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