2013 csi interchange_pietro_leo - ex

Big Data, the latest updates
1
Plenary session – 14 June 2013
Pietro Leo
Executive Architect - IBM GBS Italy Big
Data Analytics Leader
Global Technology Progam Manager -
IBM Academy of Technology
Email: pietro_leo@it.ibm.com
@pieroleo www.linkedin.com/in/pieroleo

BIOGRAPHY
Pietro Leo
Executive Architect - IBM GBS Italy
Big Data Analytics Leader
Global Technology Progam Manager
- IBM Academy of Technology
Executive Analytics Architect with 20 years professional experience in Research & IT
Services
IBM Academy of Technology Core management Team Member and Global
Technology Program Manager
Extensive experience on Content Analytics, Big Data Analytics, Social Media Analytics,
Knowledge Management, Knowledge and Data Integration, Very Large Mining and Search
Engines, Semantic Search, Bioinformatics areas helping clients in complex (multi-
millions) and mission-critical projects
Technical leader as well as chief architect and scientist in a number of analytics
projects whose overall effort size is over 150 years/man
Social Business Passionate from disparate angles: Member of IBM Service Corps
working in Ghana and strong #Socbiz and #innovation expert and #startup hunter
Multidisciplinary education background: Higher artistic degree in Oboe, Computing
science degree, Master of Science by Research degree in applied artificial intelligence,
Master's degree in public funding management.
Keynote speaker and author or co-author of more than 70 scientific and technical
publications and and as well as co-author of two IBM books edited by IBM Readbooks.
Received more than a dozen of IBM special awards for high technical
achievements including also the mention into the IBM Corporate Technical Award Book
2010 edition, the IBM IT Architect Worldwide Technical Leadership Excellence Award in
2006 and the IBM Academy President's Award 2012.

Agenda
Defining Big Data
Big Data as a macro-trend and the State-of-the-Art
The business impact of Big Data & Deep Dive on selected Big
Data Experiences
A Big Data IT Perspective
Wrap-up & organizing for Big Data: Next “best action”

Measuring Big Data by using Big Data
Source: Google Trends
Big Data
Business Intelligence
Data Warehouse
Business Analytics
Correlated Query Terms

Conventional Definitions of “Big Data”
Never before possible
Social Data
Large volumes
Unstructured Data
Valuable insight, but difficult to extract
Basically an ETL environment
….....
These are partial and
or wrong definitions

7
Big Data enables us to see with new eyes....

8
“The real voyage of discovery consists not in seeking
new lands, but in seeing with new eyes....”
Marcel Proust, A la recherche du temps perdu, 1913/27
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
Dog Head
Fruit Bowl Waterfall
Table
Bridge
Dog Collar
Dog Muzzel
Hill
Beach
River
Point of View 1Point of View 1 Point of View 2Point of View 2

9
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

10
>80% Unstructured Data
+ External Data
“Untouched” Data
+ Stream of Data
Enterprise Data Machine Data People Data
Big Data metaphor

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

We produce and consume Data for a specific purpose

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

14
Big Data as a new business concept:
New values and opportunities for a number of stakeholders
Chief Marketing Officer
how to improve customer focus?...could predict the right offer
for the right customer at the right time and improve customer
value and intimacy or prevent churn?
Chief Product Designer
...how we can innovste? … could
we improve our product
channels/design offering??
Chief Finance
Officer
...could streamline
compliance and
understand risk
exposure across
businesses and
regions?
Chief Risk Officer
...uses anti fraud predictive analytics to detect and
prevent rapid fire anomalous transactions or wire
transfers identified as high probability of fraud?
Chief Executive Officer
...could make better business decisions
using accurate data across all
company/system dimensions and
across time horizons: past, present and
future?
Chief Information Officer
...could analyze oceans of machine generated logs to
predict which components or equipment in the
datacenter are likely to fail and thereby avert a disruption
during critical quarter end? How we can support Zero
high risks or manage crisis?
Big
Data
Analytics

Big Data as a new technology concept:
We need to combine internal and external data, utilized and under-utilized data,
structured and unstructured data... and cross-link organization knowledge & data
silos
CRM
• emails
• claims
• call center scripts
• Chats with customers
• …
Transactional Info.:
• Transactions
• Orders
• consultancies
• …
Legal Info:
• Contracts
• Complaints
• Reports
• Legal Actions
• Fraud Data
• …
Knowledge Management
•Manuals, wikis, couses
•Projects Data
•Market Analysis
•RSS Business Feeds
•Data feed: Bloomberg reuters
• …
IT Systems
System Logs
Application logs: web, vending machines,
mobile
Video
Sensor Networks, RFID
• …
Social Media:
• Global Social Networks: tweeter,
facebook, etc.
• Small communities: blogs, muros
corporativos,
• Internal Social Networks
(employees)
• News
• …
Big
Data
Analytics

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

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

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 end points:
Buyer
Fail!
Fail!
Fail!
Fail
Fail!
Fail!
Fail!
Fail!
Fail!
Fail!
Fail!Fail
Fail!
Fail!
Fail!
….Win!!!
Buying Decision
Cloud
Yes!
For Business, Big Data is the answer and the need of the
new emerging sub-transactional era

19
Social
Data from and about People
Physical
Sensors & Streams
Terabytes to exabytes of
existing data
to process
Streaming data,
milliseconds to seconds to
respond
Structured, Semi-
structured Unstructured,
text & multimedia
Uncertainty from
inconsistency,
ambiguities, etc.
Volume
Velocity
Variety
Veracity
Data
Content
>80%
<20%
Traditional
Enterprise Data
Big data embodies new data characteristics created by
today’s digitized marketplace
Biological
DNA Sequencers

20 20
GlobalDataVolumeinExabytes
Sensors
(InternetofThings)
Multiple sources: IDC,Cisco
100
90
80
70
60
50
40
30
20
10
AggregateUncertainty%
VoIP
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
2005 2010 2015
By 2015, 80% of all available data will be uncertain:
Veracity
Enterprise Data
Data quality solutions exist for
enterprise data like customer,
product, and address data, but
this is only a fraction of the
total enterprise data.
By 2015 the number of networked devices will
be double the entire global population. All
sensor data has uncertainty.
Social Media
(video, audio and text)
The total number of social media
accounts exceeds the entire global
population. This data is highly uncertain
in both its expression and content.

“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?

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, from Word Summit on Big Data and Organization Design Paris – 2013

IBM Institute for Business Value and the Saïd Business
School partnered to benchmark global big data activities
24
IBM Global Business Services, through the IBM
Institute for Business Value, develops fact-based
strategies and insights for senior executives around
critical public and private sector issues.
Saïd Business School
University of Oxford
IBM
Institute for Business Value
The Saïd Business School is one of the leading
business schools in the UK. The School is
establishing a new model for business education by
being deeply embedded in the University of Oxford, a
world-class university, and tackling some of the
challenges the world is encountering.
www.ibm.com/2012bigdatastudy

Big Data Analytics has evolved from business initiative
to business imperative
63%
58%
37%
2012
2011
2010 70%
increase
Source: 1
2010 and 2011 datasets © Massachusetts Institute of Technology. 2
Analytics: The real-world use of big data. 2012
Study conducted by IBM Institute for Business Value, in collaboration with Säid Business School at the University of Oxford.
3.6x
Likelihood of organizations competing
on analytics to outperform their peers2
Percentage of respondents who cited
a competitive advantage from the use
of information and analytics1,2

Three out of four organizations have big data activities
underway; and one in four are either in pilot or
production
26
Total respondents n = 1061
Totals do not equal 100% due to rounding
Big data activities
Respondents were asked to describe the state of
big data activities within their organization.
Early days of big data era
 Almost half of all organizations surveyed
report active discussions about big data
plans
 Big data has moved out of IT and into
business discussions
Getting underway
 More than a quarter of organizations have
active big data pilots or implementations
 Tapping into big data is becoming real
Acceleration ahead
 The number of active pilots underway
suggests big data implementations will rise
exponentially in the next few years
 Once foundational technologies are installed,
use spreads quickly across the organization

Five key findings highlight how organizations are
moving forward with big data
27
Big data is dependent upon a scalable and extensible
information foundation2
The emerging pattern of big data adoption is
focused upon delivering measureable business value5
Customer analytics are driving big data initiatives1
Big data requires strong analytics capabilities4
Initial big data efforts are focused on gaining insights
from existing and new sources of internal data3

Key Findings: Customer analytics are driving Big Data initiatives
Big data
Infrastructure
Big data
Sources
Analytics
capabilitiesTotal respondents n = 1061
Big data objectives
Top functional objectives identified by organizations with
active big data pilots or implementations. Responses have
been weighted and aggregated.
Customer-centric
outcomes
Operational
optimization
Risk / financial
management
New business
model
Employee
collaboration
Big Data areas of work

Big data leadership shifts from IT to business as organizations move
through the adoption stages
29
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

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

• Advanced client segmentation
• Leveraging customer sentiment analysis
• Reducing customer churn
• Optimizing the supply chain
• Deploying predictive maintenance capabilities
• Transform threat & fraud identification processes
Operations
• Enabling rolling plan, forecasting and budgeting
• Automating the financial close process
• Delivering real-time dashboards
Finance
• Making risk-aware decisions
• Managing financial and operational risks
• Reducing the cost of compliance
Risk
Examples:
Customers /
Clients
Another perspective: let's focus on ROI in core business
areas for Big Data
• Advanced client segmentation
• Leveraging customer sentiment/opinion analysis
• Reducing customer churn

Big Data for Customer Analytics challenge: build a
360°Integrated Customer View … and more!
SINGLE VIEW
Business Data,
Social Data,
Interactive data
360°Integrated
Customer View
Marketing
Cust. Care
Sales
Risk, Fraud
Customers /
Clients

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

360°Integrated Customer View … and more
360°Integrated
Customer View
Customers /
Clients
How?How?Why?Why?
Who?Who? What?What?
Project Example 1
TV Broadcaster
Project Example 2
Media and Entertainment
Project Example 3
Hair care manufacturer
Big Data Analytics
Project Space

@pieroleo www.linkedin.com/in/pieroleo36
• Social media analysis is a new and increasingly relevant way to
become more competitive in consumer-driven markets. Mediaset
wanted to increase its market share as well as launch new services
and digital-content distribution. s marketing campaigns and better
• Television content and services are becoming increasingly
consumer driven, and the media outlet that can capture and
use customer sentiment to its benefit gains a competitive
advantage. This media provider in Italy applied an advanced
analytics solution to analyze more than 1.6 million
unstructured data points from Web 2.0 sources to gain an
understanding of its customers’ attitudes, opinions and
preferences.
Challenge
Benefits
Solution
Customer Quote
“Big data is a great opportunity for TV
innovation in the next years. TV viewing
is transforming into a multiplatform and
participative experience; the better we
know and understand our viewers, the
better we can serve them”.
36
A TV broadcaster analysed Big Data Analytics to collect Customer longitudinal point
of views from Web 2.0 and correlate them with internal data
• Television content and services are becoming increasingly
consumer driven, and the media outlet that can capture and
use customer sentiment to its benefit gains a competitive
advantage. This media provider in Italy applied an advanced
analytics solution to analyze more than 1.6 million
unstructured data points from Web 2.0 sources to gain an
understanding of its customers’ attitudes, opinions and
preferences.
• Analyzed more than 1.6 million data points on social media outlets
to discover public sentiment and correlations with customer
satisfaction
• Helped Mediaset to discover and measure viewer sentiments
expressed in Web 2.0 contents related to its TV contents and ad
campaigns

Big Data Analytics to expand knowledge about customer profiles and measuring
marketing campaign
• Analysis of social media messages for large Media and
Entertainment company to determine reaction
to movie commercials aired during the SuperBowl
• Insights based on 30M+ social media consumer
profiles created by analyzing over a Billion messages
• Real-time evolution of insights correlated with the
airing of the commercial
• Analysis of social media messages for large Media and
Entertainment company to determine reaction
to movie commercials aired during the SuperBowl
• Insights based on 30M+ social media consumer
profiles created by analyzing over a Billion messages
• Real-time evolution of insights correlated with the
airing of the commercial
Key Business Questions:
How many people are talking about the film ?
• Intention to see the movie, Impact of SuperBowl
commercial
Who are they ?
• Demographics, Influencers, avid movie goers
What is the reaction ?
• Categorized sentiment (plot, characters, …)
• Comparison with competitive movies
Key Business Questions:
How many people are talking about the film ?
• Intention to see the movie, Impact of SuperBowl
commercial
Who are they ?
• Demographics, Influencers, avid movie goers
What is the reaction ?
• Categorized sentiment (plot, characters, …)
• Comparison with competitive movies
Jan 1
5pm 6pm 7pm 8pm
Super Bowl
Monitoring Period Feb 5th
Golden Globes NFC Championship
9pm 10pm 11pm
• Buzz and sentiment
• Gender, Location and Occupation
• Avid movie-goers, comic book fans
• Intent to see specific films
• Specific attributes of the film/trailer

■ Their earlier analysis of Google
search requests suggested that
hair problems formed a
significant part of what
consumers care about…
■ … but Big Data Analytics showed
that people rarely chatted about
their hair problems when discussing
and comparing hair care products
The marketing messages were re-focused in line with this more nuanced insight –
promoting what customers want for their hair to harmonize with the social media agenda
On another perspective an Hair care manufacturer finds out what consumers
really chat about

360-degree Consumer Profiles from Social Media
Personal Attributes
• Identifiers: name, address, age, gender,
occupation…
• Interests: sports, pets, cuisine…
• Life Cycle Status: marital, parental
Personal Attributes
• Identifiers: name, address, age, gender,
occupation…
• Interests: sports, pets, cuisine…
• Life Cycle Status: marital, parental
Products Interests
• Personal preferences of products
• Product Purchase history
• Suggestions on products & services
Products Interests
• Personal preferences of products
• Product Purchase history
• Suggestions on products & services
Life Events
• Life-changing events: relocation, having a
baby, getting married, getting divorced, buying
a house…
Life Events
• Life-changing events: relocation, having a
baby, getting married, getting divorced, buying
a house…
Monetizable intent to buy products Life Events
Location announcements
Intent to buy a house
I'm thinking about buying a home in Buckingham Estates per a
recommendation. Anyone have advice on that area? #atx #austinrealestate
#austin
I'm thinking about buying a home in Buckingham Estates per a
recommendation. Anyone have advice on that area? #atx #austinrealestate
#austin
Looks like we'll be moving to New Orleans sooner than I thought.
Looks like we'll be moving to New Orleans sooner than I thought.
College: Off to Stanford for my MBA! Bbye chicago!
College: Off to Stanford for my MBA! Bbye chicago!
I'm at Starbucks Parque Tezontle http://4sq.com/fYReSj
I'm at Starbucks Parque Tezontle http://4sq.com/fYReSj
I need a new digital camera for my food pictures, any
recommendations around 300?
I need a new digital camera for my food pictures, any
recommendations around 300?
What should I buy?? A mini laptop with Windows 7 OR a Apple
MacBook!??!
What should I buy?? A mini laptop with Windows 7 OR a Apple
MacBook!??!
Timely Insights
• Intent to buy various products
• Current Location
• Sentiment on products, services, campaigns
• Incidents damaging reputation
• Customer satisfaction/attrition
Timely Insights
• Intent to buy various products
• Current Location
• Sentiment on products, services, campaigns
• Incidents damaging reputation
• Customer satisfaction/attrition
Relationships
• Personal relationships: family, friends and
roommates…
• Business relationships: co-workers and
work/interest network…
Relationships
• Personal relationships: family, friends and
roommates…
• Business relationships: co-workers and
work/interest network…

AMEX Example: Business Models based on connecting Virtual and Real Worlds
American Express
Smart Offer
A portal that collects special
offers and discounts from
retailers and detail about the
customer segment that is target
Marketing segmentation engine
that evaluate customer profiles
and select the best coupon to
propose
Moble app and connection with
Twitter, Facebook e
Foursquare to communicate
with the customers and enable
viral effects
Just virtual Coupons are managed!
Customers activate the coupon and receive
on montly basis on the credit card account the
equivalent of the coupon discounts after that
transactions were registred
API

What Data AMEX Sync acquires from Facebook, Twitter e Foursquare? New CRM Data...
American Express
Smart Offer

Twitter Inc. is experimenting with becoming a shopping mall.
Twitter and American Express Co. said Monday they struck a partnership to allow Twitter users to buy products for the
first time directly on the short messaging service.
American Express card holders who connect their card numbers to their Twitter accounts can post on Twitter to trigger a
purchase of select products, including discounted American Express gift cards, Kindle Fire tablets from Amazon.com Inc.
and jewelry from designer Donna Karan. The program will roll out over the next few days.
The arrangement hints at a potential new source of revenue for Twitter, which has largely been reliant on advertising for
revenue. Neither Amex nor Twitter will discuss financial terms of their partnership, but Twitter wouldn’t rule out taking a cut
of future e-commerce sales. The American Express partnership also is a way for Twitter to prove the link between
marketing activity on Twitter and a ringing cash register.
API - Services
Twitter, Amex Launch Pay-By-Tweet Service
Source: http://blogs.wsj.com/digits/2013/02/11/twitter-amex-to-collaborate-on-e-commerce-sales-on-twitter/

external traits
intrinsic
traits
Omni Profile
of each individual
…..Further Hyper-
Personalized
360°Integrated
Customer View
+
Personality
Opennes
Conscientiousness
Extraversion
Agreeableness
Neuroticism Perception
Fundamental needs
Ideal
Liberty
Love
Structure
Social behavior
Responsiveness
Temporal patterns of activities
Social relationships to others
Similarity
Tie strength
Frequency
Recency
Intensity
Reciprocity
Intimacy
360°Integrated Customer View … and more!
Customers /
Clients

Big Data enabled doctors from University of Ontario to apply
neonatal infant monitoring to predict infection in ICU 24 hours in
advance
Performing real-time
analytics using physiological
data from neonatal babies
Continuously correlates data
from medical monitors to
detect subtle changes and
alert hospital staff sooner
Early warning gives
caregivers the ability to
proactively deal with
complications
“Customer
Analytics” in
some Industry
means safe life

Data Warehouse
Operational Analytics
Structured, analytical, logical
Big Data
Ad Hoc Analytics
Creative, holistic thought, intuition
Unstructured
Exploratory
Iterative
Brand sentiment
Product strategy
Maximum asset utilization
Structured
Repeatable
Linear
Monthly sales reports
Profitability analysis
Customer surveys
Big Data IT Perspective: augmenting traditional IT investments

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

BI /
Reporting
Exploration /
Visualization
Functional
App
Industry
App
Predictive
Analytics
Content
Analytics
Analytic Applications
IBM Big Data Platform
Systems
Management
Application
Developmen
t
Accelerators
Big
Insights
Volume, Variety
Cost-effectively process
and analyze any type of
data
Visualization
& Discovery
Visibility
Understand, find, and
navigate federated big
data
Data
Warehouse
Volume, Structured
Purpose-built offerings
High-performance
appliances and software
Information Integration & Governance
Veracity
Trusted information
Parallel processing for
high-volume integration
Best practices
Stream
Computing
Building a Big Data Platform: The IBM perspective
Velocity
Analyze data-in-motion to
produce insights in
micro-seconds
Agile, multi-tenant shared infrastructure
BIG Performance
Option of an optimized
low-latency MapReduce
implementation fully
compatible with open-
source Hadoop

Business-centric Big Data enables you to start with a critical business pain and expand
the foundation for future requirements.... start with the most critical one!

51
Using twitter?

Dear delegate, we value the feedback provided through feedback
forms, but we would like to encourage you to use the twitter hashtag
#IBMCSII for your:
Findings on the event
Logistics
Suggestions
Network diner
Speakers
Content
Weather
...
@pieroleo
www.linkedin.com/in/pieroleo
You can find me here:

Grazie!
Pietro Leo
Executive Architect - IBM GBS Italy
Big Data Analytics Leader
Global Technology Progam Manager
- IBM Academy of Technology

2013 csi interchange_pietro_leo - ex

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