I dati sono il nuovo Capitale: come il capitale finanziario, sono una risorsa che deve essere gestita, raccolta e tenuta al sicuro, ma deve essere anche investita dalle organizzazioni che vogliono ottenere vantaggio competitivo. I dati non sono una risorsa nuova, ma soltanto oggi per la prima volta sono disponbili in abbondanza assieme alle tecnologie necessarie per massimizzarne il ritorno. Esattamente come l'elettricità fu una curiosità da laboratorio per molto tempo, finché non venne resa disponibile alle masse e dunque cambiò totalmente il volto dell'industria moderna.Ecco perché per accelerare il cambiamento è necessario un approccio innovativo alla esecuzione delle iniziative orientate ai Big Data: un laboratorio analitico come catalizzatore dell'innovazione (Data Lab).In questo webinar sulle tecnologie Oracle, utilizzeremo il consueto approccio del racconto basato su casi d’uso ed esperienze concrete.

1. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Analytics Data Lab
The power of Big Data Investigation and
Advanced Analytics to maximize the Data Capital
Roberto Falcinelli
Senior Manager - Sales Consulting & Business Development
Oracle Business Analytics
24 Gennaio 2017
Webinar per Fondazione CRUI

2. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 2
Abstract
Analytics Data Lab
The power of Big Data Investigation and
Advanced Analytics to maximize the Data Capital
I dati sono il nuovo Capitale: come il capitale finanziario, sono una risorsa che deve essere
gestita, raccolta e tenuta al sicuro, ma deve essere anche investita dalle organizzazioni che
vogliono ottenere vantaggio competitivo. I dati non sono una risorsa nuova, ma soltanto oggi
per la prima volta sono disponbili in abbondanza assieme alle tecnologie necessarie per
massimizzarne il ritorno. Esattamente come l'elettricità fu una curiosità da laboratorio per
molto tempo, finchè non venne resa disponibile alle masse e dunque cambiò totalmente il
volto dell'industria moderna.
Ecco perchè per accelerare il cambiamento è necessario un approccio innovativo alla
esecuzione delle iniziative orientate ai Big Data: un laboratorio analitico come catalizzatore
dell'innovazione (Data Lab).
Vieni a scoprire durante questo webinar, attraverso il racconto di casi d’uso ed esperienze
concrete dei suoi clienti, come Oracle mette a disposizione le tecnologie e le soluzioni che le
hanno rese vincenti.

3. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Agenda
1
2
3
Data Capital: Big Data & Analytics Market Trends
Data Lab Catalyst of Innovation
Oracle Big Data Analytics capabilities enabling the Data Lab
A Data Lab Demo Story Example
Use Cases & References
4
5

4. The Rise Of Data Capital
1. Data is now a kind of capital
2. Companies & organizations must
execute new strategies to compete
3. Data needs to be secured and
invested like the economic capital

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Analytics
2.0
Era of Competing on Analytics
• After Big Data (ABD)
• Started with Web Companies
• Extend analytics to external, larger
and less structured datasets
• New technologies for new challenges
• Recognition of Data Science
Tom Davenport – Analytics 3.0 – HBR - Dec
2013
A new shift for Analytics
Analytics
1.0
Era of Business Intelligence
• Before Big Data (BBD)
• Batch oriented internal data
collection & preparation
• Batch oriented Analysis/Reporting
Focus on Improve
performance
DATA as a CAPITAL to invest and gain competitive advantage
Era of Data Enriched offerings
• Embed Analytics in New Products /
Services
• Affordable for every industry
• New and old data management technology
combined
• Faster “test-do-learn” cycle
• Analysts focus on Data Discovery
• Data Lab & Data Factory : turn exploratory
analysis into production capabilities
Analytics
3.0

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A change of paradigm in Information Management
6
Build a Data Reservoir to
serve as an enabler for more
powerful DWH’s
Provide all tools needed to
get value out of the Data
Reservoir
Empower business Users to
get value from Big Data (not
only geeks or data scientists)
Data Warehouse
Existing Sources Emerging Sources +
Existing not used internal
sources
Data ReservoirData Warehouse

7. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
A change of paradigm in Information Consumption
7
From “Business Intelligence” ...
• Known and structured access
patterns to structured
information
• Analytics activity in «delayed
mode» vs data generation and
preparation time
... To “Business Inspiration”
• IT central role in guidance and skills
• High complexity in data management makes
analytical tools a secondary priority
• Free-hand, Search
oriented information
access based on new
business models
• Real Time analytic activity
• New roles and skill are leading: Data Officers,
Data Scientists, Data Analysts
• Optimized usability for business-wise users is
first priority
A “bi-modal” approach to Business Analytics

8. Data Lab Catalyst of Innovation
Edison’s Invention Factory
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |

9. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | 9
Data Comes from Activity
Big Data, as a Global
Phenomenon, Is
Disrupting IndustriesPROCESSES
THINGS
PEOPLE

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Data and the Innovation Process
DATA LAB
DATA FACTORY
DATA WAREHOUSE
INVENT COMMERCIALIZERESEARCH DEVELOP
Churn Monetization Upsell 360
Quality Product Design
10

11. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
The Pillars and the Process
11
= +
Data Lab
Ecosystem
Built according to the key
requirements.
Pillars
Technology Areas that provides
the required features for current
and future needs
Process
Combines both experimental
approach and production
mainstream to maximize the “data
capital”
Data Lake
(all data store)
Data
Visualization
Machine
Learning &
Graph
Data
Discovery
Security

12. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
The Pillars
Data Lake
(all data store)
 Easy, visual, friendly way of
telling a story
 Fast, self-service way of
mashing up personal and
enterprise data
 Powerful, full enterprise
scale capability
Data
Visualization
 Provides a broad range of ML
algorithms based on open source,
market leading technologies
 Combines both ML in the Lab and in
the Process
 Extend ML with Graph features for
analytics on networks based on
relationships,.
Machine
Learning
& Graph
 Explores available source data
and their relationships (schema-
on-read approach)
 Transforms data on-the-fly and
Discovers hidden patterns
 Foudation of the “Lab”
Data
Discovery
 Secures data at rest (encryption)
and on the-the-fly
 Provides Access Control (SSO, LDAP)
throughout the architectural
components
 Profiles users according to their
rolesSecurity
All in Cloud

13. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
From Pillars to Process
13
Mainstream
Lab
Collect source
data and explore
their contents
Select and
prepare data for
exploitation
Experiment on data
through advanced
analytics
Bring the value into
production
Distribute insights and
analyze the return
Consumers
Experts
Data Scientists
Experts

14. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Advanced Analytics Approach
14
“Data Driven Research”
reasoning from the data to the
general theory
Machine Learning on the
Process
...
Data Discovery in the Lab
Source data are initially explored to find out
hidden relationships. This is the basis for picking
up relevant features to feed prediction models (
“features engineering”).
Induction
Data Scientists
Experts
Advanced Analytics in the Mainstream
The final step is to run ML models as well as new
patterns in the mainstream, make their outcome
available for the broad users community through
Data Visualization and Business Intelligence.
Consumers
Machine Learning in the Lab
When the data context has been outlined and
most relevant features identified, then ML models
can be built and evaluated over historical and new
(lab) data.
Data Scientists

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Bring all the pieces into a blueprint
15
Big Data Platform
Data Lab
Data LakeData Factory
Analytics PlatformData Sources
People
Data Services
Applications
Big Data NoSQL
Data
Integration and
Governance
Big Data
Discovery
Machine
Learning
Graph
Analytics
IOT
Database
+ In-Memory
Data
Visualization
& Analytics
Services
DataFlow ML

16. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Machine Learning
with Oracle
Oracle Confidential – Highly Restricted 16
Machine
Learning
at Scale
Huge set of
predefined
models
Extends Spark Mllib with R CRAN
models and packages
35 built-in Graph
Algorithms
Based on
Standards
R Language and models
Spark MLLib on
Hadoop
Python , Java
and Scala Spark
APIs
Gremlin and Blueprints for
Property Graph
Transparently move ML models and workloads
between on-premise and public cloud
Both on
prem and
on public
cloud
Cross
Technologie
s
ORE on Oracle
databases
ORAAH, Spark MLlib, Big Data
Spatial and Graph on BDA
Real Time Decision and
Stream Explorer
Big Data Discovery
Optimized
for
performanc
e
ORE and ORAAH are optimezed version of
R to exploit parallel processing and
hardware capabilities of modern CPUs.
Oracle Engineered Systems
enhance ORE, ORAAH and Spark
models eleboration shortening
time to value.
Thightly
Integrated
Machine learning
capabilities integrated out-
of-the-box throughout the
Oracle stack, from data
stores, to front end analytics
and streaming processing
via data integration.
ODI

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Oracle Big Data Discovery. The Visual Face of Big Data
Find Explore Transform Discover Share

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A Data Lab Demo Story
Example in Banking
Luigi
Analista (aspirante Data Scientist)

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I dati aziendali vengono organizzati in un catalogo. Luigi
può visualizzare i dati a cui è stato abilitato all’accesso.
Può anche selezionare i dataset in base alle
caratteristiche e può immediatamente visualizzarne tutti
dettagli

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Dopo aver selezionato il dataset dei bonifici, Luigi
può immediatamente profilarne il contenuto,
visualizzando graficamente la completezza e la
distribuzione dei valori di ciascun attributo

21. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 21
Luigi combina più attributi per evidenziarne la correlazione.
In questo caso si evidenzia un’inversione per l’anno 2014 del
rapporto tra bonifici in ingresso e in uscita. In funzione delle
caratteristiche degli attributi Luigi può scegliere tra diverse
rappresentazioni grafiche

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Filtrando per l’anno 2014 e selezionando solo i bonifici in
uscita, Luigi visualizza in ordine descrescente l’ammontare
delle transazioni per ciascuna descrizione. Si evidenza una
particolare rilevanza di operazioni legate all’acquisto di titoli
che lo incuriosisce

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Questo fenomeno spinge Luigi ad approfondire la
sua analisi. Per far questo crea un nuovo progetto
aggiungendo a questo il dataset dei bonifici

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Per poter identificare meglio le transazioni
d’interesse Luigi definisce una funzione di
trasformazione per verificare l’esistenza di
determinate parole chiave all’interno della
descrizione del bonifico e creare un nuovo attributo.
La trasformazione viene aggiunta allo script ed
eseguita immediatamente

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Per estendere le possibilità di analisi, Luigi aggiunge
al progetto il dataset dell’anagrafica clienti e il
dataset pubblico contenente tutti i codici ABI e CAB
delle banche italiane

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Per completare la sua analisi Luigi passa alla vera e propria
attività di discovery andando a comporre la pagina con cui
rappresenterà i risultati. Partendo da una pagina vuota,
seleziona i componenti da una ampia libreria che è
comunque possibile estendere con componenti custom

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Questa è la pagina completa costruita da Luigi che
può ora procedere nelle proprie investigazioni
selezionando secondo le proprie esigenze i dati

28. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 28
Selezionando solo i bonifici in uscita, che
contengono almeno una delle parole chiave ed
effettuati da clienti nella fascia di reddito da 30.000
a 50.000 verso tre primarie banche, Luigi identifica i
clienti a cui si potrebbero proporre prodotti di
investimento

29. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 29
L’elenco dei clienti individuati è immediatamente disponbile
per ulteriori investigazioni e può diventare un ulteriore Dataset

30. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 30
Luigi decide di proporre al marketing questo target per una
campagna e per far questo genera un nuovo dataset a cui
potrà accedere direttamente il responsabile della campagna

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A Data Lab Demo Story
Example in Banking
Barbara
Campaign Manager

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A Barbara, responsabile della campagna di marketing, viene
notificata la disponibilità di un nuovo dataset con il target a
cui proporre prodotti di investimento. Decide quindi di
analizzare la composizione di questo nuovo target

33. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 33
Barbara decide quindi di creare un nuovo progetto basato
sul dataset che le è stato messo a disposizione

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I dati vengono immediatamente visualizzati lasciando la
possibilità a Barbara di variare la rappresentazione
intervenendo sulle caratteristiche della visualizzazione

35. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 35
Per poter analizzare i dati per provincia di residenza
Barbara aggiunge al progetto un nuovo datasource
scaricato da internet contenente tutti i comuni
italiani

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Selezionando provincia, giacenza media totale e nr clienti
Barbara richiede la visualizzazione geografica del suo target

37. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 37
Per finire Barbara aggiunge un grafico per
visualizzare la giacenza media per titolo di studio.
Selezionando da questo grafico la “fetta” relativa al
titolo di media superiore nelle visualizzazione
gerografica vengono evidenziate le province in cui
risiedono questi clienti

38. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 38
Al termine dell’esecuzione della campagna a
Barbara viene fornito un file con il numero di
contatti effettuati e il totale delle somme investite
per cliente. Anche questo data source viene
aggiunto al progetto per la consuntivazione finale

39. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 39
Con i dati di redemption Barbara aggiunge una nuova
visualizzazione per evidenzare le filiali su cui si sono ottenuti
i maggiori investimenti confrontando anche la giacenza
media di partenza e il numero di contatti necessari per
ottenere il risultato

40. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Use cases & references
Oracle Confidential – Internal/Restricted/Highly Restricted 40

41. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 41Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Moving to proactive
and predictive
• Large Hadron Collider (LHC) is the largest
cryogenics system in the world : 27km, +6000
superconductors; 600M collisions per second
storing 60TB per year
• Monitoring and Diagnostic system:
(temperature, magnetic & electrical fields,
pressure) with Data Discovery on 15GB of
daily log files
• Run predictive maintenance models on
cryogenics faulty valves detection in Oracle
Database using R
• Deployed Oracle Big Data Discovery, Oracle
Database, Oracle Advanced Analytics

42. Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
BigData@CERN : the Data explosion
42
The CERN Accelerator Logging Service is powerful but also brings new challenges due to exploding
datasets vs analysis strong time requirements (seconds)

43. Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
BigData@CERN : the CERN Big Data Solution
43
Accelerator Postmortem Analysis
• Diagnostic on failures
• Continue operations safely
• Interventions Required
• Designed for CERN LHC
• Extended to injectors complex (SPS)
• External Post Operational Checks
• Injection Quality Checks
Main challenges
• Stringent timing constraint
(every 30 seconds)
• High scalability
• Huge data storage
• IO throughput
• Big Data Streaming Analytics

44. Copyright © 2015 Oracle and/or its affiliates. All rights reserved. |
BigData@CERN : Big Data Discovery use cases
44
1. EXPLORATION & DISCOVERY
• Interactive catalog of all data, attribute statistics,
data quality and outliers
• Dashboards and applications
2. TRANSFORMATION
• Use of Spark in Hadoop applying built-in
transformations and proprietary scripts
• Preview of results, undo, commit and replay
transformations
• Data Enrichment:
• Text-based : entity extraction, relevant terms,
sentiment, language detection
• Geo-based : address, IP, reverse
3. COLLABORATION
• Share and bookmark transformed datasets
• Future use of Notebooks

45. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 45Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Potential savings
identified
Data Lab To Find Savings
and Cost Reductions in Health
Care Budget
• United Kingdom’s National Health Service
• Identify billing and identity fraud
• Optimize treatment by reducing use of less
effective medical procedures
• Deployed Oracle Advanced Analytics, and
Oracle Business Intelligence on Oracle
Exadata and Oracle Exalytics
$156M
“With one vendor providing the whole solution, it’s
very easy for us.” - Nina Monckton, NHS BSA

46. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. |
NHS BSA
• Responsible for a third of the NHS budget
• Manages prescription reimbursement
• Delivery of supply chain services to the NHS
• NHS Pensions
Challenges
• 4 million prescriptions processed/day
• 30%+ entered manually
• Need to find drugs misuse and fraud & error
• Unable to monitor best practice (drug
administration versus outcomes at national level)
• Inability to link structured and unstructured data
together

47. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal
Preventing Fraud for European Health
Insurance Card
Analyzing Billions of Records in Minutes
(prescription)
analyzing much larger sets of patient data, the NHSBSA can provide
insight that is helping to improve standards of care
Analyzing Unstructured Text to Measure
Satisfaction
DALL - Data Analytics Learning Laboratory
Data Scientist, Data Consultant , Statisticians ,Data Lab
Coordinator , Information and Data Analyst
Team initially supported by Oracle experts

48. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal
Preventing Fraud for European Health
Insurance Card
Analyzing Billions of Records in Minutes
(prescription)
analyzing much larger sets of patient data, the NHSBSA can provide
insight that is helping to improve standards of care
Analyzing Unstructured Text to Measure
Satisfaction
DALL - Data Analytics Learning Laboratory
Data Scientist, Data Consultant , Statisticians ,Data Lab
Coordinator , Information and Data Analyst
Team initially supported by Oracle experts
Our target for 2015/16 is to highlight at least £200 million of
potential savings for the NHS through the DALL. The
thermometer below shows what :
The DALL, however, isn’t purely about saving money as we can
also provide valuable insight into patient care, safety, probity
and quality within the NHSBSA and wider NHS.

49. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 49Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Served 100K Customers
Digital Couponing: a new
pattern of adoption
• European leader in the design, creation and
management of technology infrastructures
and services for Financial Institutions, Central
Banks, Corporates and Public Administration
bodies
• SIA Group serves customers in 40 countries
and also operates through its subsidiaries in
Hungary and South Africa
• New Initiative to enter into Digital Ecosystem
with Differentiators for New Markets
extending SIA’s value chain
Mobility solution allowing real-time cashback on
product promotions

50. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 50Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Increase in revenue in one
region by tailoring messages
and playing experience
Improve Gaming Experience
with Big Data Analytics
• Manage and analyze up to 300 billion
events per day
• Understand and segment players
• Quickly correct game play problems
• Deployed Oracle Advanced Analytics and
Oracle R Advanced Analytics for Hadoop on
Oracle Big Data Appliance and Oracle
Database Appliance
62%

51. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. | 51