1° seminario oracle crui: analytics data lab, the power of big data investigation and advanced...

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Analytics Data LabThe power of Big Data Investigation and Advanced Analytics to maximize the Data Capital

Roberto Falcinelli Senior Manager - Sales Consulting & Business DevelopmentOracle Business Analytics

24 Gennaio 2017Webinar per Fondazione CRUI

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AbstractAnalytics 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.


Agenda

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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

Oracle Confidential – Internal/Restricted/Highly Restricted 3

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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


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


A change of paradigm in Information Management

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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 Reservoir Data Warehouse


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

Data Lab Catalyst of Innovation Edison’s Invention Factory

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9Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |

Data Comes from Activity

Big Data, as a Global Phenomenon, Is

Disrupting Industries PROCESSES

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


The Pillars and the Process

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= +

Data Lab Ecosystem

Built according to the key requirements.

PillarsTechnology Areas that provides

the required features for current and future needs

ProcessCombines both experimental

approach and production mainstream to maximize the “data

capital”


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


From Pillars to Process

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MainstreamLab

Intra-day consolidated tasks

Innovative tasks not yet releasedCollect 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

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Advanced Analytics Approach

“Data Driven Research” reasoning from the data to the

general theory

Machine Learning on the Process

...

Data Discovery in the LabSource 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 MainstreamThe 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 LabWhen 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


Bring all the pieces into a blueprint

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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

IOTDatabase

+ In-Memory

Data Visualization & Analytics

Services

DataFlow ML


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

sORE on Oracle

databases

ORAAH, Spark MLlib, Big Data Spatial and Graph on BDA

Real Time Decision and Stream Explorer

Big Data Discovery

Optimized for

performance

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


A Data Lab Demo StoryExample in Banking

LuigiAnalista (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


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


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


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


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


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


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


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


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


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


L’elenco dei clienti individuati è immediatamente disponbile per ulteriori investigazioni e può diventare un ulteriore Dataset


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


A Data Lab Demo StoryExample in Banking

BarbaraCampaign Manager


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


Barbara decide quindi di creare un nuovo progetto basato sul dataset che le è stato messo a disposizione


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


Per poter analizzare i dati per provincia di residenza Barbara aggiunge al progetto un nuovo datasource scaricato da internet contenente tutti i comuni italiani


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


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


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


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

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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


BigData@CERN : the Data explosion

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The CERN Accelerator Logging Service is powerful but also brings new challenges due to exploding datasets vs analysis strong time requirements (seconds)


BigData@CERN : the CERN Big Data Solution

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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


BigData@CERN : Big Data Discovery use cases

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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


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

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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

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal

Preventing Fraud for European Health Insurance CardAnalyzing 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

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal

Preventing Fraud for European Health Insurance CardAnalyzing 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.


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


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%

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