BIG Data Gartner ITXP 2013 Oracle Approach

Copyright 2013, Oracle and/or its affiliates. All rights reserved. 1

Big Data at Work Information Management

Fernando Lemos

Latin America Vice President, Oracle Technology

Adolfo Abreu

Database and Big Data Business Development

November 6th, 2013

Copyright 2013, Oracle and/or its affiliates. All rights reserved. 2 2

Electrification Changed The World


Datafication Of Thoughts And Opinions

Copyright 2013, Oracle and/or its affiliates. All rights reserved. 4 Copyright 2013, Oracle and/or its affiliates. All rights reserved. 4

Datafication Of Things


Datafication Of Processes


Datafication Of The Natural World


Todays Enterprise Imperatives

CLOUD

SOCIAL MOBILE

Text

67% of staff use a personal mobile device for

work, whether the company

knows it or not

56% of executives believe the cloud will make

their organizations more

agile and competitive.

80% of Fortune 500 executives say enterprise-

wide collaboration is the

key to success

90% of Fortune 500 companies will have a big

data initiative in place by

the end of 2013

BIG

DATA


Trends Driving Big Data, Cloud Computing

GLOBALIZATION

9 Billion

Internet Devices

in 2013

50 Billion

by 2020

DATA

EXPLOSION

90%

Created within

Last Two Years

50X Growth

by 2020

RISE OF

MOBILITY

6 Billion Mobile Subscribers

87% Population

Mobile Data Growing 78% CAGR

SOCIAL IS

BUSINESS

Consumers Driving Experience

26% Post negative comments

86% Now stop doing business

94% Will pay more for great experience

MODERNIZE TO

SURVIVE

Lots of

20 year-old Legacy

Applications

Sources: United Nations / International Telecommunications Union, internetworldstats.com

IDC/EMC 2011 Digital Universe Study, 2010 Digital Universe Decade Study, Data rEvolution Sept 2011, CSC's Leading Edge Forum

Portio Research Mobile Factbook 2012

Facebook Conference

IDC


Gartner: Nexus of Forces Driving Innovation

Leading in Digital World

Digital Industrial Economy

Business

Model, Processes and Moments

Internet of Everything

Automated Judgement

3D Printing


Business Challenges Constraints Impeding Timely Decision Making

Grow Revenue

Time to Market

Service Levels

New Products

Risk Reduction

Reduce Costs

Accelerate Deployments

Simply Management

Improve Efficiency

Reduce Complexity


BI Adoption

Productivity

Better decisions

Efficiency

Simplicity

Technical Enablers & Market Drivers

Market

Drivers

Technology

Enablers


MEDIA/ ENTERTAINMENT

Viewers / advertising effectiveness

COMMUNICATIONS

Location-based advertising

EDUCATION & RESEARCH

Experiment sensor analysis

CONSUMER PACKAGED GOODS

Sentiment analysis of

HEALTH CARE

Patient sensors, monitoring, EHRs

Quality of care

LIFE SCIENCES

Clinical trials

Genomics

HIGH TECHNOLOGY / INDUSTRIAL MFG.

Mfg quality

Warranty analysis

OIL & GAS

Drilling exploration sensor analysis

FINANCIAL SERVICES

Risk & portfolio analysis

AUTOMOTIVE

Auto sensors reporting location, problems

RETAIL

Consumer sentiment

Optimized sales & marketing

LAW ENFORCEMENT & DEFENSE

Threat analysis - social media monitoring, photo analysis

TRAVEL & TRANSPORTATION

Sensor analysis for optimal traffic flows

Customer sentiment

UTILITIES

Smart Meter analysis

Big Data Fills Out the Complete Picture

ON-LINE SERVICES / SOCIAL MEDIA

People & career matching

Web-site optimization

Challenged by: Data Volume, Velocity, Variety in finding Value


MEDIA/ ENTERTAINMENT

Viewers / advertising effectiveness

Cross Sell

COMMUNICATIONS

Location-based advertising

EDUCATION & RESEARCH

Experiment sensor analysis

Retail / CPG

Sentiment analysis

Hot products

Optimized Marketing

HEALTH CARE

Patient sensors, monitoring, EHRs

Quality of care

LIFE SCIENCES

Clinical trials

Genomics

HIGH TECHNOLOGY / INDUSTRIAL MFG.

Mfg quality

Warranty analysis

OIL & GAS

Drilling exploration sensor analysis

FINANCIAL SERVICES

Risk & portfolio analysis

New products

AUTOMOTIVE

Auto sensors reporting location, problems

Games

Adjust to player behavior

In-Game Ads

LAW ENFORCEMENT & DEFENSE

Threat analysis - social media monitoring, photo analysis

TRAVEL & TRANSPORTATION

Sensor analysis for optimal traffic flows

Customer sentiment

UTILITIES

Smart Meter analysis for network capacity,

Big Data Fills Out the Complete Picture

ON-LINE SERVICES / SOCIAL MEDIA

People & career matching

Web-site

optimization

What is the main difference in this data?

Volume, Velocity, Variety

What information will mean to the business?

Value


What Does a Big Data World Look Like? Truck / Motor Manufacturer

What they collect Monitors the complete car

Miles Per Gallon, Driving techniques

Location information

How they use it Better tailored servicing plans

Better targeted marketing

Offer better finance deals or related options

More data for R&D

Sell on to partners

Big Data means The ability to predict when components might fail

Create better service plans based on actual usage

Drivers can post their data to manufacturers website

Black box data can help with accidents


What Does a Big Data World Look Like? Retailer / White Goods

What they collect Food monitoring by RFID tags Fridge monitors

food usage and sell-by dates

How they use it Automated food ordering

Better targeted marketing due to better data

about how/when customer uses products

More detailed information about customers

Better store based demand planning

Big Data means


What are these new big data streams?


What about Fast Data? Turning High Velocity Data into Value

-flight data

-time


Key Value Drivers of Timely, Accurate Action Delivering Tangible Results with Fast Data

Higher Quality

In Operations

Improved

Efficiency

New

Services Better Customer

Experience


Vision: Information, Big Data & Fast Data Filter, Move, Transform, Analyze, and Act at High Velocity

ANALYZE

PRESENT

ACT

MOVE &

TRANSFORM

FILTER &

CORRELATE

ACQUIRE ORGANIZE ANALYZE DECIDE


Use Case: Health Remote Monitoring

Real time health and vital

signs to predict problems

and react fast

Automated alerts to

doctors, hospital & medical

plan companies

Historical patient analysis,

support decision


Patient Ecosystem

DATA GATEWAY

PATIENT

ANALYTICS

CARE

COORDINATION

EMT

HOME

SECURITY

HOSPITAL

ADMISSIONS TELEHEALTH,

REMOTE MONITORING

FAMILY

DOCTOR

HOSPITAL TRAFFIC

MGMT


Unleash Real Time Data Nike+ Back-End implemented on Coherence

In 2011, Nike launched a

complete series of

products with Nike+

devices attached, with

the goal to track physical

activity and help to

improve workouts.

Back-end

requirements grew

significantly

300M+ Tx/day

Linear Scalability

Low Latency Access to Recent Data

Event Driven computing on the Grid

High Availability

Key Benefits

Size: 192 Nodes Cluster / 2TB Cache (>50

caches) and about 180M objects, growing

Grid Workload: GETS 1.2 Billion daily,

PUTS 300 Million daily, Hit% 92.

Grid Figures


400+ on-board sensors

Intensive and massive data collection to be analysed in small windows of time to react and change strategies in real time

Prediction forecasting and scenario analysis

Predictive information on decision makers Mobile devices

Information Real Time Analysis

Oracle Team USA @Americas Cup 34th Edition


The Problem

Ability

To

Produce

Data

Ability To

Use Data >


Structured Data & Big Data

Structured data from applications. Semi-structured Big Data from social

media and logs, sensors, feeds, etc.


Inc

rea

sin

g

Bu

sin

es

s V

alu

e

Information Architecture Maturity

BIG DATA

The Data Warehouse Evolution What are Oracles customers deploying today?

& What could happen tomorrow

Growing

investment here

DATA &

ANALYTICS

DIVERSITY

DATA WAREHOUSE CONSOLIDATE

DATA

DATA MARTS

& What happened yesterday

& What is happening today

Most

are here!

Some are

here


Step 0: Data Warehouse Foundation

Oracle

Database

Acquire Organize Analyze Decide

Dashboard

Ad-Hoc Query Oracle BI

Enterprise

Edition

High Density

Data


Step 1: Deep Analysis of Current Data

Oracle

Database


Dashboard

Ad-Hoc Query

Churn

Locality

High Density

Data

Spatial and

Graph

Advanced

Analytics

Oracle BI

Enterprise

Edition


Step 2: Architect for Volume and Variety

Oracle

Database


Dashboard

Ad-Hoc Query

Churn

Locality

Relationship

Comments

High Density Data

Hadoop

Oracle BI

Enterprise

Edition

Aggregate

Pre-Analyze

Low Density

Batch Data Spatial and

Graph

Advanced

Analytics


Step 3: Architect for Velocity

Acquire Organize

High Density

Data

Oracle

Database

Oracle BI

Enterprise

Edition Hadoop

Low Density

Batch Data

Analyze Decide

Aggregate

Pre-Analyze

Streaming Data

Real Time

Decisions Event

Processing

Act

Model

Spatial and

Graph

Advanced

Analytics

Dashboard

Ad-Hoc Query

Churn

Locality

Relationship

Comments

Recommend

Act


Real Time

Decisions

Step 4: Discover New Information

Acquire Organize

High Density

Data

Oracle

Database

Oracle BI

Enterprise

Edition Hadoop

Low Density

Batch Data

Analyze Decide

Aggregate

Pre-Analyze

Event

Processing

Act

Model

Spatial and

Graph

Advanced

Analytics

Endeca Information Discovery

Dashboard

Ad-Hoc Query

Churn

Locality

Relationship

Comments

Recommend

Act

Discover

Streaming Data


Oracle In-Memory Database

In-Memory Columnar technology seamlessly integrated into

the Oracle database

Fully compatible with ALL Oracle features and applications

Delivers

Fastest Analytics and Reporting

Faster and Simpler Enterprise OLTP

Cost Effective and Simple IM DB for Mainstream users


Oracle In-Database Analytics

Statistical Functions

Data Mining &

Predictive Analytics

Text Mining

Text Search

Graph Analysis

Spatial Analysis

Semantic Analysis

In-Database

MapReduce


Predictions, insights and models in the Database

Any BI tool can access and query using SQL

Integrated spatial mapping can be used to Map predictions

Dashboards can launch parameterized R calculations that can return data or visualizations

Any BI tool or application that supports SQL can take advantage

Integration through SQL and R

Business Intelligence + Advanced Analytics


Application-as-a-Services

Platform-as-a-Service

Database-as-a-Service

Infra-as-a-Service

Meter &

Charge

Optimize

Manag

e

Plan

Setup

Build

Test

Deploy

Monitor

+ + +

DB Multitenant Big Data Management

Engineered

System

In Memory

Technology

Big Data as a Service Reference Architecture


Oracle Big Data Solution Architecture

Stream Acquire Organize Analyze

Oracle BI Foundation Suite

Oracle Real-Time Decisions

Endeca Information Discovery

Decide

Oracle Event Processing Oracle Big Data

Connectors

Oracle Data Integrator

Oracle

Advanced

Analytics

Oracle

Database

Oracle

Spatial

& Graph

Apache Flume

Oracle GoldenGate

Oracle

NoSQL

Database

Hadoop

Oracle R

Distribution


SPARC SuperCluster

Database Appliance

Exalytics In-Memory

Exadata Exalogic Big Data

Appliance

Optimized for

Performance

Engineered Systems The family

Virtual Compute

Appliance

Optimized for

Consolidation


Thomson Reuters Identifying Cross Sell and Upsell Opportunities

Objectives

Maximize cross-sell opportunities

Lower cost and complexity

Solution

Economically capture all customer

activity

Testing 50M events/sec ingest rates into

BDA and Oracle NoSQL DB

Feeds Exadata EDW for customer

profitability & segmentation analysis

Rick King Chief Operating Officer for Technology

Thomson Reuters

toward high performance big data delivery - and

BDA Exadata Exalytics

EDW

Sandbox & DR

Event Capture

& Store

Interactive

Analytics

Research

Applications

Upsell/Cross Sell


Regions Bank Lowering Costs by Simplifying IT Infrastructure

Objectives

Comply with regulations requiring more data

to support stress testing

Reduce IT costs & streamline processing by

eliminating duplicate data stores

Solution

Single, reliable BDA/Exadata-based ODS

supporting all downstream systems

Landing zone & archival repository for both

structured & unstructured data th

- Toyota Global Vision

Operational Data Store Mainframe,

RDBMS, more

BDA Exadata

Agile business

model

All data

De-normalized

& Partial-

normalized

Normalized

Aggregate data

EDW

Oracle Enterprise Manager

Oracle Data Integrator

Data Delivery

Master

S1

Master

S2

Master

Sn SOA/API

CRMS

Other

Fast access to 85% more data

Lower costs, simplified architecture and

fast time to value

Benefits


SoftBank

Objectives

Explain and predict customer behavior

Solution

Capture customer social media activity

words

Correlate social sentiment with customer

service data

Understand what happened, why it

happened and what will happen next

Masayoshi Son Chairman & CEO

SoftBank Mobile Corp.

endeavors to further accelerate the Information

BDA Exadata Exalytics

EDW &

Sandbox

Text &Sentiment

Analysis

Information

Discovery Social Data

Stream

BIG Data Gartner ITXP 2013 Oracle Approach

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