“innovation through prediction” - hybrid cloud big data platform john andrew oracle enterprise...

“Innovation through Prediction” - Hybrid Cloud Big Data Platform

John AndrewOracle Enterprise [email protected]

Learn. Predict. Influence.

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

Safe Harbor StatementThe following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Agenda

Innovation Business Drivers

Use Case Context

Reference Architecture Context

Hybrid Cloud Solution Context

Q&A

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What if …

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1. You knew what products your customers would be the most likely to buy in advance?

2. You could maximize your profits by determining the highest price a customer will pay for a product?

3. You could optimize customer service to resolve concerns proactively before they become issues?

and finally….4. Political parties had a way of determining and influencing

the voters to vote for them?

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. 5

What does it mean for Business…

Increased Customer Satisfaction and

Revenue

Drastically Reduced Business Risk

Increased Efficiency and Productivity


Core business themes and building blocks …

Predicting and Influencing

Model Accuracy Anytime & Anywhere

availability

Time to Value and Economy of Scale


Google Trend Analysis as of Oct 2015

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Predictive AnalyticsPrediction

Big DataFoundation

ModelAccuracy

Cloud Deployment

EconomyOf scale


Hindsight -> Insight -> Foresight

DescriptiveAnalytics

PredictiveAnalytics

PrescriptiveAnalytics

• Forecasting• Trends• Relationships

• Analytics• Models• Semantics

• Reports• Alerts• Discovery

• RDBMS / SQL• (x)OLAP• Warehouse

BusinessExpectation

ArchitecturePattern

• Prediction• Next Best Action• Influencing

• Machine learning• Decision Hub• Optimization

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• Increasing sources of relevant data can boost model accuracy

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Naïve Guess or Random

100%

0% Population Size

Failu

re P

redi

ction

Acc

urac

y

Model with 20 variables



100%

More Data + Variety Data -> Better Predictive Models

Model with “Big Data” and hundreds -- thousands of input variables including:• Customer sentient data• Competitors data• Environmental data• Spatial location data• Long term vs. recent

historical behavior• Sensor data• etc.


Agenda


Use Case Context



Q&A

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Predictive Analytics Use Cases

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Predictive Pricing(Competitive, Dynamic,

and Demand)

Predicting Influence(Product and Customer)

Fraud Prevention(Box tops)


Collecting All Pricing related Data… “Data Wrangling”

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Enterprise Data Non Enterprise Data


“Training” Pricing model … Machine Learning

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Customer Preference

Consumer Price Index

Historical Pricing Avg

Inventory Turnover

Segment ID

CustomerSegment

Producer Price Index

Yes 80% .12 .60 FM HV 78%

No 60% .34 .15 ANP MV 65%

No 65% .12 .30 FR LV 60%

No 50% .18 .35 PUR MV 55%

Yes 78% .16 .70 NUTR HV 80%

No 95% .53 .40 RB LV 90%

No 74% .45 .25 CGF HV 75%

No 70% .66 .38 SFI MV 65%

Have an algorithm determine what is different and the importance of the differences in the green and gray metrics to figuring out preference


Advanced Analytics AlgorithmsMore than just linear regression to help predict the future and discovery relationships

Logistic Regression

Decision Trees

Naïve Bayes

Support Vector Machines

Regression

Linear Regression

Support Vector Machines

Classification

Multi-Layer Neural Networks

Anomaly Detection

One-Class SVM

Attribute Importance

Minimum Description Length

Principal Components Analysis

Clustering

Hierarchical k-Means

Hierarchical O-Cluster

Expectation-Maximization Feature Extraction

Nonnegative Matrix Fact(NMF)

Singular Value Decomposition(SVD)

Collaborative Filtering (LMF)Text Mining

Tokenization

Theme Extraction

Algorithms works across data sets (Relational and Non-Relational)

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Current State Constraints and Gaps

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1. Analytical Challenges

• Misspecification and using a sample to estimate the model

• Resource (memory) constraints of analytical scripts (R scripts)

• Analyze data without help from IT

2. Data Management Challenges

• Complexity and Cost issues resulted using smaller data sets for analytics

• Smaller the data sets, less accurate analytical outcomes

• Data latency issues increased as the result of data exists in multiple places

3. Deployment Challenges

• Up front large CapEx to build and deploy the Platform


Agenda


Use Case Context



Q&A

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f(predictive analytics) = ( unified data +

@scale)predictive models +


Architecture Vision

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Creating an Unified Data + Advanced Analytic Platform for the Era of Big Data and the Cloud

Simple

• Any data size• Any data variety• Any platform

Unify

• Enterprise (All) Data• Analytical Models• User Interaction

Secure

• Control Access• Protect• Integrity

Resilient

• Reliable• Timely• Elastic


Architecture Themes

Architecture FitAs-is Data Discovery

At-source Data AnalyticsAt scale and Performance

Financial FitReduced $ per Model

CapEx to OpEx TransitionLower TCO

Operational FitAutomated Infrastructure

Unified ManagementSimplified Support Model


On-Premises Architecture Pattern – Ingestion to Analytics

Data Ingestion Service

3rd Party Data Cloud Service

csvxls

❶Big Data

Discovery Service

Client R Engine

❸

❺

Big Data Service❷

Enterprise Data Warehouse

❹

Big Data SQL


Cloud Architecture Pattern – Ingestion to Analytics

Big Data Cloud Service

Data Preparation

Cloud Service


csvxls

Client R Engine

❶ ❷

Big Data Discovery Cloud

Service

❸

❺

❹Database Cloud

Service

Big

data

SQ

L


Hybrid Architecture Pattern – Ingestion to Analytics

Big Data Cloud Service

Big Data Preparation

Cloud Service

Big Data Discovery Cloud

Service


csvxlsEnterprise Data

Warehouse

Client R Engine

❶ ❷ ❸

❹

❺


Analytic Reference ArchitectureBuilt it once and use it multiple times

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FoundationalAnalyticalServices

FunctionalAnalyticalServices

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Reference Architecture Data Flow …

• Import and Ingest• Cleanse and Normalize• Repair and Standardize• Classify and Extract• Augment and Enrich• Visualization

Orchestration,Integration,

Lineage,and

Preparation

Data Sources Data Storage and ManagementData Ingestion

Sales &Inventory

Continuous

Data Warehouse

Daily Updates

CompetitorsData

On-demand

ArchiveMainframe

• Public Sources (Free)• 3rd Party Sources (Pay)

OutsideData

Structured

Unstructured

Structured

Structured

Oracle

Teradata

IBM

Data Consumption

• Unified models• Sense and respond• Mobile interaction

Discovery LabBusiness Users

Analytical LabDevelopment

Enterprise Analytics

Analytical Models

DataDiscovery

BI and Analytics

UnifiedSecureAccess

• Search• Visualize• Transform• Share/Subset

• Regression & classification• Anomaly detection• Segment analysis

• Data Movement• Data Access

Data Reservoir (Semi and Unstructured)

Enterprise Data

Data Warehouse(Structured)

Hadoop (HDFS) NoSQL

Meta data

AnalyticEngine

Data MiningAnalyticEngine

In-memory Processing

Spark

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Agenda


Use Case Context



Q&A

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Oracle Analytics as a Service PlatformPrivate, Public and Hybrid Cloud deployment options

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Graph Analytics


Predictive Pricing Hybrid Cloud Solution realization

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Unified Analytical Model Execution

Client R Engine

Oracle Database

User tables

In-dbstats/dm

Database ServerMachine

ORE packagesR Functions

Oracle R Advanced Analytics for Hadoop Oracle DB Advanced Analytics

Big SQL Services

Enterprise RData Mining


Kick Start Your Hybrid Cloud Big Data Strategy

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1. Guiding Success Factors• Integrate business and technology vision (Identify stockholders that will carry

over to implementation). Focus on the next 12 months• Identify your target architecture

₋ Avoid tunneling on one use case• Keep in mind Big Data is not a cure-all

• Hadoop is a complementary to your existing EDW. It could very well be your “System of truth” but most likely not your “System of record”

2. Jump start your Innovation with • Oracle proven reference architecture• Oracle proven Analytical platform• Oracle proven hybrid cloud deployment


Why Oracle Hybrid Cloud Analytical Platform?

Enterprise-Grade Cloud Capabilities

Discover and Predict – Fast

Govern and Secure All Data

Simplify Access to All Data

Performance Integration Availability Elasticity Manageability

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“innovation through prediction” - hybrid cloud big data platform john andrew oracle enterprise...

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