Cetas Analytics as a Service for Predictive Analytics

© 2009 VMware Inc. All rights reserved

Predictive Analytics Leads to Successful Recommendations and Direct Revenue Maximization Christos Tryfonas, Ph.D. CTO, Cloud & Big Data Analytics

Web: www.cetas.net Twi)er: @CetasAnaly/cs Blog: www.cetas.net/blog YouTube: www.youtube.com/CetasAnaly/cs

INSTANT INTELLIGENCE

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Transform IT + Transform Business

+ Transform Yourself

Leverage Cetas to Transform Real-Time and Predictive Analytics

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Infrastructure, Apps and now Data…

Private Public

Simplify Infrastructure With Cloud

(IaaS)

Simplify App Platform Through PaaS

Simplify Data with

Analytics as-a-Service

Run

Manage Analyze

Build

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INSTANT INTELLIGENCE Trend: New Data Growing at 60% Y/Y

Source: The Information Explosion, 2009

medical imaging, sensors

cad/cam, appliances, machine data, digital movies

digital photos

digital tv

audio

camera phones, rfid

satellite images, logs, scanners, twi)er

Exabytes of information stored 20 Zetta by 2015 1 Yotta by 2030 Yes, you are part of the yotta generation…

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Data Growth in the Enterprise

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Trend: Value from Data Exceeds Hardware Cost

§  Value from the intelligence of data analytics now outstrips the cost of hardware • Hadoop enables the use of 10x lower cost hardware • Hardware cost halving every 18 months

Big Iron: $40k/CPU

Commodity Cluster: $1k/CPU

Value

Cost

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INSTANT INTELLIGENCE INSTANT INTELLIGENCE

The Big Data Problem

Velocity Volume Variety

10’s of Billions of Daily Records

From Terabytes to Petabytes

Multi-Structured

Value

Business Insights

$

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Trend: Big Data Analytics – Driven by Real-World Benefit

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INSTANT INTELLIGENCE INSTANT INTELLIGENCE Big Data Use Cases

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Big Data Analytics ROI

Source: Nucleus Research http://www.enterpriseappstoday.com/business-intelligence/the-more-pervasive-business-analytics-roi-big-data.html

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INSTANT INTELLIGENCE INSTANT INTELLIGENCE Big Data Technology Stack

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A Holistic View of a Big Data System

Ingestion &

ETL

Real-Time Streams

Unstructured Data (HDFS)

Real Time Database

(HBase, Cassandra)

Big Query, Search, Index

(Google, Spunk, Cetas)

Batch Processing

Real-Time Processing

Analytics

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Cloud Infrastructure

Data Platform

Private Public

Developer Frameworks

The Unified Analytics Cloud Platform

Analytics Tools

vSphere

Database/DataStores Cassandra

Greenplum

HBase Voldemort

HDFS

Data PaaS

PaaS Map Reduce Python

Mahout R

Cloud Foundry

Splunk Cetas/VMware

Spring

Hadoop VMware /Cetas

SAS or IBM SPSS

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SQL or NoSQL Cluster

Unified Analytics Infrastructure

Hadoop Cluster Private

Public

Big SQL

A Unified Analytics Cloud Significantly Simplifies Infrastructure

Hadoop NoSQL

Decision Support Cluster

Analytics L Cluster

§  Simplify • Single Hardware Infrastructure • Faster/Easier provisioning

§  Optimize • Shared Resources = higher utilization • Elastic resources = faster on-demand

access

Analytics Engine

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Predictive Analytics Objectives

§ Optimization of one or more target functions •  Example: Increase Customer Engagement

§  Vertical Specific •  Increase Customer Satisfaction

• Maximize Customer/User Engagement • Minimize Churn Rate

• Maximize Revenue

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Sample Applications of Predictive Analytics

§  eCommerce • Customer interaction • Revenue Maximization •  Inventory management

§ Mobile • Capacity Planning

§  Ad/Publishing •  Ad serving • Customer Retention Management

§ Gaming •  Ad serving •  Virtual Good Placement •  In-App Purchasing

§  IT • Capacity Planning •  Alerting and Diagnosis

Statistical / Regression

Models

Machine Learning

(NN, Bayesian, etc.)

Social Graph Algorithms

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§  Currently predictive analytics is in Silos • Data Scientists w/ SAS, SPSS, Custom models •  Localized/Sampled Datasets

•  Building Models using Open Source Modeling Tools: Hadoop, Mahout, R

§  Actionable Analytics • Recommendation Engines (Examples: Amazon, Netflix)

•  Product Placement Engines

•  Targeted Ad Placement Engines

§  Cloudification of Predictive Analytics •  Analytics-as-a-Service approach

•  Big Data support

§ Overall Objectives • Revenue Maximization

•  Social Penetration and User Engagement

Taking Predictive Analytics to logical conclusion

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Data is Being Stretched

Big Data

Flexible Data

Fast Data

§  Non-structured data §  Developer productivity

§  Petabytes vs. Gigabytes

§  Democratize Analytics

§  Streaming, real-time and Unpredictable

§  Mobile app proliferation

Intelligent Data §  Analytics Apps and

Algorithms §  Discovery, Visibility,

Pattern Extraction

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§  Cloudification of Analytics is happening

§  Predictive Analytics follow the Big Data and Social Web App trends

§  Direct Impact in Monetization from Predictive Modeling and Analytics

Summary