Aster Discovery Platform Slidecast

INTRODUCING TERADATA ASTER DISCOVERY PLATFORM 6

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Conversations Business and IT Leaders are Having

New types of data present new opportunities

Reduce complexity of big data analytics

Empower existing resources to

generate value from big data

Bring big data analytics to every

enterprise consumer

Gain unmatched competitive

advantage using Big Data

Marketing Executives

Operational Systems

Frontline Workers

Customers Partners

Engineers

Data Scientists

Business Analysts'

Math and Stats

Data Mining

Business Intelligence

Applications

Languages

Marketing

APPLICATIONS

USERS

SQL-H, UNITY, SMART LOADER

CONNECTORS UNITY

VIEWPOINT TVI

MDM

TERADATA UNIFIED DATA ARCHITECTURE

INTEGRATED DATA WAREHOUSE

DISCOVERY PLATFORM

ERP

SCM

CRM

Images

Audio and Video

Machine Logs

Text

Web and Social

SOURCES

TERADATA PORTFOLIO FOR

HADOOP

TERADATA DATABASE

TERADATA ASTER DATABASE

DATA PLATFORM

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Discovery Platform Requirements

1

2

3

4

All Data

Multiple Analytic Methods

Diverse Enterprise Analysts

Rapid Exploration

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All Data: Web Analytics

What do you want to

discover? Recommend Analysis

Influence Analysis

Website Analysis

Satisfaction Metrics

Bus

ines

s Va

lue

TX Data

IX Logs

Review Text

Social Graph

Events Time

Emails Text

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Predictive Analytics

Influencer Analysis (6X)

Percentile Analysis

Churn Analysis

Behavioral Analysis (+25%)

Bus

ines

s Va

lue

Statistics Path

& Time Text SQL Graph Machine Learning

Multiple Analytic Methods: Telco Churn Better Predictive Churn Results

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Diverse Enterprise Analysts

Business Analysts Apps

Data Scientists and Analysts SQL, BI Tools

Developers Workbench, IDE, Library

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Rapid Exploration

Data Scientist

Business User

Rapid Exploration

Fail Fast and Discover More

? Data Acquisition

1

Data Preparation

2

Visualization

4

Analysis

3

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What We are Announced On Oct 8th

“Industry’s First Integrated Big Data Analytics and Discovery Platform”

Aster 6

Teradata Aster Discovery Platform 6 Next Generation Discovery Platform

New Integrated File Store SNAP™ Framework for Discovery

New Integrated Graph Analytic

Engine

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Aster SQL-GR™ Engine Built on a scalable framework to enable Big Graph

Feature •  Native graph processing •  Massively scalable, not bound by memory limits •  Pre-built graph functions •  Integrated with SQL •  Designed for Analytics

Benefits •  Richer insights with powerful Graph processing •  Large scale graph processing with best price

performance •  Brings Graph processing to SQL audience

GRAPH

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Recommendation for Retail

Business Objective Entice shoppers to migrate from low to high margin products Increase cross sell and upsell & improve shopping experience

Opportunity to Impact

•  Increased revenue from recouped lost sales by using behavior based personalized recommendations

•  Improved shopping experience •  Insights for relevant messaging and targeted offers

Challenges •  Multi-structured data in a variety of formats and types •  Large amounts of data – billions of records per month •  Granular analysis not possible due to slow performance •  Traditional techniques require manual and time

intensive coding

Solution •  Use Aster’s Graph Analytic engine to identify bridge products

that lead shoppers from low margin to high margin products •  Products are nodes, transactions are edges •  Perform many to many affinity analysis at any level of granularity

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Use Case: Recommendations – Bridge Products

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Use Case: HR Retention

Fred

Ethel Randy

Ricky

Ella

Ava

Jack

Ryan

Ethan Madison

Lauren

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A New Data Store – Aster File Store™

Based on Common Storage System and Services

Feature •  Purpose-built store to capture and preprocess

data in raw form •  Integral component of Aster architecture •  HDFS compatible storage API

Benefits •  Fast, flexible and efficient multi-structured

data ingest, access and preprocessing •  Easy storage management, access control,

high availability, single cluster co-location •  100% interoperability with Hadoop HDFS

FILE STORE

SNAP™ Framework For Discovery

SNAP™ FRAMEWORK

INTEGRATED OPTIMIZER

INTEGRATED EXECUTER

UNIFIED SQL INTERFACE

STORAGE SYSTEM AND SERVICES

STATS TEXT

T MAP REDUCE SQL GRAPH

FILE STORE COLUMN STORE ROW STORE

PATH

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Detect Customer Churn in Telecommunications

Business Objective Detect and prevent customer churn Enhance churn prediction and find incremental at risk customers

Business Value

•  Incremental revenue from additional high risk churners detected •  Acquisition costs avoided to replace lost customers over same period •  $10’s of millions saved in preventing brand erosion

Challenges •  Siloes of customer data from multiple channels of interaction •  Multi-structured data in a variety of formats and types •  Huge amounts of data of the order of billions of records •  Limited impact of individual analytic techniques used on

their own

Solution •  Detect behavioral churn by seamlessly combining Aster’s Graph

+ Path + Text + Statistical Predictive Analysis Functions •  Analyze ALL channels of interaction to gain a 360° view •  Identify Key Patterns of Behavior that Lead to Churn •  Enable pro-active outreach to save profitable at-risk customers

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Use Case Example - Prevent Churn in Telco

Path Sentiment Statistics Social Graph

Higher Customer Retention

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Aster 6 Delivers Unique Value

• Solve business problems that can not be solved or are difficult to solve with todays tools

• Create deeper insights using multiple analytic methods

• By making it easy to do big data analytics and discovery, brings the science of data to art of business so everyone can be a data scientist

• Fastest Time-to-value; rapid iterations

• Discovery platform that is open, standards-based and extensible to meet your current and future needs

• Plugs seamlessly into your existing infrastructure, leverage existing skills and investments

• Scalable, reliable and manageable

For Business For IT

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Summary - What’s new in Aster 6

New Analytic Engine •  SQL-GR™ Engine

Powerful Big Graph and iterative analytics

Features Benefits

A data store – Aster File Store™ •  Leverage common storage system

and services •  HDFS compatibility

SNAP™ Framework •  Executor and Optimizer •  Common Storage System & Services •  Unified SQL Interface

Quickly and reliably ingest and analyze multi-structured data •  Multi-structured data flexibility, high

performance •  100% HDFS compatible

Standards-based, extensible, integrated, easy and powerful discovery processing and storage

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Thank You!

Questions