Ibm Netezza Sales Mastery Course

© 2011IBM Corporation

Information Management

IBM Netezza Sales Mastery Coursefor Business Partners

Information Management Software 2011

© 2011 IBM Corporation


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agenda1 IBM Netezza Overview

2IBM Netezza Customer Value and Differentiation

3 Inside the IBM Netezza Twinfin™ Appliance

4IBM Netezza Sales Process and Opportunity Identification

5 IBM Netezza Customer Examples

6 IBM Netezza Advantage over Competition



The TwinFin™ Appliance – Revolutionizing Analytics

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Purpose-built analytics engine

Integrated database, server & storage

Standard interfaces

Low total cost of ownership

Speed: 10-100x faster than traditional systems

Simplicity: Minimal administration and tuning

Scalability: Peta-scale user data capacity

Smart: High-performance advanced analytics



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Content

StructuredData

AnalyzeIntegrate

Govern

Data

Transactional & Collaborative Applications

Manage

StreamingInformation

Business Analytic Applications

Streams

Big Data

Data Warehouses

External Information

Sources

www

Quality

LifecycleManagement

Security &Privacy

Data WarehouseAppliances

Master Data

IBM Netezza is part of IBM Information Management



Web Analytics, Marketing Automation, Campaign Management

Enterprise performance management, Reporting, Dashboarding, Mobile BI

Data Mining, Advanced Analytics, Predictive Modeling, Statistics

Hadoop and Map-reduce engine, Hadoop

DataStage, QualityStage, MDM

Text analytics, UIMA, Unstructured data visualization, Sentiment analysis

Business rules management systems, decision governance

“Powered by Netezza”

BigInsights

InfoSphere

Netezza is a Halo product -- Value add to IBM portfolio

Content Analytics

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Market Opportunity

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The Data Warehousing market (including SW and HW) represents a $16B opportunity, growing 8% per year through 2015

- IDC

“By 2015, at least 50% of enterprises with data warehouses in production will include a data warehouse appliance.”

- Donald Feinberg, Gartner



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Nearly 70% of data warehouses experience performance-constrained issues of various types.

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”“ - Gartner 2010 Magic Quadrant

months to deploy

specialized resources required

constant tuningdays for a single query




9


Data continues to

expand exponentially.Analytics are becoming more complex as

business demands faster answers.

The right data warehouse

is now mission critical.



Too complex an infrastructure

Too complicated to deploy

Too much tuning required

Too inefficient at analytics

Too many people needed to maintain

Too costly to operate

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Traditional data warehouses

They are based on databases optimized for transaction processing—NOT to meet the demands of advanced analytics on big data.

are just too complex

Too long to get answers



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”“Simpler, faster, more accessible analytics

This is what IBM Netezza has done in the data warehousing market: It has totally changed the way we think about data warehousing.

- Philip Howard, Bloor Research

IBM Netezza’s revolutionary approach

The Appliance


Information ManagementInformation Management

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Purpose-built analytics engine

Integrated database, server and storage

Standard interfaces

Low total cost of ownership

Speed: 10-100x faster than traditional system

Simplicity: Minimal administration and tuning

Scalability: Peta-scale user data capacity

Smart: High-performance advanced analytics

TwinFin™The true data warehousing appliance.



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completely transforming the user experience.

Appliances make it simple,

Dedicated device

Optimized for purpose

Complete solution

Fast installation

Very easy operation

Standard interfaces

Low cost



Traditional Data Warehouse Complexity



Data Warehousing – Simplified



”“

A true appliance drives

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…when something took 24 hours I could only do so much with it, but when something takes 10 seconds, I may be able to completely rethink the business…

speed that transforms the business

- SVP Application Development, Nielsen



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”“


much easier and faster deployment

They shipped us a box, we put it into our data center and plugged into our network. Within 24 hours we were up and running. I'm not exaggerating, it was that easy.

- Joseph Essas, Vice President of Technology, eHarmony

eHarmony



”“


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Our data warehouse team consists of one to two employees that we need once every three months, to do small changes for release verifications.

lower cost of ownership

- Mark Saponar, CIO, iBasis, a KPN Affiliate



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Information ManagementInformation Management

Inside the TwinFin™

Optimized Hardware + Software

Purpose-built for high performance analytics; requires no tuning

True MPP

All processors fully utilized for maximum speed and efficiency

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Deep AnalyticsComplex analytics executed

in-database for deeper insights

Streaming DataHardware-based query acceleration

for blistering-fast results



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Legacy Solution: Move Data to QueryResulting in Significant I/O Bottlenecks

Database

CACHE

Server

CACHE

Storage

CACHE

I/O

I/OI/OI/O I/O

SQL Data

SQL

DATA

Source Systems

Client

High Performance

Loader

3rd PartyApps

DBA CLI

ETL Server

SOLARIS

LINUX

HP-UX

AIX

WINDOWS



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The Netezza Approach -- Asymmetric Massively Parallel Processing™Move the Query to the Data to eliminate I/O limitations

Massively Parallel Intelligent Storage

1

2

3

960

ŸŸŸ

Network FabricSMP Host

DBOSFront End

Netezza TwinFin Appliance

High-Speed Loader/Unloader

ODBC 3.XJDBC Type 4

OLE-DBSQL/92

Execution Engine

Execution Engine

SQL Compiler

Query Plan

Optimize

Admin

SQL Compiler

Query Plan

Optimize

Admin

Source Systems

Client

High Performance

Loader

3rd PartyApps

DBA CLI

ETL Server

SOLARIS

LINUX

HP-UX

AIX

WINDOWS

High-PerformanceDatabase EngineStreaming joins,

aggregations, sorts

S-Blade

Processor &

streaming DB logic

S-Blade

Processor &

streaming DB logic

S-Blade

Processor &

streaming DB logic

S-Blade

Processor &

streaming DB logic



S-Blade Data Stream Processing – Move the Query to the Data

FPGA Core CPU Core

Compression Engine Project

Restrict,Visibility

Complex ∑Joins, Aggs, etc.

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• 96 S-Blade Data Processing Streams per cabinet• IBM Netezza processes DB functions in HW, with

compression boosting performance up to 4x • Data I/O is reduced by 95%-98%



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Advanced Analytics – the Traditional Way

Fraud Detection

Fraud Detection

Demand Forecasting

Demand Forecasting

SAS

R, S+

AnalyticsGrid

DataWarehouse

C/C++, Java, Python, Fortran, …

Data

SQL

SQL

ETL

SQL

ETL

ETL



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Advanced Analytics with TwinFin™

Fraud Detection

Fraud Detection

Demand Forecasting

Demand Forecasting

SAS

R, S+

AnalyticsGrid

DataWarehouse

Data

SQL

SQL

ETL

SQL

ETL

ETL

C/C++, Java, Python, Fortran, …



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Advanced Analytics with TwinFin™

SAS

R, S+

SQL

SQL

Fraud Detection

Fraud Detection

Demand Forecasting

Demand Forecasting

C/C++, Java,

Python, Fortran, …



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In Database Analytics: Moving application work “In-Database”

1. Replace a traditional database with Netezza for serving up SAS datasets 50 times faster at Endo Pharma

2. Recode portions of SAS Procedures to Netezza SQL 22 hours on Oracle/IBM RS6000 9 minutes on Netezza

3. Fully embedded partner applications SAS Scoring Accelerator for Netezza

o 270 times faster at Catalinao http://www.sas.com/news/preleases/CatalinaNetezza.html

Fuzzy Logix C++ Regression Models & Algorithm Libraryo Sears Market Basket Analysis 5 minutes at Catalinao Provider Scoring at Humana

o Six weeks (25 SAS jobs on Oracle) 28 minutes (Fuzzy Logix/Netezza)

4. Extension of Code support beyond C/C++ Java, Python, Fortran, R, MapReduce, Hadoop



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The IBM Netezza TwinFin™ Appliance

High-performance databaseengine streaming joins,aggregations, sorts, etc.

SQL CompilerQuery PlanOptimizeAdmin

Processor &streaming DB logic

Slice of User DataSwap and Mirror partitionsHigh speed data streaming

SMP Hosts

S-Blades™ (with FPGA-based

Database Accelerator)

Disk Enclosures



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IBM Netezza S-BladeOperates as a logical unit of 1:

1 Disk1 CPU Core1 Field Programmable Gate Array (FPGA) Core



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12 Specification (single cabinet)

• 8 Disk Enclosures• 96 1TB SAS Drives (4 hot spares)

• RAID 1 Mirroring

• 12 IBM Netezza S-Blades™:• 2 Intel Quad-Core 2+ GHz CPUs• 4 Dual-Engine 125 MHz FPGAs

• 16 GB DDR2 RAM• Linux 64-bit Kernel

• 2 Hosts (Active-Passive):• 2 Quad-Core Intel 2+ GHz CPUs

• 7x146 GB SAS Drives• Red Hat Linux 5 64-bit

• User Data Capacity: 32/128 TB**• Data Scan Speed: 35/145 TB/hr**• Load Speed (per system): 2+ TB/hr

• Power Requirements: 7.6 kW• Cooling Requirements: 26,500 BTU

**: 4X compression assumed



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IBM Netezza TwinFin Appliance Scalability

TF3 TF6 TF12 TF18 TF24 TF36 TF48 TF72 TF96 TF120

Cabinets 1/4 1/2 1 1.5 2 3 4 6 8 10

Processing Units 24 48 96 144 192 288 384 576 768 960

Capacity (TB) 8 16 32 48 64 96 128 192 256 320

Effective Capacity

(TB)*32 64 128 192 256 384 512 768 1024 1280

.......

1 10

Capacity = User Data spaceEffective Capacity = User Data Space with compression *: 4X compression assumed

Predictable, Linear Scalability throughout entire family



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IBM Netezza = Simplicity

NO INDEXES – saves disk space, allows maximum flexibility

NO dbspace/tablespace sizing and configuration

NO redo/physical log sizing and configuration

NO journaling/logical log sizing and configuration

NO page/block sizing and configuration for tables

NO extent sizing and configuration for tables

NO temp space allocation and monitoring

NO RAID level decisions for dbspaces

NO logical volume creations of files

NO integration of OS kernel recommendations

NO maintenance of OS recommended patch levels

NO JAD sessions to configure host/network/storage

Simple partitioning strategies: HASH or ROUND ROBIN

BenefitsInstead of spending time and effort on tedious DBA tasks, use the time for higher BUSINESS VALUE tasks:

• Bring on new applications and groups

• Quickly build out new data marts• Provide more functionality to your

end users


Telecom Call Detail Record FACT (6 billion rows)

Oracle Object Count *

IBM Netezza Object Count

Tables 1 1

Indexes 12

Table Partitions 47

Index Partitions 564

Table Partitions tablespaces 47

Index Partitions tablespaces 47

Table Data Files 170

Index Data Files 122

TOTAL 1,010 1“Look at all the weeks/months worth of effort, DBA design and maintenance that we don't have with IBM Netezza. The appliance claims are true.”

*: Oracle data does not account for ADDITIONAL effort required in configuring and engineering the file system design to accommodate this index management scheme.

IBM Netezza Simplicity and Effects on TCOTelco Service Provider Deployment

33 2010 IBM Corporation



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Time to Deployment

Time to Deployment Comparison

Final Testing & Production

Preparation & Initialization

Planning & Installation

Traditional DWApproach

IBM Netezza Approach

Months

Traditional Analytics Solution

Days or Weeks

IBM Netezza TwinFin



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Seamless Integration with Enterprise Ecosystem

Certified interoperability with leading applications and tools

DataIntegration

Business Intelligenceand Analytics

AdvancedAnalytics andData Mining



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IBM Netezza Analytics Appliance = Value

Network Scale Performance– Predictable, Linear Scalability to meet growing network and data analytics demands

– 10-100x Faster than other solutions…..enables speed of thought analysis

– Extreme Performance for any Ad Hoc query, Predictive Modeling, What-if Analysis

Appliance Simplicity– Simple Installation and Operation……No Configuration, No Tuning, No Indexing

– Perfect Fit as Embedded Technology Component within Partner Solution

Architectural Flexibility– Performance adapts to changing business models

• ad hoc ability to “question everything”

– Avoids brittle architecture that comes with physical database design

– Flexible, Extensive Workload Management Capabilities



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Why IBM Netezza over Conventional DW?

Larger budget allocation for application & asset development– Budget shift to strategic, value added activities– More visibility within the organization– Increased application services with better rates– Reduced low end IT oriented services

Why IBM Netezza? Because . . . – Performance matters– Onsite POCs matter– TCO and ROI matter– Business Results matter

AdministrationTypical Budget Outlay for BI Project

Real $$ Saved

Application Infrastructure

AdminApplication InfrastructureBudget Allocation with IBM Netezza architecture



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Prospecting POCPurchase&Sale

Customer Launch

Typical Sales Cycle

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QualificationBudgetSponsorshipApprovals

QualificationBudgetSponsorshipApprovals

Onsite POC PlanningShip Machine OnsiteOnsite POC ExecutionFull Disclosure Report

Onsite POC PlanningShip Machine OnsiteOnsite POC ExecutionFull Disclosure Report

3-4 months 2-3 weeks 2-4 weeks 1-3 weeks

ContractsPurchase Order

ContractsPurchase Order

nzLaunchUsage MonitoringCapacity Planning

nzLaunchUsage MonitoringCapacity Planning

Qualification

1-2 months

Lead GenerationAccount PlanningCustomer IntroNetezza Positioning

Lead GenerationAccount PlanningCustomer IntroNetezza Positioning



Costs

BI Emergencies: C-Level Pain Points

Response

Data QualityService Profit Churn

Satisfaction Latency



I can’t analyze ALL my data – I have to sample or summarize

I have a report that takes three days to run

I have to “dumb down” the problem to fit the data warehouse

My analyses are conducted on stale and outdated data

I need to involve IT for every new report or query

Opportunity Qualifiers

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I cannot keep up with growing data, users and applications

We regularly miss SLAs for data freshness/availability

Ad-hoc and analytic queries take too long or just not possible

I have a backlog of pending applications projects

I need to do more with less

Line of Business

IT



General Positioning:Benefits of High Performance

Dramatic productivity improvements for analysts

Monthly analysis done in seconds/minutes versus hours, days

Data mining/exploration done over years of historical data, not just days – sampling no longer required

More detailed customer segmentation produces an increase in retention, cross-sell and up-sell capabilities

Revenue enhancing solutions developed from new and more complex analysis

Painless scalability and integration with other systems

The Result: Act at the Speed of Thought

Maximum ROI on Business Intelligence

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Where Do You Hunt?Business Side

Current focus: Telcos, Retailers, Financial Services, Digital Media, Healthcare

Current BI systems are slow to answer Business needs settle on sample data

Management unable to answer important questions from existing data warehouse

Users want answers in seconds and minutes– Existing technology takes hours and days

Business needs to analyze up-to-date data all the time– Want to look at full, detailed data sets, not summaries– Data and queries changing and dynamic– Can’t get what they need from IT

Considering costly upgrade

New Analytic Needs that IBM Netezza now addresses

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Where Do You Hunt? Technology Side

New data mart project in development

Encountering performance challenges

Lots of complex and ad hoc queries

500 GB – 1 PB (lower GB needs to be growing quickly)

Price sensitive

Old technology installations: Sybase customers

Red Brick and Informix customers (end-of-life concerns)

Mid-range Oracle customers: Exadata and all Oracle DW BI projects

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Where Do You Avoid?

Less than 500GB of data, simple queries (SQL-Server tip-off)

Hundreds/thousands of users, lots of short/lookup queries

Just purchased large amounts of competitor’s gear

Feature fights– Warning sign – IT staff asking lots of questions about system adjustment

and tuning

Transaction processing applications (OLTP)– ERP, SFA, customer service, supply chain application support

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Qualifying Questions

Current status/pain– Are you using data warehousing, data mart, or BI technology?– Are you experiencing poor performance or pain in response time with your current

solutions?– Are there questions you would like to ask that can not be processed in the current

environment?– Do lengthy data processing windows hinder your analytical processes?– What front-end BI apps are you using?– Do you have business services/products you wish you could provide?– Are you doing Advanced analytics projects using SAS SPSS or other quantitative

tools?– When did you last update your DW BI systems?

Business issues– Do you feel as though faster business intelligence would make you more competitive?– Are your business intelligence users happy or just accepting what they get?– Are you investigating new technology to reduce the latency in your analyses?– Who evaluates BI technology, and who budgets for it?

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What is a good prospect / IBM Netezza lead

Large Data volumes:– typically always over 1TB, ideally 5TB – Petabyte of user data – if smaller data set (1TB or under), then the prospect must have simplicity as

their #1 objective - small staff, need to lessen TCO, etc.– if smaller data set then we will expand it 10x to 20x for POC

Competitive advantage separates significantly over 20TB (the line of demarcation becomes clear as compared to competition)

Company views data as a corporate asset (competitive advantage)

Must be doing complex analysis

Need to bring an application online fast

“hair on fire” type of scenario – not meeting SLAs

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What is a good prospect / IBM Netezza lead

Key attributes of opportunity are: migration, time to market, and flexibility– no easier system to implement and change due to the low / non-existent

physical modeling of IBM Netezza

Current performance is “ok” but constant tuning is required

Care and feeding costs are high

Installed Oracle and Teradata shops that have hit the limit with existing technology that are using it for analytics, not OLTP

Competing against ankle biters - GreenPlum (EMC), Vertica (HP), AsterData (TD)

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Not a IBM Netezza Lead

Small data size (under 500 GB) - any modern data warehouse platform can optimize and perform under a TB - so unless simplicity is the key issue, walk away

If they continually ask the same questions over and over - "what happened yesterday" without analytics / ad-hoc

High concurrency - OLTP / ODS / and / or just 1,000s of users asking the same type of questions

Low percentage of true ad hoc usage

If there is no sponsor who is willing to change – absolutely need a change agent if it is an ORCL / TD / MSFT / etc. shop.

Not willing to invest several hundred thousand or million plus

Don’t perceive their data as a competitive advantage or key strategy in their business

Be prepared to say “NO”



IBM Netezza Solution Proposal & Proof of Concept (POC)

Solution Proposal is a formal presentation and meeting

A CRUCIAL step in the competitive environment

Objectives:– Gain concurrence on customer pain points– Gain concurrence on vendor rules of engagement for POC (very important)– Establish logistics of the POC– Establish next steps and timelines based on agreed results and metrics

Proof of Concept (POC) Objectives:– Prove IBM Netezza’s claims, convert doubters– Differentiate from the competition, set the bar for the competition– Performed onsite, include IT and User community– Focus on severe performance problem areas, using production volumes– Focus on scenarios customer cannot currently execute

Full Disclosure Report (FDR)– Follows POC, Exec Level meeting to present results and gain concurrence– Position for close and purchase order

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Meaningful POC in 2 Weeks

Customer’s data onsite

“Real production” workload and concurrency

Unfettered access and ad-hoc testing

Out-of-the box performance with minimal tuning

Integration with 3rd party BI, ETL, Backup, etc. tools

No risk to customer

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POC: Query Performance

138X faster

106X faster

POC for a leading direct marketing services company

Configuration: TwinFin vs. Oracle v10.2.3 RAC

Results: TwinFin executes BI queries 89X faster than Oracle RAC

Netezza TwinFin

Oracle RAC



Typical Netezza Win Over Teradata: US Telco

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Completed <1 week

Netezza POC Extended POC

Ran CDR analyticsGenerated $5M in billing revenueLower overall TCO

Teradata POC

"In essence, Netezza had paid for itself prior to being purchased."

- Senior Manager, Enterprise BI



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Test Drive

TwinFin Your Data. Your Site. Our Appliance.




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IBM Netezza Client Base

Digital Media

Financial Services

Government

Health & Life Sciences

Retail / Consumer

Products

Telecom

Other

92% referencable

67% repeat business



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Case Studies, Videos and More

http://www.IBM Netezza.com/customers/index.aspx



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Retail Success: Merchandising & Planning



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Telco Success: Subscriber Data Mgmt



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Digital Media Success: Consumer Insight



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Financial Services Success: Risk & Credit Management



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Health & Life Sciences Success: Customer Intelligence for Providers



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Government Success: Smart Grid



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6 IBM Netezza Advantage over CompetitionNext Topic



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IBM Netezza Has High Success Rates vs. Oracle & Teradata

NO NO NO NO

NO YES NO LIMITED



IBM Netezza is better value than Teradata

Teradata Results In IBM Netezza Client Advantage

Costs

High initial cost

Lots of professional services

Lots of administration

High cost of ownership

Low initial cost

Little administrationLow total cost of ownership

SmartLimited analytics pushdown

Analytics causes resource contention

Poor analytic performance

Minimal contention due to analytics

More customers benefit from faster analytics

SimplicityConstant tuning for performance

Needs much administration

Difficult and slow to provide business

value

True applianceNo tuning

Faster time to value

Speed

Old inefficient legacy code

Complex workload partitions

Data warehouse performance doesn’t scale consistently

Designed for balanceHighest / most consistent data

warehouse and advanced analytics performance

Architecture

Proprietary interconnect

Virtualized MPP nodes (vAMPs)

Separating compute and storage

Unpredictable performance

True MPP

FPGA acceleration

Best architecture for data warehouse and advanced

analytics

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IBM Netezza is Better Value than Oracle Exadata

Oracle Exadata Results In IBM Netezza Client Advantage

CostsHigh initial cost

Lots of administration

High total cost of ownership

Low initial cost

Little administrationLow total cost of ownership

Smart

Limited analytics pushdown

Inefficiency of Oracle Real Application Clusters (RAC)

Poor analytic performance

Extensive analytics Pushdown capabilities

Fast time to insightMore users benefit

from faster analytics

Simplicity

Complexity of Oracle RAC

Constant tuning for performance

Complex patch process

Complex administration

True applianceNo tuning

Faster time to value

ScalabilityNo proof points on scaling

RAC scalability bottleneck

Business growth risk

Proven scalabilityBusiness growth with

confidence

Speed

Designed for OLTP

RAC is inefficient for data warehouse workloads

Poor data warehouse

performance

Designed for data warehousing

Highest data warehouse performance

ArchitectureClustered SMP database layer

+Shared disk MPP storage layer

Compromised performance

True MPP

FPGA acceleration

Best architecture for data warehousing and advanced

analytics

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Beating Oracle Exadata

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“Exadata is just a RAC cluster on steroids. Something that won't run well in a RAC cluster is not likely to run well on Exadata”

- Oracle customer quoted in Piper Jaffray Note , Oct 2010

Poor DW performance: Tuned for OLTP (e.g. FlashCache)

Complex administration: Complexity of RAC

Poor analytic performance: Very limited push-down analytics

High cost of ownership: High acquisition and admin costs



Winning With IBM Netezza

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86% TwinFin™: the true DW appliance

Key competitive weapon

Differentiated architecture

Very loyal and marquee customer base

Proven winning methodology

Halo product – generates adjacent opportunities



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