ibm netezza sales mastery course
TRANSCRIPT
© 2011IBM Corporation
Information Management
IBM Netezza Sales Mastery Coursefor Business Partners
Information Management Software 2011
© 2011 IBM Corporation
Information Management
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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
© 2011 IBM Corporation
Information Management
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
© 2011 IBM Corporation
Information Management
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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
© 2011 IBM Corporation
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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© 2011 IBM Corporation
Information Management
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
© 2011 IBM Corporation
Information Management
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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
Next Topic
© 2011 IBM Corporation
Information Management
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
Information Management
© 2011 IBM Corporation
Information Management
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Information Management
Data continues to
expand exponentially.Analytics are becoming more complex as
business demands faster answers.
The right data warehouse
is now mission critical.
© 2011 IBM Corporation
Information Management
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
© 2011 IBM Corporation
Information Management
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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
© 2011 IBM Corporation
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.
© 2011 IBM Corporation
Information Management
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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
© 2011 IBM Corporation
Information Management
”“
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
© 2011 IBM Corporation
Information Management
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”“
A true appliance drives
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
© 2011 IBM Corporation
Information Management
”“
A true appliance drives
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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
© 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
Next Topic
© 2011 IBM Corporation
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
© 2011 IBM Corporation
Information Management
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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
© 2011 IBM Corporation
Information Management
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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
© 2011 IBM Corporation
Information Management
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%
© 2011 IBM Corporation
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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
© 2011 IBM Corporation
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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, …
© 2011 IBM Corporation
Information Management
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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, …
© 2011 IBM Corporation
Information Management
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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
© 2011 IBM Corporation
Information Management
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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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Information Management
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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
© 2011 IBM Corporation
Information Management
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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
© 2011 IBM Corporation
Information Management
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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
Information Management
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
© 2011 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
© 2011 IBM Corporation
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Seamless Integration with Enterprise Ecosystem
Certified interoperability with leading applications and tools
DataIntegration
Business Intelligenceand Analytics
AdvancedAnalytics andData Mining
© 2011 IBM Corporation
Information Management
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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
© 2011 IBM Corporation
Information Management
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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
© 2011 IBM Corporation
Information Management
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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
Next Topic
© 2011 IBM Corporation
Information Management
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
© 2011 IBM Corporation
Information Management
Costs
BI Emergencies: C-Level Pain Points
Response
Data QualityService Profit Churn
Satisfaction Latency
© 2011 IBM Corporation
Information Management
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
© 2011 IBM Corporation
Information Management
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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© 2011 IBM Corporation
Information Management
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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© 2011 IBM Corporation
Information Management
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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© 2011 IBM Corporation
Information Management
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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© 2011 IBM Corporation
Information Management
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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© 2011 IBM Corporation
Information Management
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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© 2011 IBM Corporation
Information Management
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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© 2011 IBM Corporation
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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”
© 2011 IBM Corporation
Information Management
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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© 2011 IBM Corporation
Information Management
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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© 2011 IBM Corporation
Information Management
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
© 2011 IBM Corporation
Information Management
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
© 2011 IBM Corporation
Information Management
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Test Drive
TwinFin Your Data. Your Site. Our Appliance.
Information Management
© 2011 IBM Corporation
Information Management
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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
Next Topic
© 2011 IBM Corporation
Information Management
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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
© 2011 IBM Corporation
Information Management
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Case Studies, Videos and More
http://www.IBM Netezza.com/customers/index.aspx
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Financial Services Success: Risk & Credit Management
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Health & Life Sciences Success: Customer Intelligence for Providers
© 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 CompetitionNext Topic
© 2011 IBM Corporation
Information Management
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IBM Netezza Has High Success Rates vs. Oracle & Teradata
NO NO NO NO
NO YES NO LIMITED
© 2011 IBM Corporation
Information Management
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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© 2011 IBM Corporation
Information Management
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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© 2011 IBM Corporation
Information Management
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
© 2011 IBM Corporation
Information Management
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