IBM Netezza Sales Mastery Course for Business Partners

1. Information Management Software 2011 IBM Netezza Sales Mastery Course for Business Partners

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3. The TwinFin™ Appliance – Revolutionizing Analytics • 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 3

4. IBM Netezza is part of IBM Information Management Transactional & Collaborative Applications Business Analytic Applications Analyze Integrate Big Data Master Data www Data Warehouses Structured Data Manage Streams Integrate & Cleanses External Information Sources Data Warehouse Appliances Data Content Streaming Information Govern Security &Privacy LifecycleManagement Quality

5. Netezza is a Halo product -- Value add to IBM portfolio Web Analytics, Marketing Automation, Campaign Management Enterprise performance management, Reporting, Dashboarding, Mobile BI Data Mining, Advanced Analytics, Predictive Modeling, Statistics InfoSphere DataStage, QualityStage, MDM “Powered by Netezza” Business rules management systems, decision governance BigInsights Hadoop and Map-reduce engine, Hadoop Text analytics, UIMA, Unstructured data visualization, Sentiment analysis Content Analytics 5

6. Market Opportunity 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 6

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8. Nearly 70% of data warehouses experience performance-constrained issues of various types. Information Management days for a single query constant tuning “ ” - Gartner 2010 Magic Quadrant specialized resources required months to deploy

9. Information Management The right data warehouse is now mission critical. Data continues to expand exponentially. Analytics are becoming more complex as business demands faster answers.

10. Traditional data warehouses are just too complex They are based on databases optimized for transaction processing—NOT to meet the demands of advanced analytics on big data. • 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 Too long to get answers

11. IBM Netezza’s revolutionary approach The Appliance 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

12. Information Management TwinFin™ The true data warehousing appliance. • 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

13. 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

14. Traditional Data Warehouse Complexity

15. Data Warehousing – Simplified

16. …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… A true appliance drives speed that transforms the business “ ” - SVP Application Development, Nielsen

17. A true appliance drives much easier and faster deployment eHarmony “ 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

18. Our data warehouse team consists of one to two employees that we need once every three months, to do small changes for release verifications. A true appliance drives lower cost of ownership “ ” - Mark Saponar, CIO, iBasis, a KPN Affiliate

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20. Information 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 Streaming Data Hardware-based query acceleration for blistering-fast results Deep Analytics Complex analytics executed in-database for deeper insights

21. Legacy Solution: Move Data to QueryResulting in Significant I/O Bottlenecks Database Server Storage SQL DATA ETL Server I/O DBA CLI I/O I/O I/O I/O CACHE CACHE CACHE 3rd PartyApps High Performance Loader AIX SOLARIS HP-UX Client LINUX WINDOWS Source Systems SQL Data

22. The Netezza Approach -- Asymmetric Massively Parallel Processing™Move the Query to the Data to eliminate I/O limitations ODBC 3.X JDBC Type 4 OLE-DB SQL/92 Netezza TwinFin Appliance AIX SOLARIS HP-UX Client S-Blade 1 LINUX WINDOWS Processor & streaming DB logic SQL Compiler Query Plan Optimize Admin 2 S-Blade Execution Engine Processor & streaming DB logic S-Blade 3 Processor & streaming DB logic Ÿ Ÿ Ÿ High-PerformanceDatabase Engine Streaming joins, aggregations, sorts ETL Server High-Speed Loader/Unloader DBA CLI 960 S-Blade DBOS Front End Source Systems Processor & streaming DB logic 3rd PartyApps Massively Parallel Intelligent Storage Network Fabric SMP Host High Performance Loader

23. S-Blade Data Stream Processing – Move the Query to the Data FPGA Core CPU Core Compression Engine Restrict, Visibility Complex ∑ Joins, Aggs, etc. Project • 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%

24. Advanced Analytics – the Traditional Way Data Warehouse Analytics Grid SAS Data Demand Forecasting ETL SQL ETL Fraud Detection SQL R, S+ ETL C/C++, Java, Python, Fortran, … SQL

25. Advanced Analytics with TwinFin™ Data Warehouse Analytics Grid SAS Data Demand Forecasting ETL SQL ETL Fraud Detection SQL R, S+ ETL C/C++, Java, Python, Fortran, … SQL

26. Advanced Analytics with TwinFin™ SAS Demand Forecasting SQL Fraud Detection C/C++, Java, Python, Fortran, … R, S+ SQL

27. In Database Analytics: Moving application work “In-Database” • 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 • 270 times faster at Catalina • http://www.sas.com/news/preleases/CatalinaNetezza.html • Fuzzy Logix C++ Regression Models & Algorithm Library • Sears Market Basket Analysis 5 minutes at Catalina • Provider Scoring at Humana • 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

28. The IBM Netezza TwinFin™ Appliance Slice of User Data Swap and Mirror partitions High speed data streaming Disk Enclosures SQL Compiler Query Plan Optimize Admin SMP Hosts S-Blades™ (with FPGA-based Database Accelerator) Processor & streaming DB logic High-performance database engine streaming joins, aggregations, sorts, etc. 28

29. IBM Netezza S-Blade • Operates as a logical unit of 1: • 1 Disk • 1 CPU Core • 1 Field Programmable Gate Array (FPGA) Core

30. 12 Specification (single cabinet) • 8 Disk Enclosures • 96 1TB SAS Drives (4 hot spares) • RAID 1 Mirroring • 2 Hosts (Active-Passive): • 2 Quad-Core Intel 2+ GHz CPUs • 7x146 GB SAS Drives • Red Hat Linux 5 64-bit • 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 • 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

31. Predictable, Linear Scalability throughout entire family IBM Netezza TwinFin Appliance Scalability 1 10 ....... Capacity = User Data space Effective Capacity = User Data Space with compression *: 4X compression assumed

32. IBM Netezza = Simplicity • Benefits • Instead 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 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

33. IBM Netezza Simplicity and Effects on TCOTelco Service Provider Deployment “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. 2010 IBM Corporation

34. Traditional Analytics Solution Months IBM Netezza TwinFin Days or Weeks Netezza TwinFin appliance dramatically reduces all three phases of Data Warehousing deployment Time to Deployment Time to Deployment Comparison Final Testing & Production Preparation & Initialization Planning & Installation Traditional DW IBM Netezza Approach Approach

35. Seamless Integration with Enterprise Ecosystem DataIntegration Business Intelligenceand Analytics AdvancedAnalytics andData Mining Certified interoperability with leading applications and tools

36. 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

37. Why IBM Netezza over Conventional DW? Typical Budget Outlay for BI Project Application Administration Infrastructure Budget Allocation with IBM Netezza architecture Application Admin Infrastructure Real $$ Saved • 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

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39. Typical Sales Cycle Onsite POC Planning Ship Machine Onsite Onsite POC Execution Full Disclosure Report Qualification Budget Sponsorship Approvals Lead Generation Account Planning Customer Intro Netezza Positioning nzLaunch Usage Monitoring Capacity Planning Contracts Purchase Order Prospecting Qualification POC • Purchase&Sale Customer Launch 1-2 months 3-4 months 2-3 weeks 2-4 weeks 1-3 weeks 39

40. BI Emergencies: C-Level Pain Points Data Quality Service Profit Churn Satisfaction Response Costs Latency

41. Opportunity Qualifiers Line of Business • 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 IT • 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 41

42. 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 42

43. 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 43

44. 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 44

45. 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 45

46. 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? 46

47. 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 47

48. 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) 48

49. 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” 49

50. 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 50