Data Warehousing & Business Intelligence-as-a-Service Overview

1. Data Warehousing & Business Intelligence-as-a-Service Overview DRAFT

2. Agenda Background Customer Challenges Service Overview Target Customers & Markets Next Steps

3. Situation The Data Warehousing and Business Intelligence markets are large and growing (DW - 10B @ 11% CAGR*, BI - 10B @ 7% CAGR**, Data Integration 1.9B @ 9% CAGR) 50% of enterprise businesses surveyed are considering putting BI/Analytics into the cloud*** Data Warehousing & BI as Services are key market trends for 2011** Complications Historical barriers have existed to deliver DW/BI as a service Internet data transfer rates make have made it time, performance, and cost-prohibitive for large data volumes Regulatory restrictions prevent organizations from storing data outside firewall Perceived risk of data security & control Customer needs and possible solution architectures are diverse Professional Services are key to help customers understand and realize the benefits Background *Source: The 451 Group **Source: Gartner *** Source: IDC Enterprise Panel

4. Data Warehousing + Business Intelligence As a Service • Data Warehousing – “Back End” • Data integration • Extracts data from multiple sources and normalizes it • Data is stored in a form amenable to rapid analysis • Handles the “query execution” needed by Business Intelligence • Business Intelligence – “Front End” • Visualization of actual data • Reports, dashboards • Tools to support analysis (e.g., data mining) • Delivered As a Service • “Up-front” professional services work • Ongoing “hosted” services

5. Customer Challenges (1 of 2) • Usability • Traditional BI solutions are hard to use • Business users are heavily dependent on IT to make changes • Requires a high level of IT sophistication • Cost • Traditional solutions are expensive and do not scale • Maintaining data integrity on an ongoing basis is difficult • Performance • Architecting solutions for high performance is difficult • Data change rates often overwhelm system, leading to stale data

6. Customer Challenges (2 of 2) • Scalability • Data is growing exponentially (60+% CAGR) • *Need* for data is growing rapidly as well (more and more project-driven) • Data sources are multiplying in scale and scope • Implementation Complexity • Implementing data warehouses is complex • Data normalization is hard • Maintaining data integrity on an ongoing basis is difficult as well • Environment Complexity • More and more unstructured data (e.g., from the Web, mobile devices) • Increasing adoption of data outside the enterprise’ control (e.g., SaaS) • Increasing proliferation of tools required for end-to-end solution

7. Service Overview • Infrastructure and software required to run all layers in the BI stack delivered as a service • Data Warehousing (EDW, Data Marts, ODS, etc.) • Data Integration (ETL, Replication, Change Data Capture, Data Virtualization) • Business Intelligence (Reports, Dashboards, Data Visualization, etc.) • “Reference” Platforms that solve common analytic problems • Best-of-breed BI, DW, and Data Integration software platforms virtualized and networked together • Support for structured & unstructured data use cases • Enterprise-grade • Secure, Available, High-performance, Virtualized environments with flex capacity • Platform Management Services • Data management Services – backup, archiving, etc. • Potential longer-term to add BPM/BAM capabilities • Professional services • BI/DW Consulting Architecture & Implementation Services • Leverage SI partnerships and build a consulting business

8. Conceptual Architecture Data Center Customer DW Admin Cloud Platform Data Warehouse Layer BI Layer Customer BI Users & BI Analysts DBMS (Multi-tenant or dedicated, Optimized for analytical workloads) BI Software (Multi-tenant or Dedicated) Customer Premises OR Premises • Reports • Dashboards • Ad-hoc reporting • Data Visualization/Discovery • Data Mining ERP, CRM, other apps (Source Data) • Processes analytical workloads pushed down from BI layer • Stores data loaded from source systems DW Ops VDI VDI VDI VDI VDI VDI VDI VDI VDI VDI VDI VDI VDI VDI VDI VDI VDI VDI VM VM VM VM VM VM Data Integration Data Integration Software (Multi-tenant or Dedicated) SFDC Common Services • Identity Management • Metering/Billing • Portal framework • Extract, Transform, Load • Change Data Capture • Replication • Data Virtualization Customer Data Integration Developer

9. Dimensions of the Service • Multi-tenant and Dedicated Instance options • Committed Storage capacity, flex capacity on-demand for each layer • Bandwidth – shared or dedicated • Source(s) to ETL/DW layers for data movement • Access to ETL, DW, and BI layers for administrators & developers • User access to BI layer for reports, dashboards, ad-hoc analysis & data interaction • SLAs (need to validate via customer interviews) • Performance • X seconds per Y rows, for Z concurrent users • X TB per Y min load • Data transfer volume per unit of time • Availability

10. Target Customers and Markets Customers • Existing customers w/ data on our floor • New prospects w/ data on premises • Adds value to customers of all sizes Markets • New Capability Market / Special Project / Flex Capacity Market • Replacement Market • Expansion Market • Verticals • Cross-industry platform – assess focus areas following customer interviews • Develop key assets per vertical over time (Finance, Retail, Health Care, Energy, etc.)

11. What we’re working on now • Competitive Landscape Analysis • Technology Evaluation • Vendor interviews and working sessions • High-level architecture • Vendor Analysis • Data Warehouse Layer – Greenplum, Vertica, ParAccel • Data Integration Layer – Informatica, Talend, other TBD • BI layer – Tableau, Jaspersoft, other TBD • Preparing RFI • Cost & Revenue Model • Preparing for Customer Interviews • Validating interview questionnaire • Work w/ LOB and Sales to schedule interviews • POC planning for DW layer

12. Security Identity Management Integrity Compliance Regulatory Performance Data latency Data volumes/load times Concurrency Query complexity & volume Network latency Availability Cloud BI & DW - Customer Concerns

13. BI & Data Warehousing Architectural Components

14. Types of Business Intelligence