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

DRAFT

Background Customer Challenges Service Overview Target Customers & Markets Next Steps

2

Agenda

Background

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

*Source: The 451 Group

**Source: Gartner

*** Source: IDC Enterprise Panel

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

Data Warehousing + Business Intelligence As a Service

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

Customer Challenges (1 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

Customer Challenges (2 of 2)

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

Service Overview

Data Center

Conceptual Architecture

Customer PremisesOR

Premises

DBMS(Multi-tenant or dedicated,

Optimized for analytical workloads)

Data Warehouse Layer

SFDC

Cloud Platform

CustomerData IntegrationDeveloper

CustomerBI Users &BI Analysts

Customer DW Admin

DW Ops

BI Layer

Data Integration

• Reports• Dashboards• Ad-hoc reporting• Data Visualization/Discovery• Data Mining

ERP, CRM, other apps

(Source Data)

BI Software

(Multi-tenant or

Dedicated)

VDIVDIVDI

VM VDIVDIVDI

VM

VDIVDIVDI

VM VDIVDIVDI

VM

Data Integration Software

(Multi-tenant or Dedicated)

VDIVDIVDI

VM VDIVDIVDI

VM

• Extract, Transform, Load• Change Data Capture• Replication• Data Virtualization

• Processes analytical workloads pushed down from BI layer

• Stores data loaded from source systems

Common Services

• Identity Management• Metering/Billing• Portal framework

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

Dimensions of the Service

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.)

Target Customers and Markets

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

What we’re working on now

Cloud BI & DW - Customer Concerns Security

Identity Management Integrity Compliance Regulatory

Performance Data latency Data volumes/load times Concurrency Query complexity & volume Network latency

Availability

BI & Data Warehousing Architectural Components

Types of Business Intelligence

Strategic Tactical Operational

Goal Achieve long-term goals

Manage tactical initiatives

Monitor & optimize operational business processes

Consumers Executives & business analysts

LOB managers & business analysts

LOB managers, operational users, embedded in operational processes

Frequency Monthly, Yearly Daily, Weekly, Monthly

Intra-day, Daily, Event-driven

Type of Data Historical Data Historical Real-time, low-latency & historical data