Critical Success Factors Deploying Pervasive BI

Critical Success Factors
Deploying Pervasive BI
September 2007
James Markarian Stephen Brobst Jeff BedellInformatica Teradata MicroStrategyChief Technology Of?cer Chief Technology Of?cer Chief Technology Of?cerCritical Success Factors Deploying Pervasive BI
Table of ContentsPervasive Business Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Operational Pervasive BI Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Systems Pivotal Role of the CIO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Pervasive BI Reference Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Decision Repositories: Active Data Warehousing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Tactical Decisions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Pervasive Prioritizing Mixed Workloads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Business Mission Critical Data Warehouses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Intelligence Data Integration Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10What is a Data SLA? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10How do we achieve the Data SLA? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Decision Services - Pervasive BI for Front-Line Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13BI Service Level Agreements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Pervasive Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Delivery Mechanisms for Pervasive BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Decision Services for Pervasive BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Success Factors Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
AcknowledgementsWe would like to thank the support team that made this work possible. It takes a minimum of two marketing staff to keep up with one CTO - and we had three CTOswriting this paper. Many thanks to Dan Graham, Jorge Lopez, Julianna DeLua, AmyPhillips, Tiffany Gumfory, and Cheryl Bowden.
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Pervasive Business Intelligence
Historically, business intelligence and data warehouses have been achieve alignment with corporate objectives. The goal of pervasiveassociated with back of?ce employees. In the 1990s, data was BI applications is to take the data that produced the back of?ce2integrated and loaded nightly, optimized for trend reporting and ROI of more than 400% and deliver it to front-line employees in strategic analysis, and delivered via reporting tools. Back of?ce a form appropriate to their job functions - with similar results.planners running pre-de?ned reports were the bedrock users of There are thousands of business process steps in a typical enter-business intelligence (BI). Over time, knowledge workers evolved prise where pervasive BI insights can be added. What are leadingto demand richer, more diverse insights. As usage matured, companies doing with pervasive BI? Here are a few highlights:requirements to include predictive analytics, event-driven alerts, > Inbound and outbound cross selling by Customer Serviceand operational decision support have become the norm. While Representativesdemand for near real-time information always existed in front > Point-of-sale fraud detection of?ce operational communities, the costs and complexity of > Automated insurance claims triage and fraud detectionloading data multiple times per day kept data out of reach. Thiscaused expert database administrators and i... [download for more]