Download Ovum's Report: Achieving Analytic Agility with Data Mashups
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Executive Summary for the Report
The need to react quickly to a rapidly changing business climate is forcing organizations to make faster business decisions. But being able to quickly ask questions and get answers is not a simple task. While business intelligence (BI) tools and applications have emerged as viable solutions, traditional approaches to building and deploying these systems are complex, cumbersome, and expensive and often fail to keep pace with BI end-user demands and expectations. More often than not, resource-constrained IT departments are overburdened with BI requests, sometimes taking days, weeks, or months to fulfill them.
The challenge is to deliver a BI system that breaks the mold of traditional BI solution deployments by removing this IT bottleneck and shifting the analysis to business users. What's needed is a more agile approach that allows business users to self-service their BI needs. Ovum believes a key enabler for achieving self-service BI agility is data mashup technology.
Many BI systems continue to be designed by IT, based on rigid and inflexible data sources. This has created a bottleneck of end-user change requests as business needs constantly change and evolve. The inability of IT to keep up has given rise to "user-centric" system designs that provision business end user with self-service capabilities to satisfy their BI needs and provide relief to overstretched IT departments. This is not a new concept. Just as most motorists expect to pump their own gas without an attendant, corporate business end users increasingly want and expect the right information in their hands at the right time, without the intervention of IT.
BI vendors are making the jump to self-service in different ways. A promising way t o achieve self-service BI is an integration-oriented approach grounded in data mashup technology. Data mashups allow nontechnical business users to easily and quickly access, integrate, and display BI data from a variety of operational data sources, including those that are not integrated into the existing data warehouse, without having to understand the intricacies of the underlying data schemas.
Anatomy of a Data Mashup