Data Virtualization Usage Patterns for Business Intelligence and Data Warehouse Architectures
Modern organizations are having to react ever more quickly to competitive threats and new potential market opportunities and more than ever before need data which is up to date, comprehensive and easy to access. Business Intelligence has long been a key method for making this available and in recent years the most common method of serving data to this environment has been through the replication of data into a Data Warehouse architecture. Now these BI/DW architectures have to evolve fast to meet the rapidly increasing demands of today's businesses. From a business perspective traditional BI faces the following challenges:
▪ Lack of business agility: rigidity in the traditional BI/DW architecture prevents new or ad-hoc data requests from being fulfilled on time.
▪ Lost revenue opportunities: high latency in processing and delivering important data. ▪ High costs: tightly coupled systems make the modifications required to align to business needs expensive.
▪ Incomplete information for decision making: challenges in accessing disparate enterprise, web and unstructured data.
▪ Limited self-service opportunities: multiple layers of processing make it difficult for business users to operate without significant IT assistance.
Today’s users demand reports with better business insights, more information sources, real-time data, more self-service and want these delivered more quickly, making it hard for the BI professionals to meet such expectations.
In this document we show how the use of Data Virtualization can help BI professionals to accomplish such goals.
