Sponsored by IBM Corp.
How the problems of multi-source data discovery as experienced by a typical business analyst today can be addressed by introducing an integrated information platform—a modern, high-level architecture—that includes data virtualization.
In today’s highly distributed, multi-platform world, the data needed to solve any particular decision making need is increasingly likely to be found across a wide variety of sources. As a result, traditional manual approaches requiring prior collection, storage and integration of extensive sets of data in the analyst’s preferred exploration environment are becoming less useful. Data virtualization, which offers transparent access to distributed, diverse data sources, offers a valuable alternative approach in these circumstances.
This paper describes the problems of multi-source data discovery through the eyes of a typical business analyst, highlighting the difficulties encountered when using traditional manual methods of prior data integration. A modern, high-level architecture—the integrated information platform—is introduced that positions and explains data virtualization. The value of this technology in delivering business insight from data is then presented.
Finally, we explore a new offering, IBM Fluid Query, and the set of data virtualization functionality it offers through IBM PureData System for Analytics, IBM BigInsights and other IBM data management products. This initial offering provides valuable functions and also shows the direction of IBM thinking in this emerging area of data management function and value enablement.