Fit Your Data Architecture To Your Analytical Needs

Delivering business intelligence (BI) effectively depends on a data management architecture that fits your reporting and analytical requirements. Unfortunately, many data warehousing (DW) and BI professionals overlook the need to optimize an end-to-end data management architecture to support diverse BI applications. Well-designed data management architectures optimize such key infrastructure as data warehousing, data integration, data quality, metadata, and master data management. This foundational data architecture can improve — or if implemented poorly, constrain — the flexibility, scale, performance, and ultimately the anticipated ROI of your BI ecosystem. Forrester identifies and evaluates five analytical data architectures to consider, each with distinct advantages and drawbacks: distributed data marts, data federation, enterprise data warehouse, hub-and-spoke, and information-as-a-service (IaaS). Combined with Forrester's Business Intelligence Data Architecture Decision Tool, this report helps information and knowledge management (I&KM) pros determine which architecture is the best fit for their needs.