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For Application Development & Delivery Professionals

Fit Your Data Architecture To Your Analytical Needs

Forrester's Business Intelligence Data Architecture Decision Tool Helps You Make The Right Choice

July 9, 2009

Authors

  • By Boris Evelson,
  • Rob Karel
  • with James G. Kobielus,
  • Connie Moore,

Why Read This Report

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.

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Table of Contents

  • Don't Shoehorn Analytic Requirements Into Ill-Fitting Architectures
  • Fortunately, Data Architectures Can Be Designed To Fit Most Needs
  • RECOMMENDATIONS

  • Using Forrester's Decision Tool As A Guide, Explore Your Options
  • Supplemental Material
  • Related Research Documents

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