Monday, February 20, 2006

Data warehouse benchmarks

Recent article from DM Review reports survey of 454 firms engaged in different forms of business intelligence and data warehouse implementations. In particular, the authors focused on the success characteristics of different implementation models. They surveyed 20 DW experts go get a set of metrics. They characteristics fall out into the following categories:

Product Measures
  • Information quality: The data warehouse should provide accurate, complete and consistent information.
  • System quality: The data warehouse should be flexible, scalable and able to integrate data.
  • Individual impacts: Users should be able to quickly and easily access data; think about, ask questions, and explore issues in new ways; and improve their decision-making because of the data warehouse and BI.
  • Organizational impacts: The data warehouse and BI should meet the business requirements; facilitate the use of BI; support the accomplishment of strategic business objectives; enable improvements in business processes; lead to high, quantifiable ROI; and improve communication and cooperation across organizational units.
Development Measures
  • Development cost: The cost of developing and maintaining the data warehouse should be appropriate.
  • Development time: The time to develop the initial version of the data warehouse should be appropriate.
Respondents were asked to respond to a series of detailed questions that probed how successful their efforts had been on the measures described above. One interesting finding was that three of the 5 architectures included in the study scored almost identically. Responding to the following list:
  1. Independent data marts,
  2. Bus architecture with conformed dimensions (bus architecture),
  3. Hub and spoke (i.e., Corporate Information Factory),
  4. Centralized (i.e., no dependent data marts), and
  5. Federated.
Respondents identified independent data marts as the least successful strategy, followed by federated models. The remaining models all scored equally well across the success measures. This goes right to the heart of data warehouse architecture wars. The data does not seem to support arguments that any of the extreme positions on proper DW architecture are well founded.


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