KDnuggets : News : 2005 : n09 : item6 < PREVIOUS | NEXT >

Features

From: Tiffani M Berra
Date: 26 Apr 2005
Subject: Business Intelligence Benchmark Study

Business Intelligence Benchmark Survey
Your Participation is Very Important!

As you know, business intelligence (BI) is gaining greater attention in the business community. Central Michigan University Research Corporation has been chartered by a group of global corporations including The Dow Chemical Company, Ford Motor Company, and Eli Lilly and Company to conduct a study of BI best practices in large organizations. Issues that will be addressed include:

  • Opinions/Attitudes toward BI
  • The alignment of BI support resources
  • The necessary people and infrastructure required for BI implementation
  • Examples of successful BI adoption methodologies or potential barriers
We know that your time is valuable, so this survey is designed to be completed in about 15 minutes. If you provide us with your contact information after completing the survey, we will send you a copy of the final results when they become available. We are confident you will find this report informative and useful in managing your organization.

The responses of all participants will be kept confidential and used for summary purposes only. Respondents and their organizations will not be identified in any reports that are written based on this study.

If you think others in your organization are also knowledgeable about your company’s BI practices, please forward this message to them so they may also respond. If you have any questions, please contact Tim Pletcher at tim.pletcher@cmich.edu. Thank you for your time and participation.

Please CLICK HERE to complete the survey.

Business Intelligence

For the purpose of this survey, business intelligence is defined as "the (use of) processes and technologies to turn information into insight and action for business gain." (The Data Warehousing Institute, 2004) BI encompasses a broad suite of data analysis tools and analytical approaches such as data mining and mathematical modeling to support data driven decision making.


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