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Rick Sherman

Welcome! In addition to data integration, my BeyeNETWORK blog will include observations on the business and technology of performance management, business intelligence and data warehousing. Most posts will be hosted on my Data Doghouse blog, so feel free to leave comments here or on the Data Doghouse. If you'd like to suggest topics or ask me any questions, please email me at

About the author >

Rick has more than 20 years of business intelligence (BI), data warehousing (DW) and data integration experience. He is the founder of Athena IT Solutions, a Boston-based consulting firm that provides DW/BI consulting, training and vendor services; prior to that he was a director/practice leader at PricewaterhouseCoopers.  Sherman is a published author of more than 50 articles, an industry speaker and has been quoted in CFO and Business Week. He also teaches data warehousing at Northeastern University's graduate school of engineering. You can reach him at and follow him on Twitter at

Editor's Note: More articles and resources are available on Rick's BeyeNETWORK Expert Channel. Be sure to visit today!

June 2010 Archives

(This is part of our ongoing Series of Unfortunate Data Warehousing
and Business Intelligence Events. Click
for the complete series, so far

A fundamental flaw of many business intelligence solutions is recreating
what the company is already using for reporting and analysis. This
takes one of two paths:

1)    The data warehouse is built
using essentially the source systems' data model.

2)    The other end of the spectrum from 3NF is recreating your
current reporting solutions, often data shadow systems or spreadmarts,
that basically flatten out the data.

Click to read the complete post at the Data Doghouse.

Posted June 17, 2010 2:48 PM
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Over the two decades that companies have been designing and implementing business intelligence and data warehousing solutions they have made the same mistakes over and over again.

Who makes these unfortunate mistakes? Newcomers, mostly. But don't be surprised to see experienced practitioners, who made these mistakes as newcomers, keep making the same mistakes.

Click to read the rest of this blog post on the Data Doghouse.

Posted June 16, 2010 2:45 PM
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