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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 rsherman@athena-solutions.com.

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 rsherman@athena-solutions.com and follow him on Twitter at https://twitter.com/rpsherman.

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

(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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iStock_000003552667XSmall.jpgIt's a vicious battle. No, I am not talking about the partisan wars between the Democrats and Republicans, but this battle also impedes progress. Nor am I talking about the decades- or centuries-old rivalries between religious or ethnic groups across the globe, but this battle has been going on within enterprises since their first use of computers.

The battle is between the curious and the cautious regarding the use of technology. The curious would call themselves innovators while the cautious call them reckless. The cautious consider themselves prudent while the curious call them Luddites.

This battle isn't just being fought on the fringes of the technology adoption curve. It's raging smack in the middle of mainstream technology - including technology that's already become a best practice within Fortune 1000 companies.


Posted December 9, 2009 4:57 PM
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bigdoglittledog.jpgI'm not only concerned about hand-coding versus ETL tools;  I'm also concerned that potential buyers of ETL tools and the market in general are only looking at a small number of players in the ETL marketplace.

For many years industry analyst research groups have identified the top two product vendors: Informatica and IBM (from its acquisition of Ascential Software). So, naturally, these two appear on any evaluation shortlist. The rest of the evaluation shortlist usually includes the bundled products (mentioned in my recent posts) that come with the databases, BI tools or applications that the evaluating company already owns. Beyond these usual suspects, other ETL or data integration products are pretty obscure and almost invisible, at least from a general market perspective.

>>>Continue this post on The Data Doghouse

Posted November 17, 2009 9:46 AM
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hand-stop.jpgThis is a continuation of an earlier post that discussed the problems of hand-coding using ETL tools.

What Went Wrong?

There are two aspects of effectively leveraging an ETL tool. First is learning the tool's mechanics. e.g. taking the tool vendors' training either in a class or through their on-line tutorials. Most IT people have no problem learning a tool's syntax.  Since they most likely already know SQL, they learn the tool very quickly.

But the second aspect actually involves understanding ETL processes. This includes knowing the data-integration processes needed to gather, conform, cleanse and transform; understanding not only what is dimensional modeling but why and how do you deploy it; being able to implement slowly changing dimensions (SCD) and change data capture (CDC); understanding the data demands of business intelligence; and being able to implement error handling and conditional processing.

>>>continue reading this post on The Data Doghouse

Posted November 10, 2009 9:34 AM
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