October 2001 - Volume 1, Issue 13

IN THIS ISSUE

Featured Article: The Evolution of the Corporate Information Factory   

• Evaluating the BI Environment for Extranet Deployment - Part 1   
• The Business Intelligence Market: Summer Report Cards   
• Relationships Between Dimensions - Part I   
• Meta Data as a Knowledge Management Enabler   
• Should We Model Everything?   

• The Evolution of the Corporate Information Factory   
  

  Out of the swamp created by applications came the notion that there should be a split between transaction/operational processing and informational processing. In doing so, one environment could support the clerical needs of the corporation and another environment could support the management needs of the corporation. Thus born was the concept of the data warehouse. The applications of yesterday were divided into transaction processing applications and decision support processing. The informational processing centered around a structure called a data warehouse. The data warehouse represented the opportunity for the corporation to create integrated data and to collect historical data.
• Evaluating the BI Environment for Extranet Deployment - Part 1   
  

  This article is the first portion of a two-part series, which examines some of the questions that need to be asked before making a BI access environment accessible to the Internet. These questions, or group of questions, should be used in a feasibility assessment of a BI access environment, whether in existence or being evaluated for purchase, to determine possible risks. These questions are not all encompassing but when answered together they should provide a good sense of whether your BI access environment is ready for extranet deployment. Note, the assumption here is that the user is accessing the BI environment across the Internet through a web browser on their client PC.
• The Business Intelligence Market: Summer Report Cards   
  

  During the 2001 summer months (June 1 to August 31), the NASDAQ fell 15% and appeared ready to test its earlier April lows and levels not seen since 1998. Unfortunately, these numbers could be viewed as encouraging in light of the performance of our publicly traded business intelligence (BI) universe, which plummeted 33% on average. The following is a summary of results for the quarter ended June 30 at a number of the key BI vendors.
• Relationships Between Dimensions - Part I   
  

  In this article, I will discuss Relationships Between Dimensions. I will give an example of this design situation and mention the advantages and disadvantages to using this technique. It is important to note that the context for the technique in this article is for a data mart design. We intuitively apply this technique in our operational designs and possibly in certain areas of our data warehouse architecture, but we tend to overlook this technique when it comes to designing reporting applications. I believe this overlook is due to our image of a data mart data model as a pristine star schema design, where the only relationships are from dimension to fact tables. We will see in this article that sometimes building relationships between dimensions can lead to certain reporting efficiencies.
• Meta Data as a Knowledge Management Enabler   
  

  This article focuses at a high level on the role that meta data plays in knowledge management and how the management of this meta data will enable the company to locate and make use of it's knowledge. This article briefly describes three types of meta data associated with knowledge: stewardship meta data, business meta data, and artifact meta data.
• Should We Model Everything?   
  

  The level of attention paid to a model should be determined by its intended use and place in the enterprise. There are different types of models that represent different types of data, all of which should be "touched" by data administration. The actual procedures depend upon the availability of modeling resources, but every model should, at a minimum, be checked for naming standards, common formats and bundled elements. All requests should be evaluated to make sure we are not unnecessarily creating redundant data stores. These steps are important because often times models that at first appear to be at a purely application level end up containing data that could easily become a corporate data store. Checking for these issues is one of the main roles of data administration.