January 2002 - Volume 1, Issue 14

IN THIS ISSUE

Featured Article: Data Warehouse Standards   

• Evaluating the BI Environment for Extranet Deployment - Part 2   
• Next Generation BI Means Architecture   
• Relationships Between Dimensions - Part II   
• rolling-parts   
• The Knowledge Sharing Continuum: Understanding Levels of Knowledge Worker Reliance   
• Meta Data Management in the Data Warehouse Environment: A Logical Approach to Meaningful Data Analysis   

• Data Warehouse Standards   
  

  A number of organizations claim to have standards but they are also just guidelines. People will follow those "standards" if they feel like it and if they feel it benefits them. A standard should be "Thou shall" while a guideline is a recommendation, more like "You should if your situation warrants." This article will discuss standards, not guidelines.
• Evaluating the BI Environment for Extranet Deployment - Part 2   
  

  This article is the second 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 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. This second set of questions focus on around the administration, security, reliability and scalability of the BI access environment deployed on the extranet.
• Next Generation BI Means Architecture   
  

  The architecture of business intelligence (BI) products is an increasingly important topic of discussion. By "architecture," we mean whether the product has a client server (2 tier) or an Internet (3 tier) design. Historically, architecture has not been the driving factor in purchase decisions. The major query and reporting (Q&R) vendors' products were originally designed for the desktop and intended as a generic data access and data manipulation tool with limited user-interface customization and application development capabilities.
• Relationships Between Dimensions - Part II   
  

  his article will focus on two additional approaches, followed by a set of guidelines to help determine the most appropriate design option for your situation. I will run through an example of applying these guidelines and arrive at the most appropriate option.
• rolling-parts   
  

  When loading data to a warehouse on of the biggest problems is removing old data to make room for new data. Partitioning is a method used by RDBMS’ to physically isolate data within a single table. It allows you to store related groups of data in a single table but still keep the data physically separated. The biggest advantage is that the individual partitions can be managed, accessed and updated apart from the other partitions. Within a Data Warehouse one of the most common methods of grouping data is by time. This article will attempt to explain how we can partition data by time and automate the replacing of the oldest data with the newest data.
• The Knowledge Sharing Continuum: Understanding Levels of Knowledge Worker Reliance   
  

  The following is a brief and simplistic description of a knowledge-sharing continuum, a tool that can be used to help a company evaluate their current knowledge sharing capabilities and understand the characteristics of moving from being knowledge dependent to knowledge independent to knowledge inter-dependent.
• Meta Data Management in the Data Warehouse Environment: A Logical Approach to Meaningful Data Analysis   
  

  This article examines the advantages and challenges of meta data management in the data warehouse environment and offers an ideal approach to meaningful data analysis.