October 2002 - Volume 1, Issue 17

IN THIS ISSUE

Featured Article: Latest Trends   

• Use of meta data entities - Part 3 of a series on abstraction   
• Data Architecture and Packaged Implementations, Part One   
• The Business Impact of Knowledge Management   
• Financial Institutions Enhance Systems to Detect Money Laundering   
• Defining Data Mining   

• Latest Trends   
  

  Bucking the grim trend experienced by major software applications companies such as Siebel Systems in the customer relationship management (CRM) industry and i2 Technologies in the supply chain management (SCM) industry, both of which witnessed revenue in the June quarter decline by 15% and 29% from the March quarter, respectively, our business intelligence universe (consisting of Actuate Corporation, Brio Software, Business Objects, Cognos, Hyperion Solutions, Informatica, and MicroStrategy) fared much better.
• Use of meta data entities - Part 3 of a series on abstraction   
  

  The first article explained what abstraction is, the second article explained when to use abstraction, and this article will discuss where to use abstraction. This article starts off discussing the two conditions that need to be present to make abstraction effective. Then we will discuss four applications that can meet these two conditions and therefore make excellent candidates for abstraction.
• Data Architecture and Packaged Implementations, Part One   
  

  A solid enterprise architecture is crucial in a packaged application environment. When basic data quality rules are ignored, mistakes happen that will affect a company for years.
• The Business Impact of Knowledge Management   
  

  Knowledge Management (KM) and Employee Portals (EP) are the wave of the future. Companies are asking

  What is it? Why do we need it? How does it relate to what we are doing already? What does it cost (of course)? From a consultant's perspective, it appears as though knowledge management seldom means the same thing from one company to the next.
• Financial Institutions Enhance Systems to Detect Money Laundering   
  

  A year after the tragic events of September 11, 2001, financial institutions continue to enhance their Anti-Money Laundering ("AML") programs to achieve and monitor compliance with heightened governmental and industry regulations designed to deter, detect, and punish terrorists that use the U.S. and world financial markets. However, automating procedures that would normally be manually performed with extensive human interaction and analysis has proven to be quite challenging as the depth and complexity of these regulations and financial transactions expand.
• Defining Data Mining   
  

  This article is excerpted from a book tentatively titled, BI Roadmap: The Complete Lifecycle for Decision Support Applications (Addison-Wesley, 2002). All material is copyright Addison Wesley Longman, Shaku Atre, and Larissa Moss.