Volume 3, Issue 2 - April 2009

IN THIS ISSUE

Featured Article: David Marco: Designing the Optimal Meta Data Management Tool   

• Stefan Nguyen: How Meta Data Creates Exponential Value Gains   
• Anne Marie Smith: Business Processes and Logical Process Modeling – An Overview   
• Pete Stiglich: Data Rationalization – The Next Step in Semantic Resolution   
• Douglas Viverito: Meta Data Infrastructure Planning - Part 1   

PREMIUM SPONSORS

• David Marco: Designing the Optimal Meta Data Management Tool   
  

  Many government agencies and corporations are currently examining the meta data management tools in the marketplace to decide which of these tools, if any, meet the requirements for their meta data management solutions. Often times these same organizations want to know what types of functionality and features they should be looking for in this tool category. Unfortunately, this question becomes very complicated as each tool vendor has their own personalized “marketing spin” as to which functions and features are really the most advantageous. This leaves the consumer with a very difficult task indeed, especially when it seems like none of the vendor’s tools fully fit the requirements that your meta data management solution requires. At EWSolutions we have several clients that have these exact same concerns about the tools in the market.
• Stefan Nguyen: How Meta Data Creates Exponential Value Gains   
  

  Information value comes from the differentiation that it effects in decisions, actions, and understanding. The information consumption cycle starts with the specification of information needed and ends with the consumption of information for a net gain.
• Anne Marie Smith: Business Processes and Logical Process Modeling – An Overview   
  

  To enable an information system to provide optimum benefits, the architect, designer and programmers must thoroughly understand the data and processes needed. An excellent way to gain this understanding and prepare to implement software is to carefully and completely model business processes and the relevant data.
• Pete Stiglich: Data Rationalization – The Next Step in Semantic Resolution   
  

  With the Web 2.0, ontologies are being used to improve search capabilities and make inferences for improved human or computer reasoning. By relating terms in ontology, the user doesn’t need to know the exact term actually stored in the document. Data Rationalization is a Managed Meta Data Environment (MME) enabled application which creates/extends an ontology for a domain into the structured data world, based on model objects stored in various models (of varying levels of detail, across model files and modeling tools) and other meta data. Ontology is “the study of the categories of things that exist or may exist in some domain” . Ontology is comprised of “a collection of taxonomies and thesauri” about a domain. Data Models, often unknowingly, express many aspects of ontology, even though they are not stored in OWL or RDF.
• Douglas Viverito: Meta Data Infrastructure Planning - Part 1   
  

  This series of articles will focus on the planning aspects of implementing a Managed Meta data Environment (MME) infrastructure. While the more popular topics (Total Cost of Ownership (TCO), Return on Investment (ROI), Key Performance Indicators (KPI), etc.) could be the focus, these articles will move through the layers from a physical aspect of planning the infrastructure covering two layers in each article. The most important aspect is the “Big Picture”, keeping sight of the overall objective of the MME program and the fact that all the users will eventually become subscribers of the MME.