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White Papers & Articles

  • Top 10 questions to ask/Mistakes to Avoid When Building a Data Warehouse or a Meta Data Repository   link arrow

    You’ve read all of the articles, attended all of the conferences, and you’re charged up to start your own data warehousing/business intelligence project and attain that 401% ROI that you’ve heard everyone is getting. You’ve gone to the CFO or the Vice President of Marketing and gained the funding for your enterprise data warehouse. You will have to evaluate and purchase ETL software, OLAP software, all forms of hardware, middleware, and hire expensive consultants. And if that isn’t difficult enough, remember an enterprise data warehouse involves integrating data from a corporate perspective. Therefore you will have to interact with the most senior people in each of your company’s divisions and guide them to consensus. This white paper will address the essential questions that require good answers, BEFORE the data warehouse project can even begin.

  • Iterative and Narrative Data: Common Ground?   link arrow

    By David Marco © 2003

    In the first article in this series (featured in Real World Decision Support, January 2004), we introduced the term iterative data to mean what is commonly labeled “structured data”, and the term narrative data to mean what is commonly referred to as “unstructured data”.

  • Managed Meta Data Environment (MME): A Complete Walkthrough - Part One of Eight   link arrow

    By David Marco © 2003

    Almost every corporation and government agency has already built, is in the process of building, or is looking to build a Managed Meta Data Environment (MME). Many organizations, however, are making fundamental mistakes. An enterprise may build many meta data repositories, or “islands of meta data” that are not linked together, and as a result do not provide as much value (see “Where’s my meta data architecture?” sidebar).

  • Managed Meta Data Environment (MME): A Complete Walkthrough - Part Two of Eight   link arrow

    By David Marco © 2003

    The meta data sourcing layer is the first component of the MME architecture. The purpose of the Meta Data Sourcing Layer is to extract meta data from its source and to send it into the Meta Data Integration Layer or directly into the meta data repository (see Figure 1). Some meta data will be accessed by the MME through the use of pointers (distributed) that will present the meta data to the end user at the time that it is requested.

  • Managed Meta Data Environment (MME): A Complete Walkthrough - Part Three of Eight   link arrow

    By David Marco © 2003

    Often the business meta data for a corporation is stored in the collective conscience of its employees’ “tribal knowledge”. As a result, it is vital for the business users to input business meta data into the repository. The need for active and engaged business users ties into the topic of data stewardship1.

  • Managed Meta Data Environment (MME): A Complete Walkthrough - Part Four of Eight   link arrow

    By David Marco © 2003

    Many companies and government agencies are using some form of messaging and transactions, either Enterprise Application Integration (EAI) or XML (sometimes EAI applications use XML), to transfer data from one system to another. The use of EAI and XML is a popular trend as enterprises struggle with the high cost of maintaining current point-to-point approaches to data integration.

  • Managed Meta Data Environment (MME): A Complete Walkthrough - Part Five of Eight   link arrow

    By David Marco © 2003

    The meta data integration layer (Figure 1) takes the various sources of meta data, integrates them, and load it into the meta data repository. This approach differs slightly from the common techniques used to load data into a data warehouse, as the data warehouse clearly separates the transformation (what we call integration) process from the load process.

  • How to Design and Deliver an Effective Data Assurance Solution   link arrow

    By David Marco © 2005

    Data Assurance is the roadmap by which companies ensure project success when building a data warehousing, establishing and maintaining master reference data and facilitating application deployment or consolidation of legacy systems. In this whitepaper, David Marco describes how the elements of People, Process and Technology, within the Data Assurance framework, are pivotal to any data-centric integration project. It goes on to describe a technology foundation using the Managed Metadata Environment (MME) and the establishment of a Data Stewardship Committee (Team) to institute the best practices of a Data Assurance program.

  • Managed Meta Data Environment (MME): A Complete Walkthrough - Part Six of Eight   link arrow

    By David Marco © 2003

    The Meta Data Management Layer provides systematic management of the Meta Data Repository and the other MME components (see Figure 1). As with other layers, the approach to this component greatly differs whether a meta data integration tool is used or if the entire MME is custom built. If an enterprise meta data integration tool is used for the construction of the MME, than a meta data management interface is most likely built within the product. This is almost never the case; however, if it is not built in the product, than you would be doing a custom build.

  • Managed Meta Data Environment (MME): A Complete Walkthrough - Part Seven of Eight   link arrow

    By David Marco © 2003

    This part of the Meta Data Management Layer defines the criteria for MME purging requirements. Your MME’s purging specific purging requirements and criteria will be governed by its business requirements. As a general rule, meta data that is inaccurate or improperly loaded should be purged; all other meta data should be archived.

  • Managed Meta Data Environment (MME): A Complete Walkthrough - Part Eight of Eight   link arrow

    By David Marco © 2003

    There are two reasons why an MME may need to have meta data marts. First, a particular meta data user community may require meta data organized in a manner other than what is in the Meta Data Repository component. Second, an MME with a larger user base often experiences performance problems because of the number of table joins that are required for the meta data reports.