Introduction In the beginning were applications. And applications served the corporation well until there was a desire for integration of historical information. However, applications supported neither integration nor historical data. When it was noticed that applications – once built and
Effective data warehouse development requires an iterative approach that results in a robust, well-defined and usable system for analytics. This article is excerpted from Data Warehouse Project Management (Addison-Wesley, Adelman and Moss, © September 2000). Data Warehouse Iterations Introduction A
Different models represent and communicate different types of data; all are important to effective data management. Introduction The level of attention paid to a data model should be determined by its intended use and place in the enterprise. There are
Five Commandments of practical data management can ensure that fundamental best practices are followed consistently Introduction Commandments are like best practices (see the DAMA-DMBOK©); they are basic performance statements. In data management there are some practical, fundamental administrative rules for
Developing a process for cost justifying a data warehouse and metrics for measuring various costs associated with a data warehouse project are essential for success This article is excerpted from a book titled Data Warehouse Project Management (published by Addison
Knowing how to measure success and failure, and qualifying results for a data warehouse or analytics project is essential for all project managers. This article is excerpted from a book titled Data Warehouse Project Management (published by Addison Wesley Longman
Knowing how to use operational metadata in a data warehouse, business intelligence or analytics environment can be extremely beneficial to a wide variety of users. Introduction Many data warehouses and analytical systems include metadata management as part of their architectural considerations,
Strategies for implementing operational metadata can improve the design and performance of any data warehouse or business intelligence environment. Many data warehouses and analytical systems include metadata management as part of their architectural considerations, but they do not explore fully
Fully architected solutions to data warehouse / analytics systems, not independent data marts, is the right way to ensure all the benefits of integrated data Introduction The need for an architected solution to decision support / analytics data, as opposed
Independent data marts create stranded, unusable data. Independent data marts can spread like a disease through an organization and must be eradicated. There is a severe disease that has spread to epidemic proportions throughout our society. This disease is particularly
