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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
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
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
Five Commandments of practical data management can ensure that fundamental best practices are followed consistently Introduction Commandments are like best practices; they are basic performance
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
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
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.
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
Fully architected solutions to data warehouse / analytics systems, not independent data marts, is the right way to ensure all the benefits of integrated data
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
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