Data Architecture and Packaged Implementations Part Two
Every organization should follow a set of guidelines for implementing packaged applications to ensure architectural harmony and avoid numerous problems. Previously I discussed some issues
Every organization should follow a set of guidelines for implementing packaged applications to ensure architectural harmony and avoid numerous problems. Previously I discussed some issues
Business-focused data analysis activities allow for understanding data and correcting any discrepancies, regardless of systems design or implementation Introduction initiative is the first attempt at
Data mining is a powerful analytical activity that can be used with data warehouses and with operational systems, yielding valuable insights. Introduction Data mining is
Metadata supports the conditions that uses of abstraction in data modeling, ensuring lasting and scalable data models When I write on abstraction, I like to
Using package applications without a defined data architecture strategy creates silos of data, making sharing data across applications extremely difficult. Introduction I once worked with
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.
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