Challenges to implementing data architecture with packaged applications can be overcome by addressing issues and challenges during design Introduction In a previous article I discussed some issues that a client encountered when trying to implement a packaged application. They were convinced that the business would make the decisions and IT was not going to interfere in their packaged implementation. There
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 that a client encountered when trying to implement a packaged application. They were convinced that the business would make the decisions and IT was not going to interfere in their packaged implementation. There was a
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 bringing business data together from multiple sources in order to make it available across different departments. Organizations that use a traditional system development approach on their data warehouse (DW) projects usually run into severe source
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 often confused with “writing lots of reports and queries,” when in fact data mining activities do not involve any traditional report writing or querying at all. Data mining is performed through a specialized tool, which
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 remind the reader of a very abstract painting I observed in a museum, which consisted of a white dot on a red canvas that represented a city skyline. In my first article in this series,
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 a client where the business and IT did not have a peaceful history. The business made a strategic decision to move to packaged applications. They were convinced that the business would make the decisions and
Physical data models should follow the logical data model as closely as possible, while adding the optimizing items to conform to a specific database and platform. Introduction Physical data modeling involves transforming the logical model from a purely business design to a design optimized to run in a particular environment. Things that must be considered when doing physical modeling include
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 put into production – were unable to be reshaped, the limitations of trying to get information out of applications became
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 data warehouse cannot and should not be built in one Big Bang. Instead, a data warehouse is an evolving system
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 different types of models that represent different types of data, all of which should be “touched” by data administration. The
