Due to its destination and the sophisticated methods of data processing, the enterprise Data Warehouse (DW) is often claimed to be the corporate knowledge repository. However, is corporate knowledge derived only from structured information (data) stored within the organization, during its performance?

Data Warehouse: Corporate Knowledge Repository

Information is now coming centre stage in companies anxious to maintain their competitive advantage in the dot.com economy as electronic business‐to‐business exchanges rapidly emerge. The technical issues of being able to search across a wide range of different database and document types are now being solved through enterprise information portal applications (EIP), often referred to as corporate portals. These provide users with access to internal and external information resources through a customisable Web browser interface. This paper outlines the generic features of EIP applications, and categorises the different approaches being taken by EIP vendors, many of whom are adapting software developed for document management and information retrieval purposes. Emerging roles for information professionals in the deployment of EIP applications are discussed.

Enterprise information portals

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Web Farming for the Data Warehouse

The seven deadly sins

"No man sees far, the most see no further than the end of their noses." It is important to consider the basic strategies that provide a business foundation for the development of a data warehouse. There are four levels of analytical processing that must drive the evolution of the data warehousing process. Data warehouses are not an end in themselves but merely a step on the path to the information data superstore. In the never-ending quest to access any information, anywhere, anytime, an architecture is needed that includes data from both internal and external sources in a variety of formats. It must include operational data, historical data, legacy data. subscription databases, and data from Internet service providers. It must also include easily accessible metadata. Today's businesses require the ability to access and combine data from a variety of data stores, perform complex data analysis across these data stores, and create multidimensional views of data that represent the business analyst's perspective of the data. Furthermore, there is a need to summarize. drill down, roll up, slice, and dice the information across subject areas and business dimensions. These o bjectives cannot be met easily because data is scattered in many types of incompatible structures. A lack of documentation has prevented us from integrating Analytical Processing Requirements _ older legacy systems with newer systems. Although database administrators have long advocated the design of well-documented integrated databases that may be shared across artificial application lines, most organizations have failed to heed the advice. The result is that analysts cannot perform ad hoc analysis across applications without first creating integrated data structures. The Internet has opened up additional business opportunities needing nontraditional categories of information. The World Wide Web has provided us with magazines, travel, professional communication, games, shopping information, and a new set of adventures for everyone. Vendors are consolidating data from many sources into accessible databases and selling access through the Internet. Although the data and its relationships have not been standardized and defined in data directories, search engines have been developed to identify related information. The use of different search engines with the same criteria may yield different results. The need exists for improved Internet software and more accurate and accessible metadata across multiple organizations if we are to address this problem. Information systems need to support at least four levels of analytical processing in modern organizations. The first level consists of …

Data Warehouse: Corporate Knowledge Repository

References

Enterprise information portals

Web Farming for the Data Warehouse

The seven deadly sins

Data Warehouse: Practical Advice from the Experts

The Data Webhouse Toolkit.

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