- #Data warehouse and business intelligence strategy how to#
- #Data warehouse and business intelligence strategy update#
#Data warehouse and business intelligence strategy update#
This leads to clear identification of business concepts and avoids data update anomalies. Data redundancy is avoided as much as possible. The key point here is that the entity structure is built in normalized form. All the details including business keys, attributes, dependencies, participation, and relationships will be captured in the detailed logical model. There could be ten different entities under Customer. For example, a logical model will be built for Customer with all the details related to that entity. From this model, a detailed logical model is created for each major entity.
This model identifies the key subject areas, and most importantly, the key entities the business operates with and cares about, like customer, product, vendor, etc. The Inmon approach to building a data warehouse begins with the corporate data model. When a data architect is asked to design and implement a data warehouse from the ground up, what architecture style should he or she choose to build the data warehouse? What criteria can help an architect choose between the Inmon or the Kimball architecture? The Inmon Approach This difference in the architecture impacts the initial delivery time of the data warehouse and the ability to accommodate future changes in the ETL design. The key distinction is how the data structures are modeled, loaded, and stored in the data warehouse. They both view the data warehouse as the central data repository for the enterprise, primarily serve enterprise reporting needs, and they both use ETL to load the data warehouse.
#Data warehouse and business intelligence strategy how to#
In terms of how to architect the data warehouse, there are two distinctive schools of thought: the Inmon method and Kimball method. This paper attempts to compare and contrast the pros and cons of each architecture style and to recommend which style to pursue based on certain factors. There are two prominent architecture styles practiced today to build a data warehouse: the Inmon architecture and the Kimball architecture.
The data warehouse, due to its unique proposition as the integrated enterprise repository of data, is playing an even more important role in this situation. We are living in the age of a data revolution, and more corporations are realizing that to lead-or in some cases, to survive-they need to harness their data wealth effectively.
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