A data model that is well-designed is the foundation to creating business intelligence and data warehouse applications that result to a significant business value. It is the key to success in Business Intelligence (BI). It is paramount that the process is business-centered. It starts with the clear understanding of the business, its purposes, and how the data will be used to support the business.

Effective data modeling results in transforming data into an enterprise information resource that is rational, far-reaching and present. Data is transformed from operational or source systems into a data for analysis.

A data model for one line of business is hardly appropriate for another line of business. Only after thoroughly studying an organization can a data model be established to support its Business Intelligence. Usually the classic self-service BI approach usually follows the following approach:

Therefore, to ensure a successful Business Intelligence process, a data model should exist as follows:

Business-Specific: A data model should be developed based on the unique nature of an organization, their data types and the relationships. It should represent and organize the “big data” in the particular line of the organization. It should be based on how data will be inspected and used in an organization.

With Built-in “Intelligence”: A data model should have a built-in “intelligence” through metadata, data dictionaries, hierarchies and inheritances. With such knowledge, much of the “manual” inference work can be taken out of the decision process, therefore, making the Business Intelligence process more productive and competent.

Robust: The data model should be able meticulously and thoroughly represent the business, its data, and the decision process. Anything short of that will not be able to effectively support the Business Intelligence process.

Scalable: The data model should be scalable and modular to support the ever-changing business needs of the organization.

Implementable: The data model should be easily implemented, either through in-house development, or through commercial tools.


One should figure out an organization and its data before modeling the data for Business Intelligence.


Clarence Gitahi

Business Intelligence Consultant

Pathways International

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