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Chapter 7 Multi Dimensional Data Modeling Fundamentals of Business AnalyticsContent of this presentation has been taken from BookFundamentals of Business Analytics RN Prasad and Seems Zachary Published
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Identify the business requirements and objectives for the dimensional data model.
02
Identify the entities and attributes that need to be included in the model.
03
Design the star schema, including fact tables and dimension tables.
04
Define primary and foreign keys to establish relationships between tables.
05
Populate the tables with data from the source systems.
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Implement data validation and quality checks to ensure accuracy.

Who needs dimensional data modeling?

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Businesses looking to analyze large volumes of data for decision-making purposes.
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Data warehousing professionals who need to design and maintain data models for reporting and analysis.
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Dimensional data modeling is a design technique used to organize data in a data warehouse. It involves creating a structure that allows for efficient and easy retrieval of information by categorizing data into dimensions and facts.
Dimensional data modeling is typically done by data architects, data analysts, and data engineers who are responsible for designing and maintaining data warehouses.
Dimensional data modeling is filled out by identifying key business processes, determining the dimensions and facts that need to be included, and designing a schema to represent the data.
The purpose of dimensional data modeling is to provide a clear and intuitive way to analyze data in a data warehouse, making it easier for users to understand and retrieve information for decision-making purposes.
Dimensional data modeling typically includes dimensions (descriptive attributes) and facts (measurable data) that are used for analysis and reporting.
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