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Data Modelling Fact Sheet 1 Reasons for data modelling Common data system complaints Simple items of information are held in many places. When customer changes address, this involves manual updates

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What is reasons for data modelling?

Reasons for data modelling are important because they help to identify and understand the underlying structures and relationships within a dataset. Data modelling allows for better organization, analysis, and visualization of data, leading to improved decision-making and problem-solving.

Who is required to file reasons for data modelling?

The responsibility for filing reasons for data modelling typically falls on the data analysts, data scientists, or database administrators who are involved in the data modelling process. They are responsible for documenting the reasons behind the chosen data model.

How to fill out reasons for data modelling?

To fill out reasons for data modelling, one should provide a clear explanation of the objective of the data modelling process, the specific requirements and constraints, the chosen data model, and the rationale behind that choice. It is important to document the considerations that influenced the decision-making process.

What is the purpose of reasons for data modelling?

The purpose of reasons for data modelling is to provide justification and transparency for the chosen data model. It helps stakeholders understand why a particular data model was selected and enables future data model maintenance and enhancements.

What information must be reported on reasons for data modelling?

The reasons for data modelling should include information such as the project objectives, business requirements, technical constraints, stakeholders' feedback, data sources, data model diagrams, and any assumptions or hypotheses made during the modelling process.

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