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Unit 5: Data Modelling - 2022/23 - Assignment 1 (Learning Aim A) (All Criterias Met) (PowerPoint) $11.63   Add to cart

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Unit 5: Data Modelling - 2022/23 - Assignment 1 (Learning Aim A) (All Criterias Met) (PowerPoint)

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2022/23 - This presentation covers DISTINCTION level work for Unit 5: Data Modelling - Assignment 1 - Learning Aim A. All of the following criteria is met in this document: P1 - Explain the stages involved in the decision-making process for data modelling. P2 - Explain how the features of spr...

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  • September 27, 2022
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  • 2022/2023
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Data Modelling –
Learning Aim A

, Introduction

In this assignment, I will be investigating the process of the decision-making process for data
modelling. Therefore, I will first explain the stages in the decision-making process along with
others such as spreadsheet features, considering alternatives, evaluating models and being able
to provide documentation and justifications of the models.
Before you can start designing or creating a spreadsheet for your clients you first need to know
what data modelling is and how it can be applied correctly in fulfilling your client’s needs. Data
modelling is used to help create and consider alternative choices when your client has given you
certain tasks that they would like completed.
Data modelling is the process of creating a visual representation of either a whole information
system or parts of it to communicate connections between data points and structures. The goal
is to illustrate the types of data used and stored within the system, the relationships among
these data types, the ways the data can be grouped and organized and its formats and attributes.

, Stages in Decision Making Process
• Understanding the Scenario
• Identifying Information and Sources
• Factors Affecting the Quality of Information
• Analysing the Information
• Identifying Alternatives
• Identifying Consequences of Implementing the Alternatives
• Making and Justifying the Decision
• Communicating decision to others

, Understanding the Scenario
First of all, you need to understand the scenario that the data model is going to be applied to. If
you do not understand the scenario then you will not be able to create an effective data model
as you will not understand where it going to be applied to. This would be a problem for the
client as the data model will not be fully optimized to run their scenario as it was created by
someone who does not fully understand what they were creating it for. You need to know the
reason as to why this data model is being created. These reasons will include who wants the
data model, why they need it and what they would like to show from it. By understanding these
points and knowing the scenario this will allow you to create a spreadsheet which will be used to
its full extent.

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