Data Modeling

by Global Knowledge Claim Listing

The business analyst must identify, define and precisely document user requirements. Understanding user needs is a determining factor in an analyst's success. 

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Course Details

The business analyst must identify, define and precisely document user requirements. Understanding user needs is a determining factor in an analyst's success. 

By using logical data modeling, a business analyst can convey requirements in such a way that they are easy to validate. Through templates, all stakeholders can understand the requirements, business rules, and data management methods for a given project.

During this course, business analysts will have hands-on exercises in requirements modeling, using entity-relationship diagrams, super types, and subtypes, as well as attributive and associative entities. Logical data modeling involves working directly with business users to specify their needs.

Students pursuing their certification towards an accredited certificate and/or by a recognized university in Canada must attend at least 90% of the courses. 100 of the class courses, participate in the exercises as well as the knowledge checks on the section, then obtain a minimum mark of 72 p. 100 in an assessment consisting of 25 multiple choice questions.

 

Who Should Wait? 

Systems, business analysts; IT project managers, associate project managers, project managers, project coordinators, project analysts, project leaders, senior project managers, team leaders; product and program managers.

 

What You Will Learn

  • Ways in which logical data models relate to requirements

  • Identification of entities and attributes

  • Determining relationships and business rules

  • Data integrity using normalization

 

The Lesson Plan
1. Introduction To Logical Data Modeling

  • Importance of logical data modeling when dealing with requirements

  • When to use logical data models

  • Relationships between logical and physical data models

  • Logical Data Model Elements

  • Reading a high-level data model

  • Identifying Data Model Prerequisites

  • Identifying information sources for data models

  • Steps in developing a logical data model

 

2. Understanding The Context And Drivers Of A Project

  • Importance of the content of a well-defined solution

  • Uses of the functional breakdown diagram

  • Uses of a Context-Level Data Flow Diagram

  • Identifying requirements sources

    • Functional breakdown diagrams

    • Data Flow Diagrams

    • Using case templates

    • Workflow templates

    • Corporate Rules

    • Status diagrams

    • Class diagrams

    • Other documentation

  • Types of modeling projects

    • Administrative systems for transactions

    • Business Intelligence and Data Storage Systems

    • Integration and consolidation of existing systems

    • Maintenance of existing systems

    • Business Analysis

    • Standard application

 

3. Conceptual Data Modeling

  • Entity discovery

  • Defining entities

  • Documenting an entity

  • Identifying attributes

  • The distinction between entities and attributes

 

4. Conceptual Data Modeling – Identifying Relationships And Business Rules

  • Modeling fundamental relationships

  • Cardinality of Relationships

    • Bijective

    • One to many

    • Multivocal

  • Mandatory or optional relationship?

  • Designation of relationships

 

5. Attribute Identification

  • Discover the attributes of the domain

  • Assign attributes to the relevant entity

  • Name attributes using established naming conventions

  • Attribute Documentation

 

6. Superior Relationships

  • Modeling multivocal relationships

  • Modeling multiple relationships between the same two entities

  • Modeling self-referential relationships

  • Modeling ternary relationships

  • Identifying redundant relationships

 

7. Development Of Logical Data Model

  • Using supertypes and subtypes to manage complexity

  • Using supertypes and subtypes to represent rules and constraints

 

8. Data Integrity Using Normalization

  • Normalizing a logical data model

    • 1st normal form

    • 2nd normal form

    • 3rd normal form

    • Reasons for denormalization

  • Contrast between transactional versus business intelligence applications

 

9. Verification And Validation

  • Verifying the technical accuracy of a logical data model

  • Use of CASE tools to assist verification

  • Verifying the logical data model using other models

    • Data Flow Diagrams

    • CRUD Matrix

  • Montreal Branch

    800 Boulevard René-lévesque West Bureau 990, Montreal

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