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