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Home > Training > Data Warehousing > Business and Systems Analysis Training

Business and Systems Analysis Training

Course#: WA1829

This is a pragmatic workshop using a simple and well-known case study. You complete the course with experience in business and systems. The purpose of busi-ness analysis is to define the business needs, independent of implementation technology and organizational structure. You will learn how to select a suitable pro-ject and project scope. You will learn how to complete a business analysis rapidly. You will learn how business needs are rep-resented in stable data and process models. These are integrated through interaction (or usage) matrices.

You will learn state of the art tech-niques for data model abstraction and proc-ess model creation. But, you will not learn just diagrammatic techniques. Most impor-tantly, they will learn how to run a success-ful business analysis project.

Organized in life cycle order, the workshop covers the principles, methods and techniques of Business Analysis.  The methods taught in this course have been used for years with proven success in doz-ens of organizations -- in private industry and government. The workshop will also show how to apply them to any existing CASE product.

What you will learn
Upon successful completion of this course, the student will be able to understand:

  • A balanced view of business analysis
  • The strong business orientation of busi-ness and systems analysis
  • How to build data models that have sta-bility and integrity
  • How to handle business rules in models
  • How to normalize data models
  • How to balance process models
  • How to integrate data and process models
  • How to refine data and process models
  • How to build analysis models capable of driving logical design
  • How to use data dictionary and CASE tools as a natural part of development
  • How to manage a n analysis project
  • How to control scope, schedule and cost
  • What information gathering techniques to use such as JAD and interviews

Audience
Business and Systems Managers, Busi-ness and Systems Users, Business Systems Analysts, Systems Analysts, Project Man-agers, Project Team Members, Data/Database Administrators.

Prerequisites
There are no pre-requisites.  Everything you need to know about business and systems analysis is taught during the course itself.
 
Duration
4 days

Outline of WA1829 Business and Systems Analysis Training

1. Introduction

 

  • What is Information Engineering
  • The Information Engineering Framework
  • Stages of development
  • Development paths
  • Principles of information engineering
  • What is modeling?
  • Importance of modeling data and process
  • The stability of data
  • Characteristics of good analysis models

2. Overview of business analysis

 

  • Purpose of analysis
  • Deliverables of analysis
  • Characteristics of business analysis
  • The focus on the business
  • The role of modeling
  • The sequence of tasks in business analysis

3. Defining Project Scope

 

  • Selecting candidate projects
  • Identifying participants
  • Discovering events
  • Creating the context model

4. High Level Data Modeling

 

  • Used for scope, management presentations and framework for top-down development
  • Finding primary entities
  • Defining relationships
  • Validating entities
  • Identifying keys

5. High Level Process Modeling

 

  • Introduction to process modeling
  • Definition of a process
  • The seven components of a process
  • Constructing a process model
  • Event Analysis
  • Leveling process models
  • Balancing process models
  • Completion criteria for process models

6. Model Interaction

 

  • Importance of model interaction
  • Issues in model interaction
  • Integrating data and process models
  • Three means of integrating models
  • The CRUD matrix

7. The Phase Review

 

  • Assessing project feasibility
  • Assessing project size and complexity

8. Detailed Level Data Modeling

 

  • Methods of model expansion
  • Detailed modeling rules and constructs
  • Refining keys
  • Types of data
  • Types of entities
    • Primary
    • Associate
    • Characteristic
  • Stabilizing the model

9. Normalization

 

  • What normalization is and is not
  • Rules and steps of normalization
  • Practical tips for normalization
  • Doing normalization thoughtfully

10. Data Views Analysis

 

  • Definition of a data view
  • Sources of views of data
  • Importance of views for
    • Integration with process model
    • Detailed data
    • Detailed processing rules
  • Results of views analysis
  • Relevance of views to design and implementation

11. Data Views Consolidation

 

  • Reasons for model consolidation
  • Rules for consolidation
  • Redundant/inconsistent data
  • Redundant relationships
  • Conflict resolution

12. Detailed Data Model Refinement

 

  • Abstraction techniques
  • Aggregation and generalization
  • Subtyping entities
  • Subtyping roles
  • Bill of materials and recursive structures
  • Handling summary/historical data
  • Final model stabilization rules

13. Detailed Process Modeling

 

  • When process decomposition should cease
  • Definition of an elementary process
  • The ACID test for processes
  • Characteristics of elementary process
  • Balancing rules
  • Process mini-specifications

14. Phase Review

 

  • Review participants
  • Goals of phase review

 

15. The Transition to Design

 

  • The purpose of design
  • The deliverables of design
  • The transition to design
  • Model changes in design
  • Preliminary design
    • The scope of design
    • The automation boundary
    • Assigning processes to processors
    • Assigning data to processors
  • Presentation design
    • Screen flow
    • Screen/report design
  • Data design
    • Safe compromises
    • Aggressive compromises
  • Function design
    • Business logic
    • Reusable logic
  • Interface design
    • Interface data
    • Interface code

16. Conclusion

 

 

 

We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.
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