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Business Intelligence for Business Analysts

Methods, Model and Techniques for Project Success

(33 ratings)
Language:  English
The journey into business intelligence for the business analyst is through the lens of the business where requirements, models and tasks are documented, analyzed and validated.
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The advances in business intelligence open up unique opportunities for small, medium and large organizations in the realm of information to the extent that better decisions can be made with data pulled from a variety of sources to meet corporate business goals and objectives. 

The business analyst plays a key role in the elicitation, documentation, analysis, modelling and presentation of requirements to key stakeholders on a business intelligence project. The journey in business intelligence will open up methods and concepts to help guide you through this growing segment of business.

About the author

Sean possesses an honours degree from Carleton University of Ottawa and Masters in business administration from Delta International University of New Orleans. He is also a certified specialist in SQL Server Business Intelligence.

Having founded Smoothcube Business Analytics Corporation, Sean realizes a dream of providing quality, endorsed education to business analysts around the world.

  1. Stakeholder analysis 
    1. Stakeholder categories 
    2. The tactics explained 
    3. Limitation assessment 
    4. Boundary assessment 
  2. Assessing the Enterprise Architecture 
    1. Investigate the corporate direction and need (EACH Cycle Step #1) 
    2. Study the gaps (EACH Cycle Step #2) 
    3. Validate the solution approach (EACH Cycle Step #3) 
    4. Focus the scope (EACH Cycle Step #4) 
    5. Formulate the case (EACH Cycle Step #5) 
    6. Write the business requirements (EACH Cycle Step #6) 
  3. Strategic Review 
    1. Document review 
    2. Key Data Processes 
  4. The BI Framework Core 
    1. Data Sources 
    2. Data Extraction from Sources 
    3. Data Staging
    4. Data Storage
    5. Data Logic 
    6. Data Presentation 
  5. Cubes, facts and dimensions 
    1. Date and Time Dimensions 
    2. Conceptualizing Time 
    3. Modelling the example using the date dimension 
    4. The Time Dimension 
    5. The value of a time dimension 
    6. Working with time in both formats and concepts 
    7. The Value to Feasibility Matrix 
  6. Presentation Requirements 
  7. The importance of Data profiling 
    1. Data profiling 
  8. The Transitional Factor 
    1. PEST Factors 
  9. Conclusion 
  10. References 
About the Author

Sean David Christopher