1. Business Intelligence
Dr. Mahdi Esmaeili

2. Step 4: Project Requirements Definition

3. Deliverable Resulting
Application requirements document
- Technical infrastructure requirements
- Nontechnical infrastructure requirements
- Reporting requirements
- Ad hoc and canned query requirements
- Requirements for source data, including history
- High-level logical data model
- Data-cleansing requirements
- Security requirements
- Preliminary SLAs

4. Roles Involved in This Step
Application lead developer
Business representative
Data administrator
Data quality analyst
Meta data administrator
Subject matter expert

5. Step 5: Data Analysis
Data analysis are geared toward understanding and correcting the existing discrepancies in the business data, irrespective of any system design or implementation method.
Data analysis is therefore a business-focused activity, not a system-focused activity.

6. Complementary Data Analysis Techniques
integration and consistency
standardization and quality

7. Process Independence of Logical Data Models

8. Creating an Enterprise Logical Data Model

9. Data-Specific Business Meta Data Components

10. Bottom-Up Source Data Analysis

11. are all business responsibilities—not IT responsibilities.
Data archeology (the process of finding bad data)
Data cleansing (the process of correcting bad data)
Data quality enforcement (the process of preventing data defects at the source)
are all business responsibilities—not IT responsibilities.

13. Deliverable Resulting
Normalized and fully attributed logical data model
Business meta data
Data-cleansing specifications
Expanded enterprise logical data model

14. Roles Involved in This Step
Business representative
Data administrator
Data quality analyst
ETL lead developer
Meta data administrator
Stakeholders (including data owners)
Subject matter expert

16. Step 6: Application Prototyping
There is nothing business people like more than to see their requirements turn into a tangible deliverable they can "touch and feel" very quickly. A prototype accomplishes that goal

17. Best Practices for Prototyping
Limit the scope
Understand database requirements early
Choose the right data
Test tool usability
Involve the business people

18. Types of Prototypes Show-and-Tell Prototype
serves as a demo for management and business people
Mock-Up Prototype
The purpose is to understand the access and analysis requirements and
the business activities behind them
Proof-of-Concept Prototype
The purpose is to explore implementation uncertainties
Visual-Design Prototype
Understand the design of visual interfaces &
Develop specifications for visual interfaces and displays
Demo Prototype
Convey the vision of the BI application to the business people or to external groups.
Test the market for the viability of a full-scale BI application
Operational Prototype
Create an almost fully functioning pilot for alpha or beta use of
the access and analysis portion of the BI application

19. Building Successful Prototypes
Prototype Charter
The primary purpose of the prototype
The prototype objectives
A list of business people
The Data
The hardware and software platforms
The measures of success
An application interface agreement
Guidelines for Prototyping
Skills Survey

20. Prototyping Guidelines
Do not deviate from the basic purpose for which the prototype is being developed.
Develop a working prototype quickly; therefore, keep the scope small.
Acknowledge that the first iteration will have problems.
Frequently demonstrate the prototype to stakeholders.
Solicit and document top-down as well as bottom-up feedback on the prototype.
Ask for ongoing validation of the prototype results.
Continue to cycle between demonstrating and revising the prototype until its functionality is satisfactory to all parties.
Review your prototyping approach and modify it if necessary before proceeding with the next prototype iteration

21. Skills Matrix Computer Skill Business Functions knowledge
Beginning (B)
Advanced (A)
Expert (X) BB BA BX AB AA AX XB XA XX

23. Deliverable Resulting
Prototype charter
Completed prototype
Revised application requirements document
Skills survey matrix
Issues log

24. Roles Involved in This Step
Application lead developer
Business representative
Database administrator
Stakeholders
Subject matter expert
Web master

25. a sale of a product to a customer by an employee.
Step 7: Meta Data Repository Analysis
Meta data describes an organization in terms of its business activities and the business objects on which the business activities are performed.
a sale of a product to a customer by an employee.

26. Meta Data Categories
Business meta data
Technical meta data

27. Using a Meta Data Repository as a Navigation Tool

28. Meta Data Classifications

29. Meta Data Usage by Business People

30. Meta Data Usage by Technicians

31. Meta Data Mandatory Important Optional
Owner
Business data name
Technical data name
Definition
Type and length
Content (domain)
Relationships
Business rules and policies
Security
Cleanliness
Applicability
Timeliness
Origin (source)
Physical location (BI databases)
Transformation
Derivation
Aggregation
Summarization
Volume and growth
Notes

32. Meta Data Repository Challenges

33. Example of Meta Data in a BI Query

34. Entity-Relationship Meta Model

36. Deliverable Resulting
Logical meta model
Meta-meta data
Roles Involved in This Step
Data administrator
Meta data administrator
Subject matter expert