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Business Intelligence

Business Intelligence. Dr. Mahdi Esmaeili. Step 4: Project Requirements Definition. Deliverable Resulting. Application requirements document - Technical infrastructure requirements - Nontechnical infrastructure requirements - Reporting requirements - Ad hoc and canned query requirements

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Business Intelligence

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  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. 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.

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

  13. 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

  14. 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

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

  16. 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

  17. 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

  18. Skills Matrix Computer Skill

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

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

  21. 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.

  22. Meta Data Categories • Business meta data • Technical meta data

  23. Using a Meta Data Repository as a Navigation Tool

  24. Meta Data Classifications

  25. Meta Data Usage by Business People

  26. Meta Data Usage by Technicians

  27. 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 +

  28. Meta Data Repository Challenges

  29. Example of Meta Data in a BI Query

  30. Entity-Relationship Meta Model

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

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