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 - Requirements for source data, including history - High-level logical data model - Data-cleansing requirements - Security requirements - Preliminary SLAs

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

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.

Complementary Data Analysis Techniques integration and consistency standardization and quality

Process Independence of Logical Data Models

Creating an Enterprise Logical Data Model

Data-Specific Business Meta Data Components

Bottom-Up Source Data Analysis

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.

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

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

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

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

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

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

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

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

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

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

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.

Meta Data Categories Business meta data Technical meta data

Using a Meta Data Repository as a Navigation Tool

Meta Data Classifications

Meta Data Usage by Business People

Meta Data Usage by Technicians

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 +

Meta Data Repository Challenges

Example of Meta Data in a BI Query

Entity-Relationship Meta Model

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