Business Intelligence

Dr. Mahdi Esmaeili

Step 4: Project Requirements Definition

Deliverable Resulting

1.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.

integration and consistency

standardization and quality

Complementary Data Analysis Techniques

Process Independence of Logical Data Models

Creating an Enterprise Logical Data Model

Data-Specific Business Meta Data Components

Bottom-Up Source Data Analysis

• 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.

1. Normalized and fully attributed logical data model

2. Business meta data

3. Data-cleansing specifications

4. Expanded enterprise logical data model

Deliverable Resulting

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 Prototypeserves as a demo for management and business people

• Mock-Up PrototypeThe purpose is to understand the access and analysis requirements and

the business activities behind them

• Proof-of-Concept PrototypeThe purpose is to explore implementation uncertainties

• Visual-Design PrototypeUnderstand the design of visual interfaces &

Develop specifications for visual interfaces and displays

• Demo PrototypeConvey 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 PrototypeCreate an almost fully functioning pilot for alpha or beta use of

the access and analysis portion of the BI application

Building Successful Prototypes

• Prototype CharterThe primary purpose of the prototypeThe prototype objectivesA list of business people The DataThe hardware and software platforms The measures of success An application interface agreement

• Guidelines for Prototyping

• Skills Survey

Prototyping Guidelines

1.Do not deviate from the basic purpose for which the prototype is being developed.

2.Develop a working prototype quickly; therefore, keep the scope small.

3.Acknowledge that the first iteration will have problems.

4.Frequently demonstrate the prototype to stakeholders.

5.Solicit and document top-down as well as bottom-up feedback on the prototype.

6.Ask for ongoing validation of the prototype results.

7.Continue to cycle between demonstrating and revising the prototype until its functionality is satisfactory to all parties.

8.Review your prototyping approach and modify it if necessary before proceeding with the next prototype iteration

Business Functions knowledge Beginning (B) Advanced (A) Expert (X)

Beginning (B) BB BA BX

Advanced (A) AB AA AX

Expert (X) XB XA XX

Computer Skill

Skills Matrix

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

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 OptionalOwner +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

• Data administrator

• Meta data administrator

• Subject matter expert

Roles Involved in This Step