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.
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
• 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
1. Normalized and fully attributed logical data model
2. Business meta data
3. Data-cleansing specifications
4. 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.
accomplishes that goal
A
prototype
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
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
Skills Matrix
Computer Skill
Business Functions
knowledge
Beginning (B)
Advanced (A)
Expert (X)
Beginning (B)
BB
BA
BX
Advanced (A)
AB
AA
AX
Expert (X)
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
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
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
Mandatory
Important
+
+
Optional
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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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