1. School of Business Administration
Data Modeling
Professor Chen
School of Business Administration
Gonzaga University
Spokane, WA 99258

2. Data Modeling
The data model is a cornerstone for every information system, because it describes the entities that the system will create and maintain during its lifetime.

3. MODELING ENTERPERISE DATA
Enterprise View
Coporate
strategies/
goals etc.
Congruent
Conceptual
Data Models
Business
Needs
Logical Models
Maintenance
Satisfy
Physical Models
ProductionSystem
Implement

4. Data Modeling (Cont.)
Building the data model is probably the most important activity during requirements definition, because in the process of understanding how the data is organizaed and identifying the relationships that exist between entities, you can discover most of the functionality that the system will satisfy.

5. Business area of Data Model
The data model of a business area tends to be relatively stable, compared, for example, to the set of operational procedures or organizational structure, which changes frequently.
Therefore, basing the implementation of the future system upon a well-defined data model is a good first step towards developing a system that meets the real requirements of the users.

6. Techniques and Approaches
Two techniques are used interchangeably to model the data of a system:
Entity relationship model (E-R/M)
Data normalization

7. E-R/M and Normalization
E-R/M (and EE-R/M) aims at identifying the entities that are part of the system, the attributes that make up these entities, and the dependencies between entities.
Normalization makes the data model created using the E-R more robust and extends the life of systems based on the model.

8. E-R and Normalization (Cont.)
These two techniques go hand in hand and should be applied conscientiously during data modeling activities.

9. Approaches to Data Modeling
Two approaches to data modeling:
Top-down
Bottom-up

10. Top-Down vs. Bottom-Up Approach
Identify Entities
Identify Entities
Discover Relationship
Discover Relationship
Define Attributes
Analyze/synthesize Data
Define Attributes
Collect Data

11. Top-Down vs. Bottom-Up Approach
Identify Entities
Identify Entities
Discover Relationship
Discover Relationship
Define Attributes
Analyze/synthesize Data
Define Attributes
Collect Data

12. Why Normalization? Remove the anomalies due to lack of flexibility
ineffectiveness of data storage
inefficiency of data manipulation operations
inserts (additions)
deletes (deletion)
updates (modification)

13. Denormalization and Why?
Denormalization is the process of transforming normalized relations into unnormalized physical record specifications.
A normalized form (e.g., 3NF) presents many important features, however, it may required that queries join terms from different entities more often than in a less normalized data model.

14. Another example of denormalization
CUSTOMER
...
CITY
STATE
ZIP
CustID
Normalization ?
NO!
CITY
ZIP
STATE
ZIP
STATE
ZIP
CITY

15. Denormalization : CUSTOMER
In a denormalized version of the CUSTOMER entity, ZIP, CITY, and STATE may be defined as attributes of the entity whose value is derived from entity ZIP and stored redundantly with the other address properties of each CUSTOMER.

16. New Business Rules
To maintain the data consistency, new business rules should be implemented:
the values of attributes CITY and STATE will be updated only in the entity ZIP. In all other entities where these attributes are denormalized, applications may insert and select but not modify these attributes.

17. New Business Rules (cont.)
when the attributes CITY and STATE of entity ZIP are modified, the changes will be propagated in all the denormalized CITY and STATE attributes of other entities.

18. Un-normalization vs. De-normalization
Unnormalized data model is little or no analysis is performed.
Denormalized data model is derived from a model in the 3NF, after the reasons and impact of denormalizing some entities are identified and weighed.

19. Conclusion
When parts of the data model are denormalized, the rules for maintaining the redundant data must be defined clearly and implemented programmatically, in order to avoid the anomalies discussed before.

20. Are you still ...