DATA MODELLING CONCEPTS

Why Should People Learn Data Modeling?

Consider this problem. You have just landed in a strange airport. You need to get into the city.
What do you do?
• Hire a taxi? The driver knows the city … He'll take you.
• But you need a your own car to Rome the city while you are there
• OK. Rent a car? But how do you find your way?
• Get a City Map - or better still - a City/Street Directory.

With a Street Directory the inside cover, shows the indexes and layout of the city, by a grid network pages defined for each street in detail. You can then find where you are, where yo want to go, and how to get there. But there may be many routes you can choose:

1) Depending on traffic rules and traffic conditions
2) Depending on the time requirements (ie. Performance)
3) Depending on technology: taxi, car, bus, train, walking.

So how do business managers know about the data that is required for decision making?
• Ask the end-users of the data. But who are they?
• And what if the data exists in different versions? Which version is correct?

Similarly, a data model is the "Corporate Street Directory". It includes data maps and data details
in a Repository. It is analogous to the city map and details of streets. A data model, therefore:
• Depends on business rules and business conditions
• Depends on the time requirements (ie. Performance)
• Depends on the technology used: mainframe, micro, network etc

Today, a data model is an essential prerequisite for development of Application Data Bases, Data
Warehouses, for Internet/Intranet systems and for business transformation to take advantage of
the Internet and corporate Intranet technologies.

Methodology:
There are two major methodologies used to create a data model: the Entity-Relationship
(ER) approach and the Dimensional Model.

Steps In Building the Data Model:
While ER model lists and defines the constructs required to build a data model, there is
no standard process for doing so. Some methodologies,  specify a bottom-up development process were the model is built in stages. Typically, the entities and relationships are modeled first, followed by key attributes, and then the model is finished by adding non-key attributes.
sequence used for this is:
1. Identification of data objects and relationships
2. Drafting the initial ER diagram with entities and relationships
3. Refining the ER diagram
4. Add key attributes to the diagram
5. Adding non-key attributes
6. Diagramming Generalization Hierarchies
7. Validating the model through normalization
8. Adding business and integrity rules to the Model.


In my coming posts, i will explain in detail about the ER model and dimensional model.