Developing a Data Model

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The following overview outlines the ER/Studio Data Architect data model development process, which is an iterative process:

• Starting With a Basic Data Model
• Creating a Basic Data Model
• Reverse Engineering a Database
• Importing a File to Create a New Database
• Generating a Data Model Using AI
• Ensuring Data Integrity
• Automating Data Model Creation and Maintenance
• Fine Tuning the Data Model
• Securing the Data Model
• Preparing for Review and Implementation
• Maintaining the Data Model

Starting With a Basic Data Model

You can start from scratch or reverse engineer an existing database.

Creating a Basic Data Model

1. Create a new data model.
2. Add entities.
3. Define entity attributes.
4. Establish relationships between entities.
5. Assign keys.

Reverse Engineering a Database

Reverse engineer a database.

Importing a File to Create a New Database

Import data from a variety of sources to create a new database. See Importing a Model from an ERX File and Importing a Model from a SQL File for further details.

Generating a Data Model Using AI

WARNING: The accuracy of data models generated using AI is not guaranteed. Any content generated using this tool must be separately reviewed and verified by a human.

1. Click File > New.
2. In the Create a New Model dialog, select Generate Using AI, and then click OK.
3. Review the displayed terms and conditions for using the Data Architect AI feature. If you acknowledge and agree to the terms, click Accept.

   The AI Model Builder window appears.

4. In the Model Prompt field, input a description of the model that you want to build. Note that the Surprise Me button provides an example prompt so that you can get an idea of what information and format helps the AI build a good model. You can always change or delete text typed in this field.
5. In the Options area, select the appropriate initial layout. Hierarchical is selected by default.
6. Optional. Click Remember this text to save the entered prompt text to appear the next time you open this dialog.
7. Click Generate. The AI Model Builder displays a progress dialog while building the model based on your input.
8. When finished, the AI Model Builder displays your new model in the background while also displaying a robust description of the model in the AI Model Builder Progress dialog.
9. Click Save to File or Print to retain a copy of the log generated during model creation.
10. Click Close to close the AI Model Builder Progress dialog.

Ensuring Data Integrity

1. Ensure object names conform to business standards by creating and applying naming conventions.
2. Ensure data values are within a specified range or that they match a value defined in a list by defining reference values.
3. Ensure data input in tables or column is valid by add rules.

Automating Data Model Creation and Maintenance

1. Accelerate entity creation and updates by creating domains.
2. Assist user data entry by assigning default values.
3. Create macros to perform repetitive tasks.
4. Define default values for attributes, domains, and user datatypes.
5. Add reusable procedural logic.

Fine Tuning the Data Model

1. Validate the model.
2. Customize the datatype mappings.
3. Create user-defined datatypes for domains and attributes.
4. Generate a physical model.
5. Normalize to reduce data redundancy.
6. Denormalize to improve performance.

Securing the Data Model

1. Check the data model in to the Repository.
2. Prevent the data model from being updated or viewed by unauthorized persons by creating Users, Groups. and Roles and apply them to the data model. See Creating and Editing Database Users and Creating and Editing Database Roles for more information.
3. Prevent the database from being updated or viewed by unauthorized persons by creating Users and Roles and apply them to the data model. See Creating and Editing Database Users and Creating and Editing Database Roles for more information.
4. Specify data security information.

Preparing for Review and Implementation

1. Customize the model display.
2. Provide additional documentation for data model reviewers by creating attachments.
3. Document data lineage.
4. Create reports for distribution.
5. Make it easy for users to access the data by providing a dimensional mode.
6. Generate SQL to implement the database.

Maintaining the Data Model

1. Ensure the model is updated from the correct sources by documenting data lineage.
2. As the physical database is updated, synchronize the physical database model with the physical database.
3. As the physical database model is updated, synchronize the logical database with the physical database.
4. As updates are required, check out portions of the data model from the Repository.

See Also

• Data Modeling Concepts
• Customizing the Data Model