Enterprise Architect Data Modeling 101: Definition, Phases, Techniques, & Best Practices

Data is an important asset. Through data, companies can establish unknown trends and patterns to facilitate an evidence-based decision-making process, which is key for growth. 

There are different ways of storing data in organizations. The way data is structured within the storage is very important. If the data is structured well, it will be easy for both organization employees and applications to access the data. It will also be easy for the organization to enforce compliance standards and establish new relationships. This is possible by creating data models. It helps organizations to establish the right structure for their data. 

The process of creating data models is not automatic, but enterprise data architects have to plan well for the process. In this article, we will be discussing the enterprise architect data modeling process.

What is Enterprise Architect Data Modeling?

An enterprise data model is an integration model that covers all the data stored in an organization. The data models conceptualize the relationships between various types of data within the organization. They are a representation of objects and the relationships between them to help users across departments store and interact with data in a more effective way. Through data models, organizations are able to understand their data assets through core building blocks like entities, relationships, and attributes. These represent the major aspects of the organization like the product, customer, employee, and more. 

During the enterprise architect data modeling process, the data models should be made logical and easily understandable to those in need of insights on data objects. 

Phases of Enterprise Architect Data Modeling

The Enterprise Architect Data Modeling process takes the following phases:

Phase 1: The Conceptual Model

Conceptual Enterprise Architect Data Modeling involves establishing the relationships between application components. Each component is assigned a set of properties to help in defining the data relationships. The components can be organizations, facilities, people, products, and application services. 

The component definitions should identify the business relationships. For example, an item is shipped from a warehouse and then to a retail store. A good conceptual data model should trace the flow of such goods, orders, and payments between the various software systems used by the company. 

In some cases, the conceptual data models are directly translated into physical data models. However, if the data structures are complex, it’s good to create a logical data model in between. 

Phase 2: The Logical Model

In this Enterprise Architect Data Modeling step, the data architect creates unique identifiers that define every component’s property as well as the scope of the data fields. Most enterprise architects like using the original data source and the name of the property in a qualified-name structure, like Orders.CustomerName in this data modeling step. Others put the data source in parenthesis, while others don’t provide information about the data source. 

Data architects recommend that organizations should identify the data source explicitly in a way. The source information will help to eliminate redundancy in data storage. Thus, you should establish a single entity to be the data source. The data source should then be linked to all entities sharing the data. 

The logical data model can be translated directly into an entity/relationship model, relational database, business-oriented language, or object-modeling language. 

Phase 3: The Physical Model

This is the final step in Enterprise Architect Data Modeling and it involves mapping the logical model into a database management system. Once enough data has been identified and mapped explicitly, the administrators can create the actual database structure. The physical Enterprise Architect Data Modeling generates a tangible schema for the data structure components like identification keys, columns, indexes, triggers, and constraints. 

Most companies already have their preferred database tools, software, and skill set. Examples of such tools include MongoDB, SAP HANA, and MySQL. 

Data Modeling Techniques

The following are the common Enterprise Architect Data Modeling techniques:

An Entity Relationship Diagram

This is the default data modeling technique and it works well when dealing with tabular data. It involves representing data objects graphically alongside their relationships and attributes. They are good for designing traditional and Excel databases.

Unified Modeling Language

This technique uses a series of notations to design and model information structures. UMLs show the structure or behavior of data objects and use different types of diagrams. Examples of such diagrams include class diagrams, use case diagrams, and others.

Data Dictionaries

These define data assets using a tabular format. Data Dictionaries group tables and data assets together with a list of columns and attributes. They also have other sections including additional constraints, item descriptions, and relationships between tables and columns.

Enterprise Architect Data Modeling Best Practices

Don’t Create Redundancies

The best data objects are those that don’t overlap. The best way to test this is by checking whether level 2 objects can be assigned without any ambiguity. 

Use Business Capabilities

You can easily know the existing data objects after mapping your business capabilities. Thus, it will be good to first create the business capability map. 

Cross-organizational

Avoid being too specific. The objects should remain the same regardless of any changes made to the organizational structure. 

Breadth Rather Than Width

It’s true that having more levels can help you to have a better structure, but it increases complexity. Go for breadth and create a map with a maximum of three levels. 

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• Enterprise Database Architecture
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Conclusion

This is what you’ve learned in this article about Enterprise Architect Data Modeling:

• Data facilitates evidence-based decision-making in organizations for growth. 
• A well-structured data makes it easy for organization employees to access data and enforce compliance standards. This is made easier by the use of data models. 
• An enterprise data model integrates and conceptualizes the relationships between various types of data within the organization. 
• The enterprise architecture data modeling process requires proper planning and involves three phases namely the conceptual model, the logical model, and the physical model. 
• There are different data modeling techniques, but the most popular ones are entity-relationship diagrams, unified modeling language, and data dictionaries. 

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