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

on Data Modeling, Enterprise Architect Data Modeling • April 14th, 2022 • Write for Hevo

Enterprise Architect Data Modeling | cover

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.

Table of Contents

  1. What is Enterprise Architect Data Modeling?
  2. Phases of Enterprise Architect Data Modeling
  3. Data Modeling Techniques
  4. Enterprise Architect Data Modeling Best Practices
  5. Conclusion

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. 

Replicate Data in Minutes Using Hevo’s No-Code Data Pipeline

Hevo Data, a Fully-managed Data Pipeline platform, can help you automate, simplify & enrich your data replication process in a few clicks. With Hevo’s wide variety of connectors and blazing-fast Data Pipelines, you can extract & load data from 100+ Data Sources straight into your Data Warehouse or any Databases. To further streamline and prepare your data for analysis, you can process and enrich raw granular data using Hevo’s robust & built-in Transformation Layer without writing a single line of code!

Get Started with Hevo for Free

Hevo is the fastest, easiest, and most reliable data replication platform that will save your engineering bandwidth and time multifold. Try our 14-day full access free trial today to experience an entirely automated hassle-free Data Replication!

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:

Enterprise Architect Data Modeling | data modeling techniques
Image Credits: Dataedo

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.

What Makes Hevo’s ETL Process Best-In-Class

Providing a high-quality ETL solution can be a difficult task if you have a large volume of data. Hevo’s automated, No-code platform empowers you with everything you need to have for a smooth data replication experience.

Check out why Hevo is the Best:

  • Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
  • Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
  • Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making. 
  • Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
  • Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
  • Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Want to take Hevo for a spin? Use Sign Up For a 14 Day Free Trial here for a 14-day free trial and experience the feature-rich Hevo.

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. 

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. 
Visit our Website to Explore Hevo

Hevo Data, a No-code Data Pipeline can seamlessly transfer data from a vast sea of 100+ sources to a Data Warehouse or a Destination of your choice. It is a reliable, completely automated, and secure service that doesn’t require you to write any code!  

Hevo Product Video

If you are using CRMs, Sales, HR, and Marketing applications and searching for a no-fuss alternative to Manual Data Integration, then Hevo can effortlessly automate this for you. Hevo, with its strong integration with 100+ sources (Including 40+ Free Sources), allows you to export, load, and transform data — and also make it analysis-ready in a jiffy!

Also, let’s know about your thoughts and building process for Enterprise Architect Data Modeling in the comments section below.

No Code Data Pipeline For Your Data Warehouse