Data Modeling 101

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data modeling

Data Modeling creates a visual representation of a data system as a whole or as parts of it. The goal is to communicate the kinds of data being used and saved within the system. A data model should also show the data’s relationships, how the data can be organized, and the formats used.

A data model can be used to define and analyze an organization’s data requirements and support predictions for future needs. Typically, the Data Modeling process requires getting professional data modelers to work closely with management, as well as the people who will be working with the data.

Data models are normally built around the needs of the business. 

The value of intelligent Data Modeling is quite significant. It lowers the chances of errors within the data and often increases the ability to gain insights quickly and efficiently. Organizations can use the data models to express the needed data and the format it should be in. 

Data models also provide a common ground for communications and collaboration. They help to ensure everyone is working toward the same goals and using the data in uniform, consistent ways. 

What Are the Basic Types of Data Models?

There are three basic types of data models: conceptual data models, logical data models, and physical data models. Each has a specific purpose. The data models are used to represent the data and how it is stored in the database and to set the relationship between data items. A data definition language is normally used to convert the models into an active database. 

Conceptual Data Modeling: This model focuses on “what” the data system contains, not how the data is processed, or its physical characteristics. Its purpose is to organize and define business concepts and rules, while describing entities, their attributes, and their relationships. This data model provides little detail about the actual database structure but focuses on the data used by the business. The conceptual data model is built on three basic tenants, which are:

  • Entities: Real-world things.
  • Attributes: The characteristics or properties of entities.
  • Relationships: How two entities are connected. The customer (an entity) is related to the product (another entity) by the order the customer placed (the relationship).

Logical Data Modeling: Focuses on “how” the system needs to be broadly implemented for “generic” database management systems. The purpose of this data model is to create a technical map describing rules and data structures. Another use of the logical data model is its ability to act as a foundation for the physical model.

Physical Data Modeling: Describes “how” the system needs to be implemented while using a “specific” database management system. It is normally used to describe the data needed for a single project or an application. This model also helps with visualizing the database structure (database column keys, indexes, triggers, and other relational database management system features). 

What Are Data Modeling Techniques?

There are a variety of Data Modeling techniques that can be used to develop functional data models. Data Modeling techniques are part of the strategy organizations use to simplify the analytics process. Using the right Data Modeling techniques helps businesses to gain operational resilience and ensure the quality of the data when making decisions. 

Not using Data Modeling techniques can result in operational inefficiencies as the organization’s needs change. Organizations should develop a database design schema that allows the right modeling techniques to be used. Data Modeling techniques allow data analysts to use data without worrying about its quality. Below are some of the different techniques that can be used to organize data:

  • The Relational Technique: Used to describe the relationships that exist between entities (real-world things). There can be different types of relations between entities, such as one entity to another entity, one entity to many, and many entities to many entities.
  • The Entity-Relationship Model: This is a high-level relational model used for defining relationships and data elements for the entities within a system. As a conceptual design, it offers a better perspective of the data, making it easier to understand. The entire database is shown in the “entity-relationship diagram,” which is made up of entities, attributes, and relationships.
  • The Network Technique: Using a graph format, this technique provides a flexible way to represent objects (similar to entities), and their relationships. An object is shown inside a node, while the relationship is shown as a line, or edge.
  • The Object-Oriented Model: Uses the creation of objects containing stored values and supports data abstraction, encapsulation, and inheritance. During the early development stages, the model is in an abstract form because external details are the primary focus. As the model becomes more detailed, it evolves. 
  • The Hierarchical Technique: This is an older, “classic” model that is rarely used these days but can still express real-world relationships. It uses a tree-like structure. There is one root node, or one parent node with other child nodes that are arranged in a particular order. 

What Are Data Modeling Skills?

A data modeler does not have to have significant software and data systems knowledge, but they must have the ability to think both abstractly and conceptually. They must have the ability to transform logical models into physical models while adding the objects necessary to develop the database. (Experience with data warehouses is also a plus.)

Additionally, the data modeler should have excellent communication skills. This is essential. Organizations require strong communication skills in their data modelers so they can explain issues and concepts in nontechnical terminology to staff, management, and investors. 

Listed below are technical skills and background knowledge that can be quite useful in Data Modeling:

  • An understanding of SQL language and how it is implemented: SQL (structured query language) is the most commonly used programming language for managing, manipulating, and accessing data that is stored in a relational database. 
  • An understanding of digital logic: Digital logic (also called boolean logic) is the foundation of all modern programming languages and computer systems. It simplifies complex problems into values that are “1/0,” “true/false,” or “yes/no.” As the primary concept behind coding, understanding this concept is quite important.
  • Memory architecture: This deals with how binary digits are stored in a computer, and the storage of complex data in database programs and spreadsheets. The most important aspect of memory architecture involves discovering the most efficient method of combining speed, cost-effectiveness, durability, and reliability for handling data.
  • A familiarity with different modeling tools: Becoming familiar with the many modeling tools currently available to the business is an excellent idea. Being knowledgeable about tools can save valuable time and promote efficiency. 

What Are Data Modeling Tools?

Data Modeling tools are typically software applications designed to build database structures from diagrams. This process makes it fairly easy to create a highly functional database. These tools also support the development of business-specific infographics, data visualizations, and flowchart results. 

As businesses attempt to connect different systems supported by different formats and structures, Data Modeling tools are used more and more to deliver the diagrams and schemas needed to organize the process efficiently. The variety of tools that are currently available for Data Modeling is quite extensive and include the following:

  • SQL Database Modeler will import and build SQL modeling scripts and provide collaboration and sharing features.
  • Erwin Data Modeler helps to find, visualize, develop, and standardize data assets. It can integrate the conceptual, logical, and physical models using a visual interface.
  • Idera ER/Studio supports a range of data assets that can extend across platforms. It offers extensive tools for constructing business glossaries. 
  • ERBuilder supports graphic design of databases using entity relationship diagrams. It generates popular SQL databases, automatically.
  • DbSchema helps with designing and managing of SQL, NoSQL, as well as cloud databases. It provides a graphical interface.
  • HeidiSQL is a free Data Modeling tool with useful features and capabilities. It supports Microsoft SQL, PostgreSQL, NySQL, and MariaDB. 
  • Toad Data Modeler is a solution for multiplatform Database Modeling. It offers visualization capabilities displaying relationships for databases, as well as forward and reverse engineering.
  • Navicat Data Modeler offers a broad range of formats (SQL/DDL, ODBC) as well as specific frameworks (Oracle, SQLite, MariaDB, MySQL, SQL Server, PostgreSQL). It includes both reverse and forward engineering and works with all the major platforms. 
  • DeZign for Databases is a user-friendly modeling tool that supports the visualization of database structures. Use it to create new databases, or reverse-engineer existing databases.

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