Ever wondered how easy it is to remember any movie but it becomes difficult to read and understand what’s written on paper. The reason is straightforward; our brain loves visuals more than anything.
Today, data used by businesses has plenty of information in it, which is required to be processed very efficiently. However, what would happen if the reader finds the data hard to understand or fails to acknowledge it? Well, in these cases, the vital information may remain unseen and unused. To tackle this issue, data modeling comes as a savior.
In this article, we will describe how data modeling helps us understand the data easily along with various stages of the data modeling process.
What is Data Modeling?
Data modeling is the act of generating a visual representation of an entire information system or parts of it to express relationships between data points and structures. It aims to define the type of data used and stored, relationships among the data, and various ways used to group and organize the data.
Different businesses require different data models. The process of data modeling starts by collecting information about business goals and requirements from stakeholders and end-users. Usually, stakeholders set the rules and regulations of what needs to be designed in a business. A data model may be defined as a blueprint that gives a deeper understanding of what is being developed.
Like any other data process, data models and data modeling are done at different levels and with concrete specifications. Data models are of three types, whereas data modeling process has five key stages.
Types of Data Models
1. Conceptual Data Model
Conceptual data models represent the framework of data. It tells us how the data is organized and defines the reader’s path to understanding the concept. This model helps to identify the main domains of a business.
Typically, conceptual models are generated as part of the early project requirements gathering process. Conceptual data models are useful for business executives. It helps them understand how the system is laid out and how it will perform to meet all the business requirements.
2. Logical Data Model
Logical data models are created after the conceptual data models. In general, logical data models are a little less abstract than conceptual models. It shows a detailed knowledge of how the entities in the domain are interconnected.
One particular thing to note about logical data models is that they do not define the technical system requirements. They are used when the process is highly data-oriented.
3. Physical Data Model
A logical data model helps in the creation of physical data models. They are specific to a database management system (DBMS) and define how the data will be stored within a database. Physical data models are the least abstract. They provide a finished design that can be implemented as a relational database. Moreover, a relational database makes use of associative tables that define the associations between entities (data) as well as the primary keys and foreign keys that helps to keep the relationship between the data up to date.
Data Modeling Process
The data modeling process has six steps that facilitate the visual representation of data. This series of six steps need to be executed in a particular sequence to achieve the right results.
The steps of the data modeling process are as follows:
1. Identifying the entities
The process starts by identifying the domains and entities that will be a part of the data to be modeled. The entities can be things, events, concepts, etc. Also, each entity should be related to the other.
2. Identifying the critical properties of entities
Entities are related to each other but must have a unique property known as an attribute. The attribute needs to be defined in the second step of the data modeling process. For example, an entity “customer” will have a unique first and last name. Similarly, an entity “contact” will have the customer’s contact number and/or email address.
3. Identifying the relationship among entities
In the third step, the relationship between the entities needs to be identified. For example, for a company like amazon.com, entity “order” and “customer address” will be related to each other. The customer’s order will be delivered to the address mentioned in the entity “customer address.”
4. Mapping attributes to entities
An entity is just like a noun in a database, and it can be a person, a thing, a product that a company develops, or a video that a content creator makes. Now, assigning an attribute to an entity signifies how the company looks forward to doing its business.
For example, for a company like Amazon or Flipkart, an entity can be its customers and their names. Such entities can help us to understand how the business is likely to run. Assigning an address to each person’s name or saving their address in the database tells us that the company is expected to use the address to deliver products to customers. Here addresses act as an attribute, and entities are the customers. Assigning attributes to entities helps make it easier for everyone to understand the data.
5. Finalize and validate the data model
In the end, we finalize data that suits the business requirements. Also, we need to check if the data model is built correctly and validate if it provides the data that can actually help to achieve various business goals and objectives.
It is important for you to keep in mind that as business demands evolve, data modeling becomes an iterative process that should be repeated and updated.
The data modeling process gives the visual representation of any data. With domains and entities clearly defined in a data model, stakeholders and executives can easily understand the data and its flow. Also, if you are interested in data analytics, it is essential to become familiar with the process of data modeling.
We hope that through this article, you have developed a basic understanding of the data modeling process. If there’s anything about data modeling that you think others should know, simply share it in the comments below!