Last updated on Nov 07, 2023
Data modeling refers to the process of creating a data model in order to store the data in the database. The data modeling concept uses the graphical representation of the data objects, rules, and associations that exist between the data objects. Data modeling is responsible for providing the visual representation of the data, ensuring that all the compliance rules, regulatory rules, business rules and focused and followed on the data.
In simple terms, data modeling is referred to as a process that helps in formulating the data in a structured format that is present in an information system. Data modeling helps in analyzing the data which helps in making precise decisions and meet business requirements efficiently.
In order to perform the process of data modeling, data modelers are required who will be involved in working with the stakeholders and uses of the information system. As data modeling is the process of creation of the data model, the data modeling process will definitely end with the data model creation which provides support to the business information system infrastructure. Also, this process helps us in understanding the structure of the organization, providing the solutions that would help in achieving the objectives of the organization, and also helps in reducing the gaps between the different departments, functional and Technical areas.
A data model is defined as the Abstract model which helps in organizing the data semantics, data description, and also consistency constraints of the data. The data model primarily focuses on what data is needed and how the data needs to be organized rather than putting its focus on what operations have to be performed on the data. In layman’s terms, the data model is referred to as a building plan for an architect who is responsible for building the conceptual models and establishing a relationship between the different data items available.
A Data model is also referred to as a fundamental entity that helps in defining how the data is connected to each other, and also how the processing takes place, how they are stored inside the system, etc. Database modeling is referred to as a process of designing a database structure, an idea that helps in defining how the Stored information can be categorized, accessed, or modified.
There are many reasons for the usage of data modeling or the creation of a data model. There is high importance or significance for data modeling as it possesses the ability to understand the business requirements and ensure that the data is of better quality, with robust design elements, and improves performance. Let me give you a briefing of the primary reasons for which the data model is used.
1. Data modeling or data models provide the visual representation of the data which will help in performing the analysis of the data, improving the data analysis feature. It is also flexible to provide the picture of the data which can be further used by the developers in order to create a physical database.
2. A data model is a representation that would help in putting the business requirements and help us in gaining a better understanding of them.
3. A data model that is qualified also provides us with the flexibility to improve the better consistency across the different projects of the organization.
4. As the organization holds large volumes of data, all the important data will be represented in the form of a model, which further reduces the chances of data omission. Data omission sometimes might lead to faulty reports or incorrect results.
5. Data modeling helps in creating a robust design which helps in bringing the entire data of the organization on a single platform. Data modeling also helps in determining the duplicate, missing, or redundant data in the system.
6. Data model is responsible for improving the data quality and also defines the stored procedures, primary key, foreign key, and relational tables.
7. A data model also has the project manager to improve the better scope and quality management.
Perspectives of data model: There are three different perspectives of a data model. They are:
1. Conceptual model
2. Logical model
3. Physical model.
Let me explain to you about each perspective of the data model.
A Conceptual data model includes What data has to be present in the structure of the model, which is used to define the business concepts and organise the same. The primary focus of a conception data model is on the attributes, relations, and business-oriented entities. The conceptual model is designed by the business stakeholders and the data Architects.
In simple terms, the conceptual data model defines what the system has to contain. It is referred to as the organized review of the database concepts and relationships that exist between each other. The primary purpose of the creation of a conceptual data model is to create the entities and establish the attributes and relationships between them. In this model, you will not find the details about the actual database structure.
Three different basic tenants of the conceptual data model. They are:
1. Entity: Entity is referred to as the real-world thing.
2. Attribute: Attribute is referred to as the properties or the characteristics of the entity.
3. Relationship: Relationship refers to the dependency or the association between the two different entities.
Conceptual data model example:
In the below figure, the product and the customer are two different entities. The customer number name and number are the attributes of the customer entity wilder product name and product prices are the attitudes that refer to the product entity. The sale is referred to as the relationship that exists between the customer and the product
The conceptual data model process the below characteristics like:
1. The conceptual data models are designed and developed for the business audience.
2. It also provides organization-wide coverage of the business concepts.
3. The conceptual model is developed independently irrespective of the hardware specifications like location, the capacity of the data storage, and software specification slide technology and DBMS vendor.
The conceptual data models are also called as the domain models that help in creating a common vocabulary for these work holders by keeping their primary focus on the establishment of the basic concepts and scope.
The logical data model helps you in defining how the model has to be implemented. This logical data model has to be captured using the different kinds of data like tables, columns, etc. Logical data model is designed by the data architect and business analyst. The logical data model is referred to as the data model that helps in defining the structure of the different data elements and also says the relationships that exist between each other. The logical data model gives us a little more Idea and adds more information to the conceptual data model. The primary advantage of using the logical data model is that it is flexible to provide the foundation for the physical model.
In the logical data, there is no need to define the primary key or secondary key. All you need to do is to verify and adjust the character details that were already set up for the purpose of relationships.
Characteristics of the logical data model:
1. Logical data models cannot be integrated with the other data models based on the scope of the project. It specifically describes the data that is required for a single project.
2. The logical data model is developed independently from the database management system.
3. The data attributes that are present in the logical data model will have their own data types with length and exact precisions.
4. There is a logical data model so included in normalization processes that that can be applied.
The physical data model is referred to as the data model that helps in defining how the implementation of the data model has to take place. It provides the outline of implementation technology or methodology that has to be used, represented in the form of tables, indexes, partitioning, CRUD operations, etc. The physical data model is created by developers and database administrators.
A Physical data model provides a database-specific implementation of the data model. It helps in schema generation and also the database abstraction. The physical data model provides a richness of meta-data. The physical data model also helps in visualizing the database structure by performing the replication of the database column keys, triggers, constraints, and other RDBMS features.
1. Physical data model helps in providing the data that is needed for a single project or an application that is integrated with the other physical data models based on the project scope.
2. Physical data model is developed for a specific version of a DBMS, data storage or Technology, location, has to be used in the project.
3. Physical data model comprises the different relationships that exist between the tables, also responsible for addressing the availability and cardinality of the relationships.
4. A physical data model consists of the foreign keys and the primary keys, access profiles, authorizations, etc.
There are different ways or approaches to data modeling, but the concept remains the same for all kinds of data models. Let me give you a brief understanding of the different types of data models that are most popularly used.
The Hierarchical model is one of the models that is used to organise the companies that have the similar tree-like appearance. The Hierarchical model is used to structure the xML documents. This is referred to as an ideal data model in which the data comprises nested and sorted information. The Hierarchical model is found to be inefficient when the data does not have an upward to the main data point of the subject. This model is found to be efficient for the employee information management system in an organization that helps in restricting or assigning the equipment used to the particular department are the individuals.
In simple terms, the hierarchical model is based on the tree-like structure that has a single root for a parent. Hence, each of the records will have its own parent. As per the sibling records, they are sorted out in a particular order and this order can be used as a physical order in order to be stored in a database. This type of modeling can be used for Real-world model relationships. However, it is now rarely used due to some inefficiencies within the system.
In the above example, one department which can have many different courses, many different professors and many students.
The relational models its flexible to create a new type of database which is a combination of the database design and the application program. Relational models help in solving technical problems. This model came up as an alternative to the hierarchical model in the year 1970. This model does not actually require the developers for defining the data path. The relational model includes the data segments that can be explicitly combined with tables. This model has further provided us with the advantage of reducing the program complexity. The relational model requires complete knowledge about the physical data storage that is adopted by the organization. This model is further combined with a structured query language.
Network model makes use of the one to many relationships approach using the records and sets for the data records. In this model, multiple branches are allocated for the low-level structures while the branches that are connected by multiple nodes will be represented as the high-level structures. Network modelling method is one of the models that is providing an efficient way to retrieve the information and perform the organisation of the data efficiently.providing the way or means to increase business performance by allowing it to be looked at in multiple ways.
The network model is flexible To be built on the hierarchical model, allowing multiple relationships among the different link records, stating that it has multiple parent nodes. We can construct the model with a set of related records based on the mathematical set theory. Every set consists of a parent record and multiple child records. Hence each record can belong to multiple sets and also allows the model for conveying the complex relationship that exists among each other.
Dimensional model is referred to as an adaptation of the relational model and is most commonly used in conjunction with the relational model. This is done by adding the dimension of fact to the data points. The Dimensional model is responsible for helping the business to make efficient credit in business decisions and also had them analyze the target audience. Just the model is better to be used by the organizations that deal with the sales and profit analysis.
The object-oriented database model is referred to as a collection of objects. These objects will have associated features and methods. There are different types of object-oriented databases like the hypertext database, multimedia database, etc. The object-oriented database model is also known as the post-relational database model. This is because it is not limited to any sort of tables, hence we can call all types of data models hybrid models.
The Entity-relationship model is referred to as a graphical representation of the entities that exist in the database and their relationships between them. It is also called the entity-relationship diagram or E-R diagram. An entity here refers to the concept or a piece of data or the object about which the data has been stored.
In order to understand data modeling, you will need to understand facts and dimensions.
Fact Table: A fact table is a table that consists of measurements and granularity for every measurement. Facts can be either additive, semi-additive. An example would be sales.
Dimension table: A dimension Table is a table that is responsible for collecting the fields that contain the descriptions of business elements and is also referred to by the multiple fact tables.
Dimensional modeling: Dimensional modeling is referred to as a design technique that is used for the data warehouse. This technique uses we confirm dimensions and facts and is also responsible for easy navigation. Dimensional modeling design is also responsible for providing the faster performance query. Dimensional models are also known as star schemas.
Here are some of the things that are important to understand when we learn data modeling. Use of data Modelling and divided into five different categories, and they are represented below:
1. Primary and alternate keys: The primary key is referred to as a field that contains a unique record. The user can select any one of the available primary keys, while the remaining keys present will become the alternate keys.
2. Business or natural keys: Business or natural keys are those fields that help in uniquely identifying an entity. Example: Employee number, candidate ID, etc.
3. Composite or compound keys: The composite compound key is the key that is used to represent one or more fields.
4. Foreign keys: The foreign keys are those keys pointing to another key in a different table.
5. Surrogate keys: Surrogate keys those keys that do not have any business meaning and are auto-generated.
In the process of data modeling, it includes designing and producing all the different types of data models. These data models are converted using the data definition language, which is used to generate the database. The database is called the fully attributed data model.
There are many benefits that are available using the data model. Few of them are presented below:
1. The data objects that are provided by the functional team are represented accurately by using the data modeling techniques.
2. Data modeling also improves communication across the organization, allowing you to query the data from the database and generate the reports, and derive the decisions based on the data.
3. Data modeling helps the data analyst with the help of reports, which will help improve the productivity and quality of the assigned project.
4. Data modeling also improves business intelligence, allowing the data models to work together, including gathering the data from multiple and structured sources, spending patterns, and reporting the requirements as and when required.
5. Data modeling alerts you to represent the data in a structured format from unstructured data forms.
6. Data modeling also helps in documenting the data mapping that is related to the ETL process.
7. The primary goal of designing the data model is to ensure that the data objects offered by the functional team are recognized, represented in the right way.
8. The data model is represented in detail in order to build the physical database efficiently.
1. One has to be aware of the physical data that is stored with its characteristics in order to develop a data model.
2. Development of the data model is found to be the deadliest job.
3. The data modeling system also requires complex application development and knowledge of the biographical truth.
4. The data model is not user-friendly as small changes in the application might require a major modification.
Data models are usually developed and implemented for the data to be stored in the database. Despite some of the drawbacks or limitations that are available in data modeling, data modeling is the first and most important phase of the database design because it is involved in defining the data entities and the relationships between the different dates of objects, etc.
Every organization is involved in maintaining large volumes of data. The creation of data models has to be done accurately by the architects, data modelers. There is a high scope for the trained and certified individuals in data modeling to attain the job easily as everything is handled with data in the current era of Living. I would recommend you to get trained and certified in data modeling, which would help you attain the best job opportunities with a prosperous career.
As a Senior Writer for HKR Trainings, Sai Manikanth has a great understanding of today’s data-driven environment, which includes key aspects such as Business Intelligence and data management. He manages the task of creating great content in the areas of Digital Marketing, Content Management, Project Management & Methodologies, Product Lifecycle Management Tools. Connect with him on LinkedIn and Twitter.
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