Data is changing the way the world functions. It can be a study about disease cures, a company’s revenue strategy, efficient building construction, or those targeted ads on your social media page; it is all due to data.
This data refers to information that is machine-readable as opposed to human-readable. For example, customer data is meaningless to a product team if they do not point to specific product purchases. Similarly, a marketing team will have no use of that same data if the IDs didn’t relate to specific price points during buying.
This is where Data Modeling comes in. It is the process that assigns relational rules to data. A Data Model un-complicates data into useful information that organizations can then use for decision-making and strategy. According to LinkedIn, it is the fastest-growing profession in the present job market. Before getting started with what is data modelling, let’s understand what is a Data Model in detail.
What is a Data Model?
Good data allows organizations to establish baselines, benchmarks, and goals to keep moving forward. In order for data to allow this measuring, it has to be organized through data description, data semantics, and consistency constraints of data. A Data Model is this abstract model that allows the further building of conceptual models and to set relationships between data items.
An organization may have a huge data repository; however, if there is no standard to ensure the basic accuracy and interpretability of that data, then it is of no use. A proper data model certifies actionable downstream results, knowledge of best practices regarding the data, and the best tools to access it.
After understanding what is data modelling, let’s discuss its examples.
What is Data Modeling?
Data Modeling in software engineering is the process of simplifying the diagram or data model of a software system by applying certain formal techniques. It involves expressing data and information through text and symbols. The data model provides the blueprint for building a new database or reengineering legacy applications.
In the light of the above, it is the first critical step in defining the structure of available data. Data Modeling is the process of creating data models by which data associations and constraints are described and eventually coded to reuse. It conceptually represents data with diagrams, symbols, or text to visualize the interrelation.
Data Modeling thus helps to increase consistency in naming, rules, semantics, and security. This, in turn, improves data analytics. The emphasis is on the need for availability and organization of data, independent of the manner of its application.
Data Modeling Process
Data modeling is a process of creating a conceptual representation of data objects and their relationships to one another. The process of data modeling typically involves several steps, including requirements gathering, conceptual design, logical design, physical design, and implementation. During each step of the process, data modelers work with stakeholders to understand the data requirements, define the entities and attributes, establish the relationships between the data objects, and create a model that accurately represents the data in a way that can be used by application developers, database administrators, and other stakeholders.
Levels Of Data Abstraction
Data modeling typically involves several levels of abstraction, including:
- Conceptual level: The conceptual level involves defining the high-level entities and relationships in the data model, often using diagrams or other visual representations.
- Logical level: The logical level involves defining the relationships and constraints between the data objects in more detail, often using data modeling languages such as SQL or ER diagrams.
- Physical level: The physical level involves defining the specific details of how the data will be stored, including data types, indexes, and other technical details.
Data Modeling Examples
The best way to picture a data model is to think about a building plan of an architect. An architectural building plan assists in putting up all subsequent conceptual models, and so does a data model.
These data modeling examples will clarify how data models and the process of data modeling highlights essential data and the way to arrange it.
1. ER (Entity-Relationship) Model
This model is based on the notion of real-world entities and relationships among them. It creates an entity set, relationship set, general attributes, and constraints.
Here, an entity is a real-world object; for instance, an employee is an entity in an employee database. An attribute is a property with value, and entity sets share attributes of identical value. Finally, there is the relationship between entities.
2. Hierarchical Model
This data model arranges the data in the form of a tree with one root, to which other data is connected. The hierarchy begins with the root and extends like a tree. This model effectively explains several real-time relationships with a single one-to-many relationship between two different kinds of data.
For example, one supermarket can have different departments and many aisles. Thus, the ‘root’ node supermarket will have two ‘child’ nodes of (1) Pantry, (2) Packaged Food.
3. Network Model
This database model enables many-to-many relationships among the connected nodes. The data is arranged in a graph-like structure, and here ‘child’ nodes can have multiple ‘parent’ nodes. The parent nodes are known as owners, and the child nodes are called members.
4. Relational Model
This popular data model example arranges the data into tables. The tables have columns and rows, each cataloging an attribute present in the entity. It makes relationships between data points easy to identify.
For example, e-commerce websites can process purchases and track inventory using the relational model.
5. Object-Oriented Database Model
This data model defines a database as an object collection, or recyclable software components, with related methods and features.
For instance, architectural and engineering real-time systems used in 3D modeling use this data modeling process.
6. Object-Relational Model
This model is a combination of an object-oriented database model and a relational database model. Therefore, it blends the advanced functionalities of the object-oriented model with the ease of the relational data model.
The data modeling process helps organizations to become more data-driven. This starts with cleaning and modeling data. Let us look at how data modeling occurs at different levels.
These were the important types we discussed in what is data modelling. Next, let’s have a look at the techniques.
Benefits Of Data Modeling
Data modeling is a critical process in the development of any software application or database system. Some of the benefits of data modeling include:
- Improved understanding of data: Data modeling helps stakeholders to better understand the structure and relationships of the data, which can help to inform decisions about how to use and store the data.
- Improved data quality: Data modeling can help to identify errors and inconsistencies in the data, which can improve the overall quality of the data and prevent problems later on.
- Improved collaboration: Data modeling helps to facilitate communication and collaboration among stakeholders, which can lead to more effective decision-making and better outcomes.
- Increased efficiency: Data modeling can help to streamline the development process by providing a clear and consistent representation of the data that can be used by developers, database administrators, and other stakeholders.
Limitations Of Data Modeling
Despite the many benefits of data modeling, there are also some limitations and challenges to consider. Some of the limitations of data modeling include:
- Limited flexibility: Data models can be inflexible, making it difficult to adapt to changing requirements or data structures.
- Complexity: Data models can be complex and difficult to understand, which can make it difficult for stakeholders to provide input or collaborate effectively.
- Time-consuming: Data modeling can be a time-consuming process, especially for large or complex datasets.
Evolution Of Data Modeling
Data modeling has evolved significantly over the years, reflecting changes in technology, data management practices, and business requirements. Early data modeling approaches were often manual and focused on the conceptual level, while more recent approaches use automated tools and support multiple levels of abstraction. Other trends in data modeling include the increasing use of data modeling languages and standards, such as SQL and UML, and the integration of data modeling with other data management processes, such as data governance and data quality. Overall, the evolution of data modeling reflects the ongoing importance of effective data management in today's data-driven business environment.
Types of Data Modeling
There are three main types of data models that organizations use. These are produced during the course of planning a project in analytics. They range from abstract to discrete specifications, involve contributions from a distinct subset of stakeholders, and serve different purposes.
1. Conceptual Model
It is a visual representation of database concepts and the relationships between them identifying the high-level user view of data. Rather than the details of the database itself, it focuses on establishing entities, characteristics of an entity, and relationships between them.
2. Logical Model
This model further defines the structure of the data entities and their relationships. Usually, a logical data model is used for a specific project since the purpose is to develop a technical map of rules and data structures.
3. Physical Model
This is a schema or framework defining how data is physically stored in a database. It is used for database-specific modeling where the columns include exact types and attributes. A physical model designs the internal schema. The purpose is the actual implementation of the database.
The logical vs. physical data model is characterized by the fact that the logical model describes the data to a great extent, but it does not take part in implementing the database, which a physical model does. In other words, the logical data model is the basis for developing the physical model, which gives an abstraction of the database and helps to generate the schema.
The conceptual data modeling examples can be found in employee management systems, simple order management, hotel reservation, etc. These examples show that this particular data model is used to communicate and define the business requirements of the database and to present concepts. It is not meant to be technical but simple.
These were the important types we discussed in what is data modelling. Next, let’s have a look at the techniques.
Data Modelling Techniques
There are three basic data modeling techniques. First, there is the Entity-Relationship Diagram or ERD technique for modeling and the design of relational or traditional databases. Second, the UML or Unified Modeling Language Class Diagrams is a standardized family of notations for modeling and design of information systems. Finally, the third is Data Dictionary modeling technique where tabular definition or representation of data assets is done.
Data Modeling Tools
We have seen that data modeling is the process of applying certain techniques and methodologies to the data in order to convert it to a useful form. This is done through Data Modeling tools which assists in creating a database structure from diagrammatic drawings. It makes connecting data easier and forms a perfect data structure according to requirement.
Those are the important tools we discussed in what is data modelling.
Importance of Data Modeling
It is clear by now that data modeling is necessary foundational work. It allows data to be easily stored in a database and positively impacts data analytics. It is critical for data management, data governance, and data intelligence.
- It means better documentation of data sources, higher quality and clearer scope of data use with faster performance and few errors.
- From the regulatory compliance view, data modeling ensures that an organization adheres to governmental laws and applicable industry regulations.
- It empowers employees to make data-driven decisions and strategies.
- It builds on business intelligence as it allows the identification of new opportunities by expanding data capability.
That was all about the article “What is Data Modelling”.
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Q1. What is data modeling?
The process of creating a visual representation of either part of a system or the entire system to communicate connections between structures and data points using elements, texts, and symbols.
Q2. What are the types of data models?
There are three types of data models: dimensional, relational, and entity relational. These models follow three approaches: conceptual, logical, and physical. Other data models are also there; however, they are obsolete, such as network, hierarchical, object-oriented, and multi-value.
Q3. What are the types of data modeling techniques?
The following are the types of data modeling techniques: hierarchical, network, relational, object-oriented, entity-relationship, dimensional, and graph.
Q4. What is the data modeling process?
The first step in the data modeling process is identifying the use cases and logical data models. Then create a preliminary cost estimation. Identify the data access patterns and technical requirements. Create DynamoDB data model and queries. Validate the model and review the cost estimation.
Q5. How can AWS help with data modeling?
You can use Amazon RDS (relational database service) to implement relational data models, Amazon Neptune to implement graph data models, and AWS Amplify DataStore for faster and easier data modeling to build web and mobile applications.
Q6. What are data modeling concepts?
Data modeling concepts answer the question of WHAT the system contains. A conceptual model helps to organize, scope, and define business concepts and rules. These concepts are created by data architects and business stakeholders.
Q7. Why is data modeling important?
An organized and comprehensive data modeling is crucial to create a simplified, logical, and physical database. It is necessary to eliminate storage requirements and redundancy and enable efficient data retrieval.
Q8. What are the types of data modeling?
The predominant data modeling types are hierarchical, network, relational, and entity-relationship. These models help teams to manage data and convert them into valuable business information.
Q9. What are the three levels of data abstraction?
Three levels of data abstraction are physical or internal, logical or conceptual, and view or external. The lowest form is physical, and the highest is the view. On a logical level, the information is stored in the database in the form of tables.