Data modeling is the process of creating visual representations of whole systems or parts to show connections between data points and structures. The goal is to show the types of data stored in the system, their relationships, and the formats and attributes of the data.
Data models are designed to meet business requirements. Business stakeholders provide feedback to help define rules and requirements that can be used to design a new system or adapt an existing one.
There are many levels of abstraction that data can be modeled at. It starts by gathering information from stakeholders and end-users about business requirements. These business requirements are then converted into data structures to create a concrete database design. A data model is similar to a blueprint, a map, or any other formal diagram that helps you understand what is being created.
Data modeling uses standardized schemas and formal methods. This allows for a consistent and predictable method of managing data resources within an organization or even beyond.
Ideally, data models should be living documents that adapt to changing business requirements. Data models are essential for supporting business processes and planning IT architecture and strategy. Data models can be shared among vendors, industry peers, partners, and/or other parties.
Importance And Benefits of Data Modeling
Data modeling is an important stage in any software project. Without it, it's impossible to get a clear picture of how your database should look and how it will be used.
It is a way to determine the relationships between various pieces of information. This will help you decide what kind of queries can be run against this data.
Data modeling supports Business Architecture, which in turn is a data model for a business that an organization uses to align business and technology goals. Data modeling also supports other elements of Business Architecture, such as Data Governance, Business Intelligence, and Application Architecture, by helping to define their requirements and schedule.
Without a data model, you could end up with a system that isn't user-friendly.
These are just a few reasons why your applications need a solid data model; now, let's discuss the benefits of data models:
#1 Higher Quality Applications
Data modeling has the obvious advantage of producing higher-quality applications that are low maintenance and are less susceptible to crashes.
Here's what happens if you don't use data models:
- You take up raw input from users and stuff it into variables.
- The code allows you to manipulate these variables, creating new values which can then be loaded into other variables.
- Continue this process until you are several levels deep and hopelessly lost.
It doesn't matter how big or small your company is. Your application will look like spaghetti code if it doesn't have a structure. Your code will become a mess if it is ever changed or new features are added to it.
#2 Reduced Cost and Time in Application Development
Data modeling can significantly impact how much and how quickly you can build an application. You will have to gather requirements from your users and code the database structure manually if you don't have a data model.
It is easier to add tables and views to a data model if you already have one. If you build an app and need to add or modify a table, you can add it to the data model and update your existing application.
Your team will need to update the database and code if you don't have data models. If you have to make multiple changes to the entire application, this can be time-consuming and costly.
#3 Early Detection of Data Issues & Errors
Often, errors and data issues are not detected until the process progresses. A user may go to make a purchase but get an error message stating "bad data". This is a sign that the data was not good from the start. It is possible to test it in a laboratory or test server. However, errors are not discovered until production runs.
You have more time to fix a problem before it affects your users.
Many companies use data modeling because it provides a detailed view of your users' interactions with your business, down to the details such as which fields they access and how frequently they use them. This level of detail provides crucial information that can help you identify problems and make the right corrections. Regular Data Model Audits will ensure that your data model is optimized for users and their goals.
#4 Faster Application Performance
Data modeling is more than just about making or saving money. While that's certainly important, the true value of data modeling lies in making your application run more efficiently and faster.
Data modeling is crucial to an application's performance because it gives a high-level plan of how the application should deal with data. Developers know what data they can expect, how they will use it, and where each piece of information will be kept in their memory. They can therefore write simple functions that retrieve data quickly.
This is a very different approach to using tables to store data in an unorganized way. Developers would need to write complex SQL queries to find the information they are looking for if unstructured tables were used. Structured tables will allow the database engine to locate the information, and developers won't worry about it.
What is the result? Applications can handle large data volumes more efficiently without slowing down.
#5 Better Documentation for Long-Term Maintenance
Data models are used to describe business processes and their interrelationships. It is easier to understand and maintain long-term business processes if all data related to them are in one place.
Data modeling is also useful in documenting business requirements and designing the application. A single source can help communicate the requirements and design better. Identifying and implementing changes is easy due to new requirements, enhancements, or bug fixes.
Data modeling is an essential part of software development. It requires expertise and effort, but the rewards are well worth it.
Different Types of Data Models
data model is a blueprint that describes the organization's internal information structure. They ensure that all information can be accessed easily by key business stakeholders and authorized personnel.
Data models are created by looking at the current information, identifying the entities, and then determining their relationship to one another. This is similar to an organizational diagram, except that it does not highlight lines of authority but rather shows how information is organized.
Data modelers can use many techniques to create models. There are three main types of data modeling:
1. Conceptual Data Model
Every data model is built based on conceptual data models as its foundation. Conceptual data models allow you to identify the entities in your business and their relationships. The details of the attributes that are attached to an entity's conceptual model don't get included in this model.
A conceptual model is a diagram that describes your business and its relationships. This is a view of entities and their relationships in a hierarchical structure. It's often created to provide stakeholders with a general overview of the database. Data modeling tools will help you create a conceptual schema for your database in no time.
Before creating a conceptual data model, you need to ask these questions: What purpose does your database serve? Who will use it? What will it be used for? This will allow you to identify which entities are in your database and what relationships exist between them.
2. Logical Data Model
The logical Data Model is about how data is stored within an organization's systems. This model shows how data flows between its source (for instance, a person) and its destination (such as a database). To describe each entity in a relational table, it uses entities, attributes, relationships, cardinality, and constraints.
The foundation for creating physical data models is the logical data model. These models can define tables in relational databases and objects in object-oriented languages like SQL, Java, and C++.
3. Physical Data Model
Creating a physical data model defines the structure of a database schema that stores information. A database administrator or system analyst usually creates the physical model. It is used to create views, tables, and indexes. These are then implemented using Structured Query Language (SQL) statements.
Data modeling is the simplest type. It involves creating models that explain how data should be stored in tables. These models can then be implemented in one or more databases. Data modeling can be more complicated by creating a logical model of how data will interact with end-users.
Data Modeling: Key Terms You Should Know
- Entity: An entity is a specific item, such as an employee, a department, customer, computer, that exists in a company.
- Relationship: These are the connections between entities. A relationship between a department and an office entity might mean that an office contains a department. Conversely, a relationship between an employee and a department entity might mean that the employee is a member. A one-to-one relationship means that each instance of an entity is related to one instance of a second entity. A one-to-many relation means that each instance of one entity is related to one or more instances. Many instances of one entity can relate to many others in a many-to-many relationship.
- Attribute: An attribute is a property or relationship. An example of an attribute might be the name, address, phone number of the employee.
- Domain: You can define and populate a domain with metadata (such as data types or validation rules, dependencies, default values, etc.). Domains can be used to replace standard data types to maintain the consistency of data types in a database. Suppose your company has offices in France and United States. In that case, you could create a US_HOME_PHONE domain with a variable character data type of 12 and a FR_HOME_PHONE domain with a variable data type 8. These domains can be used for any entity attribute (e.g., customer's number or employee's home number) in the database.
- Data dictionary: It is a set of metadata. The data dictionary is a read-only collection of tables and views created when the data model has been implemented in a DBMS.
- Supertype: A supertype refers to a generic entity that has a relationship with one or several subtypes. A supertype is, for example, an entity of an employee.
- Subtype: A subtype refers to a group with common attributes or relationships within an entity. An example is the employee entity, including the subtypes exempt and not-exempt.
Data Modeling Developing Cycles
There are many stages in the life of a Data Model, and it is important to understand them all.
Stage 1: Gathering Business Requirements
The Data Modeler must interact with business analysts to obtain functional requirements. They will also need to interact with the end-user to determine the reporting and service level requirements.
Stage 2: Conceptual Data Model (CDM)
The data model contains entities at a high level. This is the initial phase of the model and will not provide much detail. This will make it easy for the user to understand the system and not get lost.
Highlight(s): The CDM is a key step in building a data model using a top-down approach. It provides a visual representation of an organization's business requirements. CDM shows the overall database structure and provides high-level information about the subject areas. It will begin with the main subject area, then move through each entity to understand it more fully. CDM includes data structures that are not yet implemented in the database. This phase is where technical and non-technical groups can share their ideas to build a solid data model. This model includes entity types and relationships (one-to-one, one-to-many, and many ones to many).
Stage 3: Logical Data Model (LDM)
This stage takes the high-level entities created in stage 2 and improves the model to reflect the organization's business needs. This involves changing the entities to tables and adding details to them. These details can be displayed by adding attributes (columns), relationships among tables (primary and foreign keys), as well as business requirements (constraints).
Stage 4: Physical Data Model (PDM)
This is the complete model used to implement the organization's policies. This includes the tables, columns, relationships, constraints, and other information required to implement the model physically. This can be customized for a particular vendor.
Stage 5: Database
The physical model is converted into SQL Code specific to the vendor and executed against the server to create the target database.
Future Of Data Modeling
Data modeling is an evolving paradigm. The three-schema approach to data modeling dates back to the 1970s. This is why the rise in cloud storage, computing, and other technologies might disrupt the process.
However, data modeling and the cloud will continue to be valuable for businesses. It will also remain a crucial part of planning for the future.
- These techniques provide clear lines for cross-functional communication in an ever-changing technological environment.
- Data modeling allows stakeholders to decide how and when to move data to the cloud as companies shift their infrastructures.
- A morphing data ecosystem is evident in the rise of its variants, vast improvements in infrastructure and data storage (most recently fueled by this expansion in cloud computing), and growing interest in streaming. It is essential to understand how to integrate these foundational technologies and anticipate the next "big shift" in the data ecosystem.
Data modeling is fundamentally a paradigm that involves careful data understanding and analysis before taking action. These trends will only make data modeling more valuable.
Data Modeling is a practice that helps in the visual representation of data. Data models are created during a project's analysis and design phases to meet application requirements. Data Modeling is here to help.