Data modeling is a crucial component for any business that can leverage data. Which is increasingly becoming the fate of every online business. There are tons of data touchpoints that businesses can analyze to increase sales, predict consumer behavior, improve the product, and optimize operations. However, analyzing all the volumes of data is difficult and time-consuming. Thus, data modeling helps organize the data in the right format to leverage this data effectively, easily, and quickly. A robust data modeling system is now a competitive advantage. Due to this, data modeling experts and developers are in demand these days. Moreover, all IT developers should also have some basic understanding to create and read data models.
What Is Data Modeling?
Data modeling is the process of designing data models. But what’s a data model? A data model is a plan to define and organize data structures in a database. It also helps visualize how these data structures interact with each other. This is done by defining the rules on how the data items are stored and processed for a given architecture or application. Thus, the data model represents the relationship between the data structures and their associated rules. These rules help implement the business logic for the product. They translate the working product in the real world into the corresponding storage and retrieval of associated data points. Therefore, both technical and business managers should collaborate on creating data models. They have to agree on the definition of these rules and the data objects. The data model should also align with the strategic goals of the organization.
In a data model, data objects are the touchpoints that businesses need to track and store. These data objects are like a table in a database. Each of these data objects has attributes. Attributes are additional details that you need to track for each data object. These are like columns in the table in the database. A group of data objects with the same properties is a data entity. For example, “user” can be a data object in the model, while “age” and “country” can be its attributes.
The business rules dictate how these data entities connect with each other. These interactions are called relationships. These relationships can be one-to-one, one-to-many, many-to-one, or many-to-many. These are also called cardinality in data modeling. These entities, attributes, and relationships are denoted using symbols in an Entity Relationship Diagram (ERD). Thus, the ERD provides a simple visual blueprint for the data model and its components.
Why Is Data Modeling So Essential?
As mentioned, data modeling is key for any business to effectively utilize their data. It helps to easily visualize the product architecture and the underlying business logic. This improves collaboration among multiple teams. There are many more benefits to implementing data modeling:
- Visual data models help to put every stakeholder in the organization on the same page. Different teams can understand, discuss, and collaborate on leveraging the data more seamlessly. Moreover, they help translate complex tech terms and architecture to a simpler diagram that even nontech teams can understand.
- Effective data models help a business become data driven by extracting more critical information from their data.
- It helps maintain data integrity and reduce risk. Thus, it makes the use of data more reliable, accurate, and trustworthy.
- An efficient data model empowers the organization to analyze and interpret the data quickly and easily. Because an unorganized format for storing and accessing data will slow down the process of using the data.
- Data modeling also makes it easier for the business team to communicate their requirements before final implementation. In turn, this helps reduce the time to production and saves development costs.
- Data modeling provides key business intelligence that can help optimize the processes of an organization. It gives a quick overview of all your business processes, how they fit in with each other, and their underlying data requirements. Thus, businesses will be better equipped to improve these processes.
- Data modeling makes information and insights easily available, presenting them in a consistent and timely fashion.
Types of Data Models
Depending on the level of abstraction, there are a few different types of data models. Each of them serves a different purpose and has a corresponding application.
Physical Data Modeling
The physical data model details how a particular physical database represents and stores its logical database components and services.
This physical database may be a relational database, as described below. It’s created using a database management system (DBMS). Thus, this model is a more accurate representation of how the data design is supposed to be implemented in the chosen DBMS. Hence, the physical data model will be different for each DBMS.
The physical data model usually derives from the logical data model, which in turn derives from the conceptual data model. The conceptual data model provides a simple overview of the business requirements. The logical model provides more information to this to add a bit of complexity. Finally, the physical data model provides all the necessary information to create the database. The entities in the logical model become table names in the physical model. Similarly, attributes become column names. Moreover, the data types, the primary and foreign keys, and the relationships between the tables are also specified.
Relational Data Modeling
The relational data model is the simplest data model design. Organizations use the relational data model mostly for entering data into a database. Thus, it works best for tools that need to input data into the system. It’s also highly scalable. The relational data model stores the relations between data entities in the table format. These tables are in the Third Normal Form (3NF). This reduces the chances of data duplication. The rows represent data values, while the columns represent the corresponding attributes. This primary structure allows it to have superior storage efficiency. The relational data model is the most used database system today.
Dimensional Data Modeling
The dimensional data model is a data modeling technique used to implement data warehousing systems. The process involves breaking down the business process into “facts.” Facts refer to the measure of the metric for the business process. Furthermore, the process manager should break down these facts to the lowest level of information. This will allow the organization to collect more granular data for each fact.
For example, the “cost of acquisition” can be a fact for the process of the “advertising campaign.” These facts are then further broken down into “dimensions.” Dimension refers to the additional information for each fact. An example of dimensions might be “social media channel,” “product sold,” and “date” for the fact “cost of acquisition.” Finally, the “attributes” are the columns in a dimension table that provide the characteristic of the dimension. For example, “month” and “day” can be the attribute for the dimension “date.”
The dimensional data model is designed to retrieve stored data as fast as possible. Thus, the optimized data querying leads to faster performance. The dimensional model is great for building reporting and analytics tools.
In today’s data-rich world, application developers should understand the fundamentals of data modeling. This will allow developers to collaborate more seamlessly with the business and executive teams. Thus, it’s a skill that can help you get promoted and differentiate you from other developers. Moreover, data modeling experts are very much in demand right now. Therefore, learning data modeling will be a great investment for your career.
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