Data Modeling – Purpose Of a Data Model in Data Science

Data modeling is an essential component of data science workflow. The purpose of data modeling is to generate more reliable, structured, and consistent data for business applications and reliable results. The several categories of data models, the data modeling process, and a few data modeling tools are covered in this blog.

How does Data Modeling work?

A data model is a graph database construction approach that allows you to start with general ideas. Defining actual objects and their properties provides a uniform framework. These entities have been reduced to their most basic parts and are often related in some way. Everything is expressed in a relatively straightforward manner, including commodities, their attributes, and relationships between objects. Data models can be used to explain how entity data is arranged, saved, and searched.

There are many benefits to building and maintaining a data model for your database. These data models will be described and shown in more detail below. The benefits of having a data model from a business perspective are as follows:

• The Data Architect and the business people can communicate more quickly since a data model creates a common communication layer. Since it is a visual model with standard terminology, it guarantees that all the details may be discussed in detail.
• High-quality documentation guarantees high-quality implementation of the code. The code improves on the choices made in the earlier documentation step, which lowers the error rate.
• Because less code needs to be altered, developers may devote more time to creating new features. As a result, less coding time is required, and some expenses are saved.
• The scope of the project can be determined by building a data model. Because of the Data model's visual representation, complexity is reduced, and complex concepts are understood. Sharpen your skills with the best data science course online and learn to solve difficult problems. 

Benefits of Data Model

The benefits of a data model in terms of technology are as follows:

• Technical Layer: A technical layer is affixed to a data model and contains all the technical data (defined by the data architect). This allows developers to focus on execution rather than interpretation.

• Fewer Errors: There are fewer errors. The Data model's correctness and clarity lead to fewer errors on the data and application sides. Instead of focusing on database design, developers may work on feature development.

• Database Structure Optimization: The database structure can be improved before any data is input. As a result, less data needs to be transferred.

• Data Risk Reduction: Data risks are decreased. If Data Architects and Database Administrators have a better idea of the extent of the data, they may create backup and restoration procedures. Having plans and protections in place reduces risks in disaster recovery scenarios.

Types of Data Modeling in Data Science 

Data modeling has advanced alongside database management systems as businesses' needs for data storage have increased, with model types growing more complex. These are a few examples of several model types:

• Hierarchical data models: One-to-many relationships are represented using a hierarchical data model with a tree-like structure. With this architecture, each record has a single parent or root table corresponding to one or more child tables. The IBM Information Management System (IMS), introduced in 1966 and quickly gained popularity, embraced this concept, especially in the banking sector. This approach is used in XML systems and Geospatial Information Systems, despite being less effective than more recently developed Database models (GISs). Refer to the data science courses, for detailed explanations on types of data models. 

• Relational Data Models were introduced in 1970 by E.F. Codd, a researcher with IBM. They are still used in relational databases of today, which are often used in business computers. Relational data modeling does not require in-depth familiarity with the technical details of the data storage technology. By directly linking Data segments using tables, it lessens database complexity.

• Models for entity-relationship (ER) data: ER Data Models use formal diagrams to express the connections between entities in a database. To create visual maps that convey database design goals, data architects use several ER modeling tools.

• Object-oriented Data Models: Around the middle of the 1990s, these many kinds of data models rose to prominence at the same time as object-oriented programming. The "things" in question are fictionalized versions of real things. Hierarchies of classes, each with a unique set of properties, are used to arrange objects. Tables can handle more complex data exchanges and can be used in object-oriented databases. Among other applications, this technique is employed in multimedia and hypertext databases.

• Models for Dimensional Data: Dimensional Data Models were developed by Ralph Kimball to hasten data retrieval for analytical applications in a Data Warehouse. Dimensional models use redundancy to make finding information for reporting and retrieval simpler, in contrast to relational and ER models emphasizing efficient storage. Modeling in this way is typical of OLAP platforms.

Data Modeling Tools

Data modeling, diagramming, and visualization tools are just a few of the commercial and open source Computer-Aided Software Engineering (CASE) solutions that are widely used today. These are a few instances:

• Data modeling tool Erwin Data Modeler is based on the Integrated DEFinition for information modeling (IDEF1X) Data modeling language and supports multiple notation approaches, including a dimensional approach.
• Enterprise Architect is a visual modeling and design tool for enterprise information systems, architectures, software programs, and databases. Object-oriented languages and standards serve as its cornerstone.
• Database design software ER/Studio integrates with several popular database management systems used today. It supports relational and dimensional data models.
• Free data modeling tools include Open Source Solutions like Open ModelSphere.

Conclusion

It will be advantageous to create data models as a software developer or data architect. Knowing when to use the suitable data model and how to involve key stakeholders in the decision call process is helpful. This blog gives you an insight into the different kinds of data models and the data modeling technique. If you are interested in exploring more about the data science techniques, head to the best data science courses in India, and become an IBM-certified data science professional.