During our school days, almost every one of us has created models for science. The very famous model of the heart on thermocol sheets depicts the way our heart pumps blood to the different parts of our body and the way the blood reaches back to the heart.
Clearly, the models are created to make the process easy to understand and remember all the way. It shows clearly how arteries and veins are connected to various parts and how the heart works. The structure is so complex that it is difficult to understand without looking at the model.
Similarly, Data models are built to make complex data readable and understandable. With massive amounts of data generated every second across the globe through our activities over the Internet, it is crucial to have a technique to organize such huge quantities.
It is important to verify that the data is utilized fully and efficiently so that your business is improved. To ensure the accuracy, extensibility, interpretability, and coverage of data, you need data models.
A data model helps you in defining the structure of data and the mistakes, and eventually the valuable insights. Today, data analytics is important in every sector regardless of the size and type of industry.
The demand for data analysts is making the headlines across the globe. According to the predictions for the future workforce through 2022 by the World Economic Forum, it is expected that 85% of companies will have adopted big data and analytics techniques by 2022.
This is the primary reason for taking Data Analytics courses made available by online training institutes so that you can make a career in the ever-evolving domain.
What is Data Modeling?
The process that involves defining the structure, connections, and constraints that are meant for available data is referred to as Data Modeling.
Data modeling is all about creating visual representations of information systems as a whole or parts of it to communicate connections between structures and data points. The primary objective of data modeling is to interpret the type of data used and stored within the system, the kind of connection among these data types, and the different ways in which the data can be grouped and arranged, and its attributes and formats.
Data models are built according to the requirements of a business. Rules and the requirements are defined in advance via feedback collected from business stakeholders such that they can be assimilated into the design of the new system or into an iteration of the existing model.
There are various levels of abstraction for data modeling. The first step is to collect information about the requirements of the business from the stakeholders and also from end-users. The second step involves transforming these business rules into data structures such that a concrete database design can be formulated. A data model formed can be considered as a roadmap, a blueprint by an architect, or a design diagram made by a civil engineer in such a way that it makes clear what is being designed.
There are standard schemas and formal techniques you need to employ during data modeling. The standard schemas are defined in such a way that facilitates a common, consistent, and predictable way of identifying, defining, and managing data resources across an organization or beyond.
So, typically data models are those living entities that evolve with changing business requirements. Data models play a crucial role in assisting business processes and planning strategies, and IT architectures. They can be shared with partners/vendors or industry peers.
Data modeling is an integral part of any analytics deployment and project of business intelligence.
Types of Data Models
Generally, data models are divided into three broad categories that vary as per their degree of abstraction. The process of data modeling begins with a conceptual model and progresses towards the logical model that concludes with a physical model.
Let us go through these data models in brief.
- Conceptual Data Models:
They are also called domain models and provide the master plan of what the system will contain the way it will be organized, and the business rules that are to be applied. These models are generally created as a part of the process of collecting the requirements of the initial project. This includes entity class which defines the type of things that are crucial for the business to represent in the data model, their constraints and characteristics, the connections between them, and the requirements related to security and data integrity. The notations included in this model are usually simple.
- Logical Data Model:
In this model, the level of abstraction is lesser and impart greater detail regarding the concepts and relationships in the domain which is considered. One out of different data modeling notation systems is followed. These models indicate the data attributes, data types, and connections between them. But no technical system requirements are specified by logical models. Generally, this step is eliminated from DevOps and Agile practices. Logical data models are of great help when for highly procedural implementation environments and for projects such as reporting systems or data warehouse design.
- Physical Data Models:
The physical data model imparts a schema of the way data is to be stored in the database. Clearly, physical models are the least abstract of all. This model provides a final design that is to be implemented as a relational database that may include associative tables describing the relationships between entities. This also includes a description of primary keys and foreign keys that are used to maintain those connections. Physical data models may also include DBMS systems along with their specific properties.
Data Modeling Process
The steps involved in the process of data modeling include the following steps:
- Identifying the entities.
- Identification of key features of each entity.
- Defining relationships among those entities.
- Mapping of attributes to entities completely.
- Assigning keys as per the requirements and opt on a degree of normalization such that the requirements are balanced thereby reducing redundancy.
- Finalizing and validating the data model.
Benefits of Data Modeling
- Minimizes errors in the process of database and software development
- Enhances the performance of application and database
- Data mapping is made easy through organization
- Improves consistency in documentation and system design
- Enhances interaction between developers and Business intelligence teams
- Quicker process of database design via conceptual, logical, and physical models
Today, data analysts and business intelligence experts are deploying data models for every project irrespective of the type of sector they are working in. This helps them in carrying out the project easily and minimizes errors.
If you wish to upgrade your career, just enroll yourself in an online training institute and take up a course. One good thing about these courses is that there are industry experts who make you go through real-life projects so that you are prepared completely to take up the certification exam.
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