DATA MODELLING: DEFINITION, TYPES, IMPORTANCE, AND BENEFITS

The use of abstraction to represent and illustrate the nature of data flow, to better understand the information system within an enterprise is what we refer to as data modeling.??

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The process of data modeling revolves around creating a visual representation of the entire information system or certain parts of it to communicate the connections between different data points and structures. The purpose is to illustrate the types of data that are getting used and stored, the relationships amidst these data types, the way the data is organized, its formats and attributes.?

??Preferably, data models evolve as per the changing times and needs of businesses and play an essential role in supporting various business processes, plans, IT architecture, and business strategy to attain a single aim: business growth.?Three primary attributes that we expect every data model to have in diverse business settings;??

• IT IS BUILT AROUND BUSINESS NEEDS;?through upfront feedback taken from business stakeholders and business process leaders, an idealized design model gets prepared or adapted to iteration an existing data model.?
• CAN BE MODELLED AT VARIOUS LEVELS OF ABSTRACTION;?a data model can be well-compared to an architect’s blueprint design where a formal visual diagram facilitates a deeper understanding of the enterprise’s information system be modeled according to business rules and requirements.?
• UTILIZES STANDARDISED SCHEMES AND FORMAL TECHNIQUES;?formal techniques and standardized schemes provide an enterprise with standard, consistent, and foreseeable ways of defining and managing data resources.??

Now, let’s have a sneak peek at some of the types of data models, grouped according to their degree of abstraction and use.?

TYPES OF DATA MODELS?

Any design process begins with a high degree of abstraction, whether in an information system or database, which continually becomes more concrete and specific.?Within data modeling, the degree of abstraction determines the type of model required, and for such reason, they have been subdivided into three main categories.??

1. Conceptual model; the conceptual data model is also referred to as a domain model, which offers a broader view of what the system contains how the data will be organized and conceptualized by the business rules. Conceptual models get created as part of gathering initial project requirements. It primarily includes entity classes (defining essential business things in the data model), characteristics and constraints, the relationship between different data sets, relevant security, and associated data integrity requirements.?
2. ??Logical model; on the other hand, Logical data models are lower in the degree of abstraction and provide extraneous details about the concepts and relationships of the domain under consideration. Its main function is to indicate data attributes such as data types and their corresponding lengths and present relationships among entities. It doesn’t call for any technical system requirements, making this data model debarred from agile or DevOps practices. The logical data model is most effective in highly procedural environments where projects are data-oriented by nature, like in data warehouse design.?
3. ??Physical model; the physical model provides a robust schema for storing the data in the database. One of the primary characteristics is that it is the least abstract of all other data models. A physical data model offers a finalized design that can be implemented as a relational database; it sets relationships among entities, including primary keys and foreign keys, which maintain the relationship with data. This type of data model consists of a data management system (DBMS); certain specific properties comprise performance turning.?

DATA MODELLING: IMPORTANCE AND BENEFITS??

Data modeling is the core foundational work needed for the further data analytics process. It enables data to be easily stored in the database and plays a critical part in data management, governance, and data intelligence. Overall, it helps businesses to make data-driven informed decisions and to build strategies to ensure that the organization adheres to regulatory government compliance laws and regulations.??

Additionally, data modeling makes the task of developers, data architects, and business analytics way more manageable, as it helps them view and understand the relationships between different data points and databases.??

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