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Data Modeling

Data Modeling is just like drawing Blueprint of our New home to be build. So, the layout is used to define the structure of the house to efficiently support the requirement. Likewise, Data modeling is the procedure of diagramming data flows. When creating a new or alternate database structure, the designer starts with a diagram of how data will flow into and out of the database. This flow diagram is used to define the characteristics of the data formats, structures, and database handling functions to efficiently support the data flow requirements. After the database has been built and deployed, the data model lives on to become the documentation and justification for why the database exists and how the data flows were designed.

The data model that results from this process provides a framework of relationships between data elements within a database as well as a guide for use of the data. Data models are a foundational element of software development and analytics. They provide a standardized method for defining and formatting database contents consistently across systems, enabling different applications to share the same data.

Why is data modeling important?

A comprehensive and optimized data model helps create a simplified, logical database that eliminates redundancy, reduces storage requirements, and enables efficient retrieval. It also equips all systems with a ‘single source of truth’ – which is essential for effective operations and provable compliance with regulations and regulatory requirements. Data modeling is a key step in vital functions of a digital enterprise and software development projects (new or customizations) performed by IT professionals.

Before designing and building any software project, there must be a documented vision of what the final product will look like and how it will behave. A big part of that vision is the set of business rules that govern the desired functionality. The other part is the data description – the data flows (or data model) and the database design to support it.

Data modeling keeps a record of the vision and provides a roadmap for the software designers. With the database and data flows fully defined and documented, and systems developed per those specifications, the systems should deliver the expected functionality required to keep the data accurate (assuming that procedures have been properly followed.)

Today’s data models transform raw data into useful information that can be turned into dynamic visualizations. Data modeling prepares the data for analysis: cleansing the data, defining the measures and dimensions, and enhancing data by establishing hierarchies, setting units and currencies, and adding formulas.

What are the types of data modeling?

Object-oriented Model

The object-oriented approach is the creation of objects that contains stored values. The object-oriented model communicates while supporting data abstraction, inheritance, and encapsulation.

Network Technique

The network model provides us with a flexible way of representing objects and relationships between these entities. It has a feature known as a schema representing the data in the form of a graph. An object is represented inside a node and the relation between them as an edge, enabling them to maintain multiple parent and child records in a generalized manner.

Entity-relationship Model

ER model (Entity-relationship model) is a high-level relational model which is used to define data elements and relationship for the entities in a system. This conceptual design provides a better view of the data that helps us easy to understand. In this model, the entire database is represented in a diagram called an entity-relationship diagram, consisting of Entities, Attributes, and Relationships.

Relational Technique

Relational is used to describe the different relationships between the entities. And there are different sets of relations between the entities such as one to one, one to many, many to one, and many to many.

Hierarchical Technique

The hierarchical model is a tree-like structure. There is one root node, or we can say one parent node and the other child nodes are sorted in a particular order. But, the hierarchical model is very rarely used now. This model can be used for real-world model relationships.

What are the three levels of data abstraction?

There are many types of data models, with different types of possible layouts. The data processing community identifies three kinds of modeling to represent levels of thought as the models are developed.

1.Conceptual data model

This is the “big picture” model that represents the overall structure and content but not the detail of the data plan. It is the typical starting point for data modeling, identifying the various data sets and data flow through the organization. The conceptual model is the high-level blueprint for development of the logical and physical models and is an important part of the data architecture documentation.

2.Logical data model

The second level of detail is the logical data model. It most closely relates to the general definition of “data model” in that it describes the data flow and database content. The logical model adds detail to the overall structure in the conceptual model but does not include specifications for the database itself as the model can be applied to various database technologies and products. (Note that there may not be a conceptual model if the project relates to a single application or other limited system.)

3.Physical data model

The physical database model describes the specifics of how the logical model will be realized. It must contain enough detail to enable technologists to create the actual database structure in hardware and software to support the applications that will use it. Needless to say, the physical data model is specific to a designated database software system. There can be multiple physical models derived from a single logical model if different database systems will be used.

Data modeling process and techniques

Data modeling is inherently a top-down process, starting with the conceptual model to establish the overall vision, then proceeding to the logical model, and finally the detailed design contained in the physical model.

Building the conceptual model is mostly a process of converting ideas into a graphical form that resembles a programmer developer’s flow chart.

Modern data modeling tools can help you define and build your logical and physical data models and databases. Here are a few typical data modeling techniques and steps:

Determine entities and create an entity relationship diagram (ERD). Entities can be better described as “data elements of interest to your business.” For example, “customer” would be an entity. “Sale” would be another. On an ERD, you document how these different entities relate to each other in your business and which high-level connections exist between them.

Define your facts, measures, and dimensions. A fact is the part of your data that indicates a specific occurrence or transaction, like the sale of a product. Your measures are quantitative, like quantity, revenue, cost, and so on. Your dimensions are qualitative measures, such as descriptions, locations, and dates.

Create a data view link using a graphical tool or via SQL queries. If you are unfamiliar with SQL, the graphical tool is the most intuitive option, allowing you to drag and drop elements into your model and visually build your connections. While creating a view, you have the option to combine tables and even other views into a single output. When you select a source in the graphical view and drag it on top of a source already associated with the output, you will have the option to either join or create a union of these tables.

Modern analytics solutions can also help you select, filter, and connect data sources using a graphical drag-and-drop display. Advanced tools are available for data experts typically working in IT – but users can also create their own stories by visually creating a data model and organizing tables, charts, maps, and other objects to tell a story based on data insights.

From this blog, hope you understand the importance of Data Modeling.


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