What is Data Modeling?

Data Modelling means data analysis in its simplest form. It is one of the best ways to understand, store and manage data. It is made by data scientists and modelers. At the same time, those who work with data modeling are also called designers. When developing a new or different database structure, the designer first creates a diagram of how data will enter and exit the database. This is also called a data flow diagram (VAD). This schema is used to describe the properties of data formats, structures, and database processing functions. Once the database is created, the data model provides documentation of the reason for the database's existence and the way data flows are designed.

The resulting data model shows the relationships between data items in a database and allows you to obtain a guide for using the data. Data models are a fundamental element for software development and analytics. They allow different applications to share the same data, providing a standardized method for consistently identifying and formatting database content across systems.

Why is Data Modeling Important? What Does It Do?

An expanded and optimized data model helps develop a simplified, logical database that eliminates redundancy, reduces storage requirements, and allows efficient retrieval. In addition, all systems have a 'single source' based on real data. This is necessary for efficient operation and provability in regulations. Data modeling is also an important step in enabling a digital business to maintain its core functions.

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Data Modeling for IT Professionals

Before a software project is designed and built, there must be a documented vision of how the final product will look and work. A large part of this vision consists of a set of business rules that determine 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 follows the vision at this stage and provides a roadmap for software designers. Assuming that procedures are followed correctly, systems can provide the expected functionality needed to keep the data accurate, as the database and data flows are fully defined and documented, and the systems are developed in accordance with these specifications.

Decision Making Tool for Data Analytics and Visualization

Due to the ever-increasing volume of data and the growing number of users, organisations need a way to transform raw data into information that can be used to make decisions. Therefore, the demand for data analytics is quite high. Data visualisation makes data more accessible to users by displaying it graphically.

Current data models, on the other hand, turn raw data into useful information that can be turned into dynamic visualizations. In short, data modeling cleans up data to prepare data for analysis, defines measures and dimensions, adjusts units and currencies by creating hierarchies, and improves data by adding formulas.

Data Modeling and Types

Analyzing data using data modeling is an important part of data management. Therefore, data modeling is also an analysis and design method for finding, analyzing and determining the scope of data needs. Modeling, on the other hand, has to do with requirements in the field of writing (syntax), meaning (semantics) and the interrelationships between these data. The precise display and transmission of these data requirements can be done using a data model.

A data model, on the other hand, can take different forms and can be distinguished conceptually, logically, and physical/technical, depending on the use. Conceptual data modeling and logical data modeling are requirements analysis activities, while physical data modeling is a design activity. Logical data models, which include entity types and attribute types, are built on the basis of a conceptual data model (also known as an information model - without attributes). The data itself, on the other hand, is stored in a database, as it forms instances of logical data models and allows you to obtain technical data models.

There are basically three types of primary data models; relational, dimensional, and entity-relationship (ER). In addition, there are many data modeling methods that are less general, including hierarchical, network, object-oriented, and multi-valued ones. The model type describes the logical structure, that is, how data is logically stored, organized, and retrieved.

Although “obsolete” in the relational approach, the most common database model that is still in use today is relational. Data is stored in fixed-format records, organized into tables with rows and columns. The two elements of the most basic type of data model are measurements and dimensions. Measures are quantities used in mathematical calculations, such as totals or averages, and numerical values such as income. Dimensions, on the other hand, can be text or numeric. They are not used in calculations, they contain descriptions or positions. Raw data is defined as a measure or dimension. Other terms used in relational database design include “relationships” (rows and columns table), “attributes” (columns), “bundles” (rows).

Since the dimensional approach is less structured, it supports a contextual data structure that is more relevant to business use. This database structure is optimized for online queries and data storage tools. Critical data items such as transaction amount are called “facts” and are accompanied by reference information or “dimensions”. Available in product ID, unit price or transaction date data. The case table is the primary table in a dimensional model. Data can be retrieved quickly and efficiently by being stored together for a particular type of activity, but the lack of relationship links can make it difficult to receive and use the data analytically. Since the data structure depends on the business function that generates and uses the data, combining data from different systems (for example, in a data warehouse) can create problems.

An ER model represents a business data structure in graphical form with rows to describe dependencies or “relationships” in different ways to represent activities, functions, or “assets”. The ER model then creates a relational database where each row represents an entity and the fields in that row contain attributes. As with all relational databases, important data elements are used to link tables.

Data Modeling Techniques

Data modeling is a systematic process in which detailed design is performed in order to create the overall vision first, then the logical model, and finally the physical model. Creating the conceptual model often turns ideas into a graphical form similar to a programmer/developer's flowchart. Modern data modeling tools help you define and build your logical and physical data models and databases. Here are some common data modeling techniques and steps:

• Identify assets and create an “asset-relationship” diagram (ERD): Assets are also defined as “data items that are important to your business.” For example, a “customer” or “sale” is an asset. In an ERD, you can document how these different entities are related to each other and what connections exist between them.
• Define facts, measurements, and attributes: Facts are the part of your data that indicates a specific event or process, such as the sale of a product. Measurements are quantitative, such as quantity, income, cost, etc. Attributes, on the other hand, are qualitative measures such as descriptions, locations, and dates.
• Create a data display link using a graphical tool or SQL (Structured Query Language) queries: If you're not familiar with SQL, the graphing tool is the most intuitive option. This method allows you to drag elements into your model and visualize your links. When you create a view, you have the option to combine tables and even other views into a single output. You can combine these tables or create a combination by selecting a source in graphical view and dragging it over a source that is already connected to the output.

Modern analytics solutions can also help you select, filter and connect data sources through drag-and-drop graphical representation. Advanced tools are available for data professionals working in IT, but users can also create their own scenarios by building a data model and organising tables, charts, maps and other objects to tell a story based on data insights. If you want to perform your data analyses efficiently and store data in a healthy way, you can apply data modelling methods and make the process more practical by using the data services offered by Komtaş.