What is Data Modeling?

office clerk working with statistical reports and entering data into a computer

We have all dealt with data in our daily lives Data is nothing but raw information, but what is data modeling? Diagrams and symbols are used to represent data elements in a software system and their movement through the data model. Data models serve as a guide while establishing a new database or re-engineering an old database. Overall, data modeling assists businesses in maximizing their information resources.

A data model can be viewed as a flowchart that depicts the relationships and attributes of data entities. This tool enables documentation and analysis of application data requirements prior to code creation. Existing data models can be reverse-engineered. This approach documents ad hoc, relational databases and designs schemas for raw data stored in data lakes or NoSQL databases to support analytics applications.

What are the various types of Data models?

It is typical for data modelers to develop three distinct models to represent business concepts, methods connected to the required data entities, and the technology structures required to handle and store the data. When businesses plan new applications and databases, they frequently generate the models sequentially. The following is a list of the several data models and the information they hold:

  1. Theoretical Data model: This is an overview of how a system would assist with business or analytical procedures. You can choose what data is required, how various business organizations are interconnected, and the rules that govern them. To demonstrate to company leaders how a system will function and guarantee that it meets their requirements, you must employ conceptual data models. A conceptual model is independent of any specific database or application technology.
  2. Logical Data model:  This information can subsequently be utilized to develop a less abstract logical data model. Logical data models emphasize the connections between data pieces and provide a technical representation of the data. As an illustration, they construct data structures and provide details on properties, keys, and data types. The technical side of an organization uses logic models to better comprehend the design needs of its applications and databases. However, like conceptual models, they are not connected to a certain technology platform.
  3. Physical Data Model: On top of a logical data model is a physical data model. Implementations of the database management systems (DBMS) or application software require the use of physical models. The database or file system will leverage these data storage and management components. Included are more DBMS components such as tables and their columns, fields, and indexes. Database designers employ physical data models to develop database designs and generate database schemas.

What are the techniques for Data Modelling?

Data modeling arose in the 1960s to improve mainframe and minicomputer databases. It offered consistent, repeatable, and regulated data processing and management. Data models haven’t changed much, but methods for constructing new databases and computer systems have.

Despite being replaced by newer ways, several of these have remained popular.

1. Using a hierarchical data structure model

In a hierarchical data model, parent and child records are organized in a tree-like structure. This modeling strategy is one-to-many, as a child record can only have one parent. Information Management System (IMS) from IBM is the most well-known example of the hierarchical method in mainframe databases. In spite of the fact that relational data models mostly supplanted hierarchical ones in the 1980s, many firms continue to utilize IMS. Similarly, XML, also known as Extensible Markup Language, employs a hierarchical structure.

2. The modeling of network data

In addition, this was a common data modeling option in mainframe systems, albeit it is less popular now. Hierarchical data models gave rise to the ability to link child records to many parents in network data models. The Conference on Data Systems Languages (CODASYL), a defunct technical standards organization, adopted a network data model specification in 1969. The CODASYL model is hence usually referred to as the network approach.

3. Data modeling in a relational fashion

Relational data models have superseded hierarchical and network models, allowing more data flexibility. Edgar F. Codd’s relational model maps data elements in rows and columns. By the mid-1990s, it was the dominant data modeling technique due to its use in developing relational databases.

4. Modeling of entity-relationship data

Entity-relationship (ER) models are a variation of the relational model that depicts entities, their properties, and their relationships when used with various types of databases. For instance, the properties of an employee data object may include last name, first name, years of service, and other essential information. ER models are useful for transaction processing applications due to their efficient ways of data collection and update.

5. The representation of dimensional data

Business intelligence data warehouses and data marts use dimensional data modeling. In fact tables, transaction, or event data is recorded, while dimension tables store entity characteristics. Connected dimension tables can hold the item and consumer data in a fact table. Star schemas link a fact table to several dimension tables; snowflake schemas include multiple tiers of dimension tables.

6. Data modeling using an object-oriented methodology

In the 1990s, object-oriented programming and object databases led to object-oriented data modeling. Object-oriented techniques represent data properties and relationships like ER. Existing classes can transfer their traits and behaviors to new ones, grouping similar objects. Object databases remain a specialized technology for specific applications, limiting OOM.

7. Graph Data Modelling

Graph data models are more recent than network and hierarchical data models. It is widely used in the context of graph databases to describe data sets with a high degree of interconnection. Graph data modeling is utilized by a number of applications in the fields of fraud detection and recommendation engines. In a property graph data model, nodes that represent data items and record their properties are connected by connections, also known as edges or links, that indicates how various nodes are related.

What data modeling can and cannot accomplish for you.

With well-designed data models, it is easier to develop and implement a data strategy that maximizes the use of an organization’s data. Individual databases and applications that use efficient data modeling are better suited to meet corporate objectives for data processing and administration.


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