Updated February 27, 2023
Introduction to Multidimensional Data Model
Multidimensional Data Model can be defined as a method for arranging the data in the database, with better structuring and organization of the contents in the database. Unlike a system with one dimension such as a list, the Multidimensional Data Model can have two or three dimensions of items from the database system. It is typically used in the organizations for drawing out Analytical results and generation of reports, which can be used as the main source for imperative decision-making processes. This model is typically applied to systems that operate with OLAP techniques (Online Analytical Processing).
How does Multidimensional Data Model work?
Like any other system, the Multidimensional Data Model also works based on the predetermined steps, in order to keep the pattern the same throughout the industry and for enabling the reusability of the already designed or created database systems. For creating a Multidimensional Data Model, every project should go all the way through the below phases,
- Congregating the requirements from the client: Similar to the other software applications, a Data Model also requires the precise requirement from the client. Most of the time, the client might not know what could be accomplished with the selected technology. It is the software professional’s duty to provide clarity on to what extent a requirement can be achieved with the selected technology, and elaborately collect the complete requirement.
- Categorizing the various modules of the system: After the process of collecting the entire requirement, the next step is to identify and categorize each of the requirements under the module where they belong. Modularity helps in better management, and also makes it trouble-free to implement, one at a time.
- Spotting the various dimensions based on which the system needs to be designed: Once the separation of various requirements and moving them to the matching modules are completed, the next step is to identify the main factors, from the user’s point of view. These factors can be termed as the dimensions, based on which the multidimensional data model can be created.
- Drafting the real-time dimensions and the corresponding properties: As a part of next step, in the process of the Multi-Dimensional Data Model, the dimensions identified in the previous step can be further used for recognizing the related properties. These properties are termed as the ‘attributes’ in the database systems.
- Discovering the facts from the already listed dimensions and their properties: From the initial requirement gathering, the dimensions can be a mix of dimensions and facts. It is a significant step to distinguish and segregate the facts from the dimensions. These facts play a great role in the structure of the Multi-Dimensional Data Models.
- Constructing the Schema to place the data, with respect to the information gathered from the above steps: Based on the information collected so far, the elaborate requirements, the dimensions, the facts, and their respective attributes, a Schema can be constructed. There are many types of Schemas, from which the most suitable type of schema can be chosen.
Examples to Implement Multidimensional Data Model
The Multi-Dimensional Data Models are, in general, applied on complex systems, whereas the single dimension data models can be used for simple uncomplicated data models. For Instance, One such complex system is the Healthcare system.
As the first step, the requirement here will be to manage the healthcare details of the customers with the help of an online system or a web-based application. After that, identifying the modules is an essential step, and in this case, the Modules will be identified based on the user types and the functional units. Next, the dimensions are listed, that is, the customer, the healthcare providers, the healthcare consultants, etc. Then, the dimensions are filtered more for the facts and attributes. This includes the customer or patient’s personal details as facts like name, age, address, job, etc, and the attributes like the health history, the previous policy details, the coverage, the claim history, etc.
With the information as the foundation, a schema can be constructed by the database professionals. The type of schema can be chosen based on the number of facts and dimensions, and their related attributes. A few of the commonly used schema types are the Star Schema, the Galaxy Schema, and the Snowflake Schema.
Advantages and Disadvantages of Multidimensional Data Model
Below are the advantages and disadvantages:
Advantages
- Multi-Dimensional Data Models are workable on complex systems and applications, unlike the simple one-dimensional database systems.
- The Modularity in this type of Database is an encouragement for projects with lower bandwidth for maintenance staff.
- Overall, organizational capacity and structural definition of the Multi-Dimensional Data Models aids in holding cleaner and reliable data in the database.
- Clearly defined construction of the data placements makes it uncomplicated, in situations like one team constructs the database, another team works on it and some other team works on the maintenance. It serves as a self –learning system if and when required.
- As the system is fresh and free of junk, the efficiency of the data and performance of the database system is found to be advanced & elevated.
Disadvantages
- As the Multi-Dimensional Data Model handles complex systems, these types of databases are typically complex in nature.
- Being a complex system means the contents of the database are huge in the amount as well. This makes the system to be highly risky when there is a security breach.
- When the system caches due to the operations on the Multi-Dimensional Data Model, the performance of the system is affected greatly.
- Though the end product in a Multi-Dimensional Data Model is advantageous, the path to achieving it is intricate most of the time.
Conclusion
The Multi-Dimensional Data Model is a significant improvement amongst various areas of Data Science, like the Data Warehouse system and the Data Management techniques. Multi-Dimensional Models are found to be the competent relational systems, which can serve as a key input for generating Analytical outcomes for the purpose of business decision making processes.
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