A multidimensional data model is a more sophisticated method to arrange data in enterprise databases, which offers a better organization and structure for the databases’ content. Unlike the one-dimensional systems like the lists, multidimensional models may have two-dimensional or three-dimensional items in the database system. It is ideally used in organizations to fetch analytical results from data and prepare insightful reports. In the competitive market, it can offer an imperative advantage in terms of business decision-making. This data analytics model is applied to the systems which operate in an OLAP or Online Analytical Processing model.
Working on the multidimensional data model
Like other data management systems, a multidimensional data model also functions based on the defined steps to keep the patterns across the industries. This approach enables the reusability of the already created DB systems too. To create a feasible multidimensional data model, the projects must go all the way through various steps as below:
Gathering all the requirements: Like the various other software applications for data management, the multidimensional model requires a very precise requirement from clients. Many times, the client may not know what can be accomplished using the given technology. It is the IT project administrator’s responsibility to share the exact requirement and the goal to be achieved with the given technology and elaborately collect the full requirement.
Categorize the different modules in the system: After collecting all the requirements, the subsequent steps will unveil and categorize each requirement to identify where they belong to. Ensuring modularity will help better plan and manage the project, making it much easier to implement and run.
Understanding different dimensions to design the system: Once various requirements are separated and matching them with the corresponding modules, the next step is to identify the major factors from a user perspective. These factors are known as dimensions, based on which a multidimensional model is created.
Drafting actual dimensions of the model and defining its properties: At the next step of the multidimensional data model, different dimensions drawn out in the previous step are further elaborated to recognize the related properties. These properties are collectively called the attributes of the database system.
Discovering related facts from the dimensions and properties: From the requirement gathering stage itself, the dimensions may remain as a mix of various facts and dimensions. So, the next step is significant as to segregate various facts from different dimensions. These facts play a critical role in the data structure of a multi-dimensional data model.
Construct a schema for data placement: This step is administered based on the above steps’ information. Based on the given information gathered so far, the dimensions, facts, and elaborated requirements along with their related attributes, one may create a related schema. There are various types of schemas from which you must choose the most suitable schema for your purpose.
Use cases of multidimensional data model implementation
Usually, multidimensional data models are applied to more complex systems, and the single-dimension models are used for uncomplicated data models. For example, Healthcare is one of the complex systems where the multidimensional model is ideal to consider.
At the first step, you need to manage all the patients’ healthcare details and users to run a web-based application. Once this is set up, then next, the modules need to be identified, and in the case of healthcare, the modules needed to be devised based on the functioning of the various units related to healthcare. Next, various dimensions needed to be listed based on the customers and the healthcare providers, consultants, etc. These dimensions also need to be filtered based on various attributes and facts. This will also include the patients’ details like the name, address, job, health history, etc.
With all the confidential information as the foundation of the application, a schema must be constructed first. The schema type can be further chosen based on various dimensions and facts and their related attributes. Some of the most commonly used schemas are Galaxy Schema, Star Schema, Snowflake Schema, etc.
Multidimensional model advantages and disadvantages
Further, let us evaluate some of the major advantages and disadvantages of the multidimensional model.
Advantages
- The multidimensional models can work on complex applications and systems, unlike the single-dimension database systems.
- The modularity of multidimensional models can encourage the products with lower bandwidth for the maintenance staff.
- The multi-dimensional models’ structural definition and organizational capacity will aid in holding much reliable and cleaner data in databases.
- Well-defined data construction and placements make it much uncomplicated in different scenarios, like one team for the construction of the DB and another team working on maintaining the same. It overall serves as a self-propelling system as required.
- As the multidimensional model is free of any junk, the efficiency of data and database systems’ performance is much elevated and advanced.
Disadvantages
- Multidimensional models are meant to handle more complex systems, so these databases are typically much complex in nature.
- Being so complex means the contents of multidimensional databases are huge in terms of volume as well. This also makes the systems highly risky when there is any breach of security.
- When a system caches due to its operations on the multidimensional data model, then such systems’ performance is also affected greatly.
- Even though the end product of multidimensional data models is very advantageous, the path to achieving the same is so intricate.
Overall, the multidimensional data model has undergone a significant level of improvement amongst the growth of various advanced data science sectors like data warehousing and data management techniques, etc. Nowadays, multidimensional data models are competent enough to deal with relational database systems and non-relational database systems. It also serves as a key input to generate analytical outcomes for timely and informed business decision-making.
