MultiDimensional Data Model
Last Updated :
03 Apr, 2025
A Multidimensional Data Model is defined as a model that allows data to be organized and viewed in multiple dimensions, such as product, time and location
- It allows users to ask analytical questions associated with multiple dimensions which help us know market or business trends.
- OLAP (online analytical processing) and data warehousing uses multi dimensional databases.
- It represents data in the form of data cubes. Data cubes allow to model and view the data from many dimensions and perspectives.
- It is defined by dimensions and facts and is represented by a fact table. Facts are numerical measures and fact tables contain measures of the related dimensional tables or names of the facts.
Multidimensional Data Representation
Working on a Multidimensional Data Model
On the basis of the pre-decided steps, the Multidimensional Data Model works.
The following stages should be followed by every project for building a Multi Dimensional Data Model :
Stage 1 : Assembling data from the client : In first stage, a Multi Dimensional Data Model collects correct data from the client. Mostly, software professionals provide simplicity to the client about the range of data which can be gained with the selected technology and collect the complete data in detail.
Stage 2 : Grouping different segments of the system : In the second stage, the Multi Dimensional Data Model recognizes and classifies all the data to the respective section they belong to and also builds it problem-free to apply step by step.
Stage 3 : Noticing the different proportions : In the third stage, it is the basis on which the design of the system is based. In this stage, the main factors are recognized according to the user’s point of view. These factors are also known as “Dimensions”.
Stage 4 : Preparing the actual-time factors and their respective qualities : In the fourth stage, the factors which are recognized in the previous step are used further for identifying the related qualities. These qualities are also known as “attributes” in the database.
Stage 5 : Finding the actuality of factors which are listed previously and their qualities : In the fifth stage, A Multi Dimensional Data Model separates and differentiates the actuality from the factors which are collected by it. These actually play a significant role in the arrangement of a Multi Dimensional Data Model.
Stage 6 : Building the Schema to place the data, with respect to the information collected from the steps above : In the sixth stage, on the basis of the data which was collected previously, a Schema is built.
Example to Understand Multidimensional Data Model
1. Let us take the example of a firm. The revenue cost of a firm can be recognized on the basis of different factors such as geographical location of firm’s workplace, products of the firm, advertisements done, time utilized to flourish a product, etc.
Example 1
2. Let us take the example of the data of a factory which sells products per quarter in Bangalore. The data is represented in the table given below :
2D factory data
In the above given presentation, the factory’s sales for Bangalore are, for the time dimension, which is organized into quarters and the dimension of items, which is sorted according to the kind of item which is sold. The facts here are represented in rupees (in thousands).
Now, if we desire to view the data of the sales in a three-dimensional table, then it is represented in the diagram given below. Here the data of the sales is represented as a two dimensional table. Let us consider the data according to item, time and location (like Kolkata, Delhi, Mumbai). Here is the table :
3D data representation as 2D
This data can be represented in the form of three dimensions conceptually, which is shown in the image below :
3D data representation
Features of multidimensional data models
- Measures: Measures are numerical data that can be analyzed and compared, such as sales or revenue. They are typically stored in fact tables in a multidimensional data model.
- Dimensions: Dimensions are attributes that describe the measures, such as time, location, or product. They are typically stored in dimension tables in a multidimensional data model.
- Cubes: Cubes are structures that represent the multidimensional relationships between measures and dimensions in a data model. They provide a fast and efficient way to retrieve and analyze data.
- Aggregation: Aggregation is the process of summarizing data across dimensions and levels of detail. This is a key feature of multidimensional data models, as it enables users to quickly analyze data at different levels of granularity.
- Drill-down and roll-up: Drill-down is the process of moving from a higher-level summary of data to a lower level of detail, while roll-up is the opposite process of moving from a lower-level detail to a higher-level summary. These features enable users to explore data in greater detail and gain insights into the underlying patterns.
- Hierarchies: Hierarchies are a way of organizing dimensions into levels of detail. For example, a time dimension might be organized into years, quarters, months, and days. Hierarchies provide a way to navigate the data and perform drill-down and roll-up operations.
- OLAP (Online Analytical Processing): OLAP is a type of multidimensional data model that supports fast and efficient querying of large datasets. OLAP systems are designed to handle complex queries and provide fast response times.
Advantages of Multi Dimensional Data Model
The following are the advantages of a multi-dimensional data model :
- A multi-dimensional data model is easy to handle.
- It is easy to maintain.
- Its performance is better than that of normal databases (e.g. relational databases).
- The representation of data is better than traditional databases. That is because the multi-dimensional databases are multi-viewed and carry different types of factors.
- It is workable on complex systems and applications, contrary to the simple one-dimensional database systems.
- The compatibility in this type of database is an upliftment for projects having lower bandwidth for maintenance staff.
Disadvantages of Multi Dimensional Data Model
The following are the disadvantages of a Multi Dimensional Data Model :
- The multi-dimensional Data Model is slightly complicated in nature and it requires professionals to recognize and examine the data in the database.
- During the work of a Multi-Dimensional Data Model, when the system caches, there is a great effect on the working of the system.
- It is complicated in nature due to which the databases are generally dynamic in design.
- The path to achieving the end product is complicated most of the time.
- As the Multi Dimensional Data Model has complicated systems, databases have a large number of databases due to which the system is very insecure when there is a security break.
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