MultiDimensional Data Model
Last Updated :
19 Jul, 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
Features of Multi-dimensional data model
- 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 RepresentationWorking on a Multidimensional Data Model
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 12. 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 dataIn 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 2DThis data can be represented in the form of three dimensions conceptually, which is shown in the image below :
3D data representationFeatures of multidimensional data models
- Measures: Measures are numerical values like sales or revenue that can be analyzed. They are stored in fact tables in a multidimensional model.
- Dimensions: Dimensions are descriptive attributes like time, location, or product that give context to measures. They are stored in dimension tables.
- Cubes: Cubes organize data into multiple dimensions, linking measures and dimensions for fast and flexible analysis.
- Aggregation: Aggregation summarizes data (e.g., total sales by month), allowing users to view data at different levels of detail.
- Drill-down: View data in more detail (e.g., from year → month).
- Roll-up: View data in summary (e.g., from day → quarter).
These help explore data across levels. - Hierarchies: Hierarchies arrange dimensions into levels (e.g., Year > Quarter > Month > Day), supporting drill-down and roll-up.
- OLAP (Online Analytical Processing): OLAP tools allow quick analysis of large data sets using cubes, hierarchies, and aggregation for complex queries.
Advantage and Disadvantage of Data Model
Advantage | Disadvantage |
|---|
Easy to handle | Requires skilled professionals |
|---|
Simple to maintain | Complex structure |
|---|
Better performance than relational databases | System performance drops if cache fails |
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More intuitive data representation (multi-viewed) | Dynamic and harder to design |
|---|
Handles complex systems and applications well | Longer path to final output |
|---|
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