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Multidimensional Data Analysis Techniques

The Multidimensional Data Analysis Definition

The terminology of data analysis can sometimes sound as if it comes out of a science fiction movie or mysterious computer world, for example, dyadic data analysis. Single dimensional data analysis might take the number of defeats for a given basketball team over a period of several years. Multidimensional, or numerous levels, takes it further by reviewing data with more than two dimensions (levels). This has special value for forecasting models, as predictions are allowed to be made for numerous timelines, which were created by a number of forecasters, and made in numerous horizons.

statistical data analysis schema

Uses of Multidimensional Data Analysis

The academic definition can still be a little bit confusing, and a real-life example can help. Multidimensional data analysis is used in econometrics. It permits the review of highly sophisticated relationships on a number of levels. An example could be trying to better understand sales activity. Instead of just looking at sales by store, an analyst can look at sales by store (dimension number one), by year (dimension number two), by customer feature (education and age; dimension three). Other dimensions can be added as well to even further define the information. The dimensions can have a hierarchy of members. Customer features can have the already mentioned types, and could also include gender, income or something else. This allows for even more minute detail.

Multidimensional Data Analysis in Data Mining

Computer capabilities for gathering massive amounts of data have improved dramatically over the last few years. The challenge is that all the organization may have is just a very large mountain of meaningless figures. There has to be a way to massage the data to find out from different angles what is being communicated. Multidimensional data analysis can perform the queries necessary to find information deep within the figures. Online Analytical Processing (OLAP) is used in queries that are concerned with the multidimensional analysis in data mining. It is possible with OLAP to investigate the multidimensional data derived from the mining from a number of perspectives.

The techniques employed by OLAP may include the aggregation of information accumulated and computed in numerous dimensions (Consolidation), giving users the chance to concentrate on particular statistics while ignoring others (Drill – Down), or allowing the user to take specific data from a set and review the data from different points of view (Slicing and Dicing).

Multidimensional data analysis is invaluable to a business. Everything has been mentioned so far leads to a very important rationale for using this particular type of data analysis. The modern marketplace has become even more intense competitively. Companies cannot fly blindly into commerce and must try to extract as much information as practicable from data. E-commerce sites are visited by millions if not billions of consumers annually. This always produces an enormous amount of information, but it is useless if it cannot be analyzed. Furthermore, the analysis must come from all types of angles. This is the only way to survive the competition. OLAP will permit will permit the inquiries needed. Decisions such as geographical placement of product, opening up new branches, or even closing nonproductive units can be done more accurately. Suffice it to say this is more than just an esoteric exercise. Considerable amounts of cash and possibility of profit result from effective multidimensional data analysis.

Learn about using multidimensional analysis in data mining today!