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Dimensional Modeling

Extracts from this document...


Dimensional Modeling
(With modeled examples of
Inventory Management and HR Processes)

Submitted To.

Mr. Imran Khan

Course Supervisor

Advance Databases

Submitted By.

Mr.Kamran Ellahi


Mr.Mohd Hanif



Business intelligence is the key achromous in today’s competitive world of business and Data Warehousing the approach for achieving this level of intelligence about your business from your business.  For years, data management people believed that there was only one real, persistent level of data – the operational level. All other data, while accepted, was derivable from this level. This is not true as there are several levels of data within an organization.
The reason stems not from information technology (IT), but from business. Classically, there are three major levels of management and decision making within an organization: operational, tactical and strategic (figure 1). While these levels feed one another, they are essentially distinct. Operational data deals with day-to- day operations. Tactical data deals with medium-term decisions. Strategic data deals with long- term decisions. Decision making changes as one goes from level to level. At the operational level, decisions are structured. This means they are based on rules. (A credit card charge may not exceed the customer's credit limit.) At the tactical level, decisions are semi-structured. (Did we meet our branch quota for new loans this week?) Strategic decisions are unstructured. (Should a bank lower its minimum balances to retain more customers and acquire more new customers?)

 Levels of Analysis (figure 1)

Corresponding to each of these decision levels are three levels of data. These levels of data also are separate and distinct –  again, one feeding the other. Not all strategic data can be derived from operational data. In an organization, there are at least four different kinds of data, including: internally owned, externally acquired, self-reported and modeled. External data

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Dimensional model is inherently dimensional, and it adheres to a discipline that uses the relational model with some important restrictions. Every dimensional model is composed of one table with a multipart key, called the fact table, and a set of smaller tables called dimension tables. Each dimension table has a single-part primary key that corresponds exactly to one of the components of the multipart key in the fact table. This characteristic "star-like" structure is often called a star join.

A dimensional model (figure 4) is a form of analytical design (or physical model) in which data is pre-classified as a fact or dimension. The purpose of a dimensional model is to improve performance by matching the data structure to the queries. People use the data by writing queries such as, "Give this period's total sales volume and revenue by product, business unit and package." Access can be inside-out or outside-in. When access occurs from dimension to fact, it is outside-in. "Give me total sales in volume and revenue for product XYZ in the NE region for the last period, compared to the same period last year" is outside-in. Access can also be inside-out, in which case the query analyzes the facts and then retrieves the appropriate dimensions. For example, "Give me the characteristics of term life policies for which the insured amount is less than $10,000" is inside-out.

image03.pngDimensional Model Example (figure 4)

A particular form of a dimensional model is the star schema, which consists of a central fact table containing measures, surrounded by one perimeter of descriptors, called dimensions. In a star schema, if a dimension is complex or leveled, it is compressed or flattened into a single dimension. For example, if Product consists of Product, Brand and Category, Brand and Category are compressed into Product.

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We can further indulge in the inventory systems by evolving entities from our transactional systems like vendors and brand.


Periodic inventory snap shot (figure 10).

The dimensional model of Periodic inventory snap shot (figure 10) is another perspective for analyzing the inventory system. The model answers queries like the number of products returned on each inventory indent. By this the management can rate there vendors and brand on the basis of consistency in delivery the right quality of product


The classical transaction database is not able to do analytical processing, because:

  1. "Transactional databases contain only raw data, and thus, the processing speed will be considerably slower.
  2. Transactional databases are not designed for queries, reports and analyses…
  3. Transactional databases are inconsistent in the way that they represent information."

In order to surpass the above allegations a new environment was needed to be developed which was named as “Data Warehouse” and for this new environment we needed a representation system which would assist in silent translation from our existing transactional system to the warehouse environment and hence “Dimensional Modeling” came to existence.
Presented in the article are some interesting facts on the traditional transactional system and data warehouse/data mart. Followed by a dimensional implementation of some of the business process of an organization and identifying the issues the new model addressed.
Data warehouses are specially designed to handle different types of queries—queries based on statistical analysis. Until there are better definitions of what and how to process the volumes of available data within a company in a meaningful and reliable way, any company considering implementing a data warehouse or data mart would have to persist with the current techniques to cater there needs.


  • Data warehousing Fundamentals By Paulraj Ponnaih
  • Building a Data-Warehouse(2nd Edition) by Inwon
  • Data warehousing Strategies, Technology and Techniques By Hammergren
  • http://freedatawarehouse.com/tutorials/dmtutorial/
  • http://www.donmeyer.com/
  • http://searchcrm.techtarget.com/
  • http://www.dbmsmag.com/
...read more.

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