notes

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Data Warehouse

Creating data to be analytical requires that it be subject-oriented, integrated, time-referenced, and non-volatile.

Subject-Oriented Data

In a data warehouse environment, information used for analysis is organized around subjectsemployees, accounts, sales, products, and so on. This subject specific design helps in reducing the query response time by searching through very few records to get an answer to the user’s question.

Integrated Data

Integrated data refers to de-duplicating information and merging it from many sources into one consistent location. When short listing your top 20 customers, you must know that “HAL” and “Hindustan Aeronautics Limited” are one and the same. There must be just one customer number for any form of HAL or Hindustan Aeronautics Limited, in your database.

Time-Referenced Data

The most important and most scrutinized characteristic of the analytical data is its prior state of being. In order words, time- referenced data essentially refers to its time-valued characteristic. For example, the user may ask “What were the total sales of product ‘A’ for the past three years on New Year’s Day across region ‘Y’?” To answer this question, you need to know the sales figures of the product on New Year’s Day in all the branches for that particular region.

Non-Volatile Data

Since the information in a data warehouse is heavily queried against time, it is extremely important to preserve it pertaining to each and every business event of the company. The non-volatility of data, characteristic of data warehouse, enables users to dig deep into history and arrive at specific business decisions based on facts.