DATA WAREHOUSING

To what degree is it possible or desirable to separate the technical issues involved in creating and managing data warehousing from the political issues (i.e., the distribution of costs and benefits to different components)?

To what degree is it possible or desirable to separate the technical issues involved in creating and managing data warehousing from the political issues (i.e., the distribution of costs and benefits to different components)?

Introduction

The data warehouse as one of the most important management tools and business development is a subject-oriented, integrated, time-dependent data set. The data warehouse is aimed not only to automate business processes, but also on the content analysis of information and is intended to support decision making, and its users - it's upper and middle management organizations, analysts, representatives of departments of financial analysis, marketing and other departments.

Integrity of data means that, for example, customer data, divisions, products and services from different sources are stored centrally and consistently. In this complete information about the customer may include data received from both the basic and transactional information (accounting, banking or shopping) systems as well as from the front-office or any other application (Kelly, 1997). In this connection, this study is going to discuss the statement that to what degree is it possible or desirable to separate the technical issues involved in creating and managing data warehousing from the political issues that is the distribution of costs and benefits of various components.

Types of Data Warehouse

The two main types of data involved with data warehousing are operational and informational data. Operational data, which consists of the data businesses use on a daily basis, is normally stored in a relational database and retrieved and updated by an OLTP system. Once operational data has been manipulated and summarized, it is considered informational data; this is what makes up a data warehouse. In the process of data warehousing, informational data is created from operational data via transformation or propagation tools. This process is a standardization of sorts that helps ensure that information can be retrieved quickly and easily at a later date (Simon & Hammergren, 2009). Multi-dimensional analysis, or OLAP, is the desired result of data warehousing. It allows users to analyze large amounts of data related to sales, products, customer service, and other business operations.

The informational data stored in a data warehouse is also known as metadata, which comprises both technical data and business data. Technical data, which provides information about the data warehouse itself, is used mainly by system administrators. It is the business data that users seek most often for tasks such as forecasting sales or predicting trends. To perform such analyses, the data warehouse program uses applications known as data mining tools to interpret data and find patterns within the information. For example, a retail company, either online or traditional, might use Data warehousing and data mining technology to pinpoint customer purchasing patterns and to gather additional information about its buyers (Grant, 2003).

Importance of Data Warehousing

The discussion that can highlight the importance of the data ...