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Data Warehouse and Business Intelligence
In order to write a paper on the similarities and contrast between data warehouse and business intelligence, we need to first define each term before finding the similarities and contrasts between the two.
Data warehouse are used for storing data for archival, analysis, and security purposes. The warehouses themselves are made up of one or many computers (i.e. servers) that are connected together into one giant computer (network system).
The data warehouse is more than a 'storing house' or archival system. It also serves as a place for categorizing and 'tidying' the data, thereby making it easy and convenient for the user to get hold of in his day-to-day business. This is essential for the business for in order to make effective decisions it needs to be able to access the right data in a speedy manner. Doing so helps the organization in its decision support system (DSS) which refers to the facts, trends, or other patterns that are needed for the organization to make relevant decisions. In fact, the entire data warehouse discipline was created in the late 1980s (by two IBM researchers Barry Devlin and Paul Murphy) due to the fact that organizations were hunkering under the redundancy of information that they were accumulating and burying their heads in as well as the consequent challenge of forming effective decisions. The problem was exaggerated for large organizations or for those that were linked to others. Many times, businesses lost huge amounts of money in their efforts to accumulate and retrieve the necessary information. The process of gathering, sorting through, revamping, and integrating data was time-consuming and costly, aside from which the data had to be constantly checked, corrected and modified (Inmon, 2005). Data warehouses came about as a result.
Data warehouses come in four different categories. These are:
1. Offline Operational Data Warehouses where data from an online system is copied and pasted into an offline system to be used at the user's convenience.
2. Offline Data Warehouses which is also an offline system that stores data for users to share. Data on this system is updated frequently on a daily, weekly, or monthly basis.
3. Real Time Data Warehouse is a virtual base where data is updated continuously as each new influx of data is fed into it.
4. Integrated Data Warehouses are data warehouses that are integrated with other systems thereby enabling users to access various systems simultaneously. The result is that various systems can pool together in order to process reports and explore various data. (Tech-FAQ Data Warehouse).
Data warehouses have both their advantages and disadvantages. The advantages include the fact that employees and shareholders of a certain organization can have equal access to the same pool of data accessing it for reports, analysis, and decision making. By storing data in one common place, data warehouses also make it easier to conduct development and research for the organization and help the users be on the same page regarding key business concerns.
A further advantage is that pooling the data in one place helps users better regulate and clean the data in order to maintain its consistency and currency. The fact that most data warehouses are integrated also enable users to accumulate diverse pieces of data (such as regards human resources, finance, IT, accounting, etc.) and tie them together towards the needed objective.
On the other hand, data warehouses are difficult to set up and run. These activities are time consuming, and the data stored in the warehouse needs to be continuously checked and assessed making this an expensive and supplementary tasks.
Systems are also always modified and introduced making current systems possibly incompatible with the data which also requires that users need to update the system alongside with the necessary training. This adds to expenditure of time and expense.
Lastly, security is often a concern particularly with data that is relayed over an open system such as the Internet (Tech-FAQ Data Warehouse). Hackers and viruses proliferate and data is vulnerable to attack and exposure.
Business Intelligence (BI) first developed in the 1960s and evolved through the 1980s stemming form the from the decision support systems (DSS) that were really computer-aided models used for decision-making and planning. Howard Dresner coined the term in 1989 to refer to "concepts and methods to improve business decision making by using fact-based support systems." (Power 2007)
Business Intelligence (BI) has become the umbrella term used for the variety of software applications…[continue]
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