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Sample Coursework Paper on MT3500 Managing Information Systems

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Sample Coursework Paper on MT3500 Managing Information Systems

Data warehouses are databases, which hold business information from multiple sources in an enterprise (Stair & Reynolds, 2013). Also known as enterprise data warehouses, they play an integral role in the storage of historical and current data and are used in the creation of analytical reports for knowledge workers in the whole enterprise. Some of the analytical reports created by data warehouses include quarterly and annual comparisons and trends as well as detailed analysis of sales on a daily basis, and for them to store data; the data has to be uploaded from operational systems in the enterprises such as sales and marketing.

Subsets of data warehouses are known as data marts, which are channels through which users get data (Stair & Reynolds, 2013). Data marts are often oriented to specific business lines or teams, and they contain information that is associated to single departments in enterprises. The primary role of data marts in the enterprise is to ensure an improvement of the end-user response, an objective that is achieved when users are allowed to have access to the particular type of data they need. Also, data marts play a role in the improvement of end-user response because they provide data required in a way that provides support to the users’ collective view.

Data mining refers to the automated discovery of patterns as well as relationships in a data warehouse, which is carried out by an information analysis tool that helps users discover, categorize, and then summarize data patterns and relationships from multiple dimensions or angles. In the enterprise, data mining is used when there is a need to identify various groups in a given data, which can then help obtain more accurate or precise prediction results through a decision support system.

Business intelligence (BI) involves the collection of the right or needed data, and this occurs in a timely manner and usable form, after which the data is analyzed to have a positive impact on the strategy, tactics, and operations of the business (Loshin, 2013). The primary objective of BI in the enterprise is to pave the way for easy interpretation of large volumes of data. It should also be noted that enterprises that implement BI often gain stability over the long-term. There are business decisions that are supported by business intelligence, and these include product pricing or positioning, which in the long run, help the enterprise gain a competitive advantage in the market.

Online analytical processing (OLAP) is a part of the broader business intelligence applications, which is fundamental to data discovery, complex analytical calculations, as well as predictive planning. It allows provides users with the opportunity of exploring data from multiple perspectives. Also, it provides top-down and query-data driven analysis in enterprises helping them gain a competitive market advantage (Stair & Reynolds, 2013).

Object-relational database management systems (ORDBMS) are to some extent similar to relational databases, only that they have object-oriented database models and offer support to classes, objects, and inheritance both in the query language and database schemas. Moreover, these systems are similar to pure relational systems because they assist in the extension of the data model while leveraging on custom methods and data-types (Stair & Reynolds, 2013). ORDBMS are also of significance as they provide third parties with the ability to add new data types and operations to the database.




Loshin, D. (2013). Business intelligence: The savvy manager’s guide. Waltham, MA: Morgan Kaufmann.

Stair, R., & Reynolds, G. (2013). Fundamentals of information systems. Cengage Learning.

Sumathi, S., & Sivanandam, S. N. (2006). Introduction to data mining and its applications. Berlin: Springer.


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