Business Intelligence and Data Warehouses
Businesses today are extremely reliant on large amounts of data for making intelligent business decisions. Likewise, the data warehouses are often structured in a manner that optimizes processing large amounts of data.
Write a two to three (2-3) page paper in which you:
- Outline the main differences between the structure of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data.
- Outline the main differences between database requirements for operational data and for decision support data.
- Describe three (3) examples in which databases could be used to support decision making in a large organizational environment.
- Describe three (3) examples in which data warehouses and data mining could be used to support data processing and trend analysis in large organizational environment.
The specific course learning outcomes associated with this assignment are:
- Describe the role of databases and database management systems in managing organizational data and information.
- Distinguish the role of databases and database management systems in the context of enterprise systems.
- Use technology and information resources to research issues in database systems.
- Write clearly and concisely about relational database management systems using proper writing mechanics and technical style conventions.
Business Intelligence and Data Warehouses Sample Paper
Databases and data warehouses are examples of technological advancements in the contemporary world that allow businesses to manage large volumes of data. According to Conn (2005), large amounts of data collected by businesses are used for intelligent decision making. Even though both databases and warehouses offer frameworks of large data management for organizations, they have got a number of structural differences (Conn, 2005).
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A relational database optimized for online transactions and a data warehouse optimized for processing and summarizing large amounts of data are structurally different. First, databases optimized for online transactions are highly normalized with many tables. On the other hand, a data warehouse optimized for processing and summarizing large amounts of data is typically de-normalized, consist of fewer tables, with frequent use of star and snowflake schemas (Conn, 2005). Second, databases optimized for online transactions consist of short and fast updates and inserts initiated by end users. Conversely, a data warehouse optimized for processing and summarizing large amounts of data consist of periodic long-running batch job that refresh the data (Conn, 2005). Third, in a database optimized for online transactions reveals a snapshot of ongoing business processes. On the contrary, a data warehouse optimized for processing and summarizing large amounts of data reveals multidimensional views of different kinds of business activities (Conn, 2005).
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There are some differences between database requirements for operational data and for decision support data (Cippico, 1997). First, operational data need a database that can allow transactions to happen in real time, while decision support data require a database that allow transactions to happen at a given point in time (Cippico, 1997).
Second, a database for operational data contains features for update transactions while a database for decision support data mainly contains features that permit query or read-only transactions (Cippico, 1997). Third, databases for operational data contain many tables that store data that only represents information about a given transaction. On the other hand, databases for decision support data do not include the details of every operational transaction. Rather, databases for decision support data contain aggregated and integrated tables that allow summarization of data for decision making purposes. The degree to which decision support data are summarized in decision support database is different from that in operational databases (Cippico, 1997).
Databases can be used to support decision making in a large organizational environment. With the emergence of Internet-hosted databases and query tools that are user-friendly, large organizations are now turning to decision support databases to analyze their data in order to obtain useful information for decision making (Phillips-Wren, Hahn and Forgionne, 2004). Decision support databases contain analytical and report-writing features that enable users to translate raw data into information that can be used for decision making. For example, large organizations can compare information in decision support databases with that of enterprise resource planning to accelerate operations and enhance productivity. This comparison allows large organizations to identify which operations are to be performed first for improvement of productivity (Phillips-Wren, Hahn and Forgionne, 2004).
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Decision support databases also enable large organizations to attack complex problems by reducing them to simpler problems to allow comparisons between various combinations of criteria and options. This enables organizations to choose between pairs of options, which allows for quick decision making. Since decision support databases allow mangers to make comparisons between options, they can be used by large organizations to make consistent decisions especially when there are large sets of options to be considered (Phillips-Wren, Hahn and Forgionne, 2004).
Databases encourage quick collaboration in large organizations with several decision makers. Decision support databases provide several users with access to the organization’s data. This feature provides multiple users with an opportunity to clarify the decision-making process to enhance accuracy and consistency among various leaders of large organizations (Phillips-Wren, Hahn and Forgionne, 2004). When large organizations are required to respond to quick strategic decisions, they normally consider a wide range of alternatives. A database therefore serves as a very good tool that can assist managers of large organization to make decisions within a short period of time (Phillips-Wren, Hahn and Forgionne, 2004).
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Data warehousing refers to a depository for business databases that provide a clear picture of historical and current operations of an organization (Joseph, 2013). Data mining can be defined as a set of methods used by organizations to analyze data, developed with the objective of finding out specific rules, relations and dependence related to large volumes of data, and transforming them to high quality data (Joseph, 2013). Data warehouses and data mining can be used to support data processing and trend analysis in large organizational environment. For example, organizations use data warehouses and data mining to compile, analyze and forecast business performance, for bond and stock assessment (Joseph, 2013). In addition, businesses can use data warehouses and data mining to identify prospects that should be included in mailing list in order to obtain highest response time. Again, manufacturing organizations use data warehouses and data mining to improve quality control and maintenance (Joseph, 2013).
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