History & BackgroundWorks CitedBusiness IntelligenceRyan Feigelson Angela KoslowskiTony DevineMay 26, 2006BA 471AbstractBusiness Intelligence is the exploration and mining of data in search of meaningful patterns. Its uses span from business to government to other organizations in need of a way to compile a large amount of data, discern patterns from them, and use it for strategic purposes. A thorough understanding of how business intelligence works can assist business students, as well as professionals, in using information technology to plan and execute various tactical and strategic decisions. IntroductionIn this report, we cover several aspects of business intelligence. First we discuss a comprehensive summary of the history and background, including definitions of important terms associated with business intelligence. In addition, BI will be examined through the 1990’s and after the events of September 11th. Then, modern uses and business applications of BI will be presented to more thoroughly place BI in a business context. We discuss companies that provide this service, and how they design and implement a BI program. Then some of the flaws of business intelligence will be introduced. Lastly, an explanation of smarter uses of business intelligence will conclude the report. 2History & BackgroundBusiness intelligence is the exploration and mining of data in search of meaningful patterns. In other words, BI comprises a set of business processes for collecting and analyzing business information. Included in business intelligence are the technology used in these processes and the information obtained from them. These include software programs gathering and analyzing large quantities of data. Before discussing the business side of business intelligence, it is important to understand the history of this concept.An early reference to non-business intelligence occurs in Sun Tzu’s The Art of War (wikipedia.org). He asserts that in order to succeed in war, one should have a full understanding of one’s own strengths and weaknesses and full knowledge of one’s enemy’s strengths and weaknesses. This is directly applicable to business intelligence, in that collecting data, discerning patterns and meaning from that data, and responding to the resultant information are all parts of competition—whether that be on the battlefield or in the marketplace. Before today’s information age, businesses struggled to gather data from non-automated sources and often made strategic decisions based on intuition. As more systems became automated in the 1990’s, more data became available. However, the 3structure of businesses soon became a problem. Due to incompatibilities between systemsand platforms, firms were unable to merge data from various sources in order to make them meaningful. Even though they had a way to gather large assortments of data, they were still using different systems to gather different kinds of data, and therefore these data could not be merged. In addition, there was no way to make use of these data due to the fact that patterns could not yet be discerned. Corporate level executives were wasting their time sifting through these massive amounts of data, and instead of making company-wide strategic decisions; they were concerning themselves with day-to-day operations. Thus, a need for short- and long-term data developed for corporate-level decision makers to use on a larger scale. In addition to the desire of businesses to gather information and discern patterns from it, the United States government soon became interested in this idea as well. The events of September 11th magnified security concerns. The need to know its enemy (terrorist groups, in this case) became prevalent for the government. These terrorist attacks, coupled with the demands of business leaders, encouraged developers to create efficient systems that worked for many industries and organizations. Data warehouses and data marts were highlighted as solutions to the problem of multi-system incompatibility, even though they had been introduced long before 9/11. A data warehouse is a repository of integrated information, available for queries and analysis. It is a place where relevant data can be held for completing strategic reports for management. Since most executives are less concerned with day to day operations, 4and more with overall business functions, data warehouses allow them access to vital information to make strategic company-wide decisions. Data warehouses are a solution tothe multi-system incompatibility problem, because they take data form a variety of platforms and place them in a common location. This makes access to relevant data a lot more convenient, while being less likely to affect day to day operations. It should be emphasized that data warehouses are not a place to store all data, but they are a place to store data that are interesting. This is due to the fact that BI software discerns patterns from raw data related to the entire enterprise, and places relevant information in one place. One common example of a data warehouse is the data collected from supermarket reward cards. In a data warehouse, information about the buying habits of all of a store’s customers could be stored. A data mart is a specialized subset of a data warehouse. The creation of data for a data mart is predicated on a specific, defined need for a certain grouping of select data. Data marts are smaller than data warehouses, and focus on a particular subject or department. The data contained in data marts is also in the data warehouse, but data martsfulfill a certain need of a firm. Similar to the previous example, data marts can be used to track trends in sales at a supermarket. A more defined need, such as the buying habits of males ages 18-24 on Fridays, can be fulfilled using this tool. Managers can, in turn, use this information to display products that are purchased at the same time in similar places. Reward cards will be discussed in further detail later in this report.5Designing and Implementing a Business Intelligence Program When businesses look to create a business intelligence program there are a vast amount of queries they use in order to assure their program is going to deliver the correct results (business intelligence). Query One: Goal Alignment queriesThe first step in this query is to assess the short term and medium term reason for this program. For example; “What strategic goals does
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