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Chapter8 review.docx

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Department
Business Administration
Course
BUS 237
Professor
Kamal Masri
Semester
Winter

Description
Chapter 8 Decision Making and Business Intelligence Q1: What are the challenges managers face in making decisions? Early in the main frame era, Russell Ackoff wrote an article called “Management Misinformation Systems.” It suggested that several erroneous assumptions were being made about information systems and described how these assumptions affected managerial decision making. 1. Managers will have no problem making decisions if they get the data they need. 2. Poor decisions are made because managers lack relevant information. 3. Managers are aware of the data they need.  Information overload ----- A total of 403 petabytes of new data were created in 2002. ----- The study also found that data were growing at the rate of 30 percent a year. At that rate of growth, by 2010, nearly 3300 petabytes, or 3.3 exabytes, of data will have been generated. The challenge for managers in a world overloaded with information is to find the appropriate data and incorporate them into their decision processes. Information systems can both help and hinder this process.  Data Quality A final challenge in decision making is the quality of the data. Raw operational data are seldom suitable for more sophisticated reporting or data mining. Major problem categories: A. Problematic data are termed dirty data. Ex: values of B for customer gender. B. Missing values. Ex: a non-profit organization can process a donation without knowing the donor’s gender or age, but a data-mining application will suffer if many such values are missing. C. Inconsistent data. Before data can be used, they must be recoded for consistency over the period of the study. D. Data can also be too fine or too coarse. Data granularity refers to the degree of summarization or detail. Coarse data are highly summarized; fine data express precise details. ----- Generally, it is better to have too fine a granularity than too coarse. If the granularity is too fine, the data can be made coarser by summing and combining. Conclusion: This section has suggested that a number of factors, including complexity, uncertainty, information overload, and data quality, make management decision making challenging. Information has the potential to meet some of these challenges. Q2: What is OLTP and how does it support decision making? Recall chater7: functional information systems, like general ledger systems, human resource systems, and operational systems, are used to capture details about business transactions and then create updated information by processing these transaction details. Therefore, information systems are a critical component for capturing and processing the details about these transactions because they are very efficient and accurate. Chapter8 If you are collecting data electronically and processing the transactions online, then you are using an online transaction processing (OLTP) system. There are two basic ways that transactions can be processed. 1. If transactions are entered and processed immediately upon entry, then the system is operating in “real time,” because there is little or no delay in updating the systems with new data. 2. Wait for many transactions to pile up before you process them. An example of “batch” processing. The choice of whether to use real-time or batch processing depends on the nature of the transactions, the cost of the system, and the needs of the organization. Real-time systems tend to be more complex and cost more to implement. Conclusion: OLTP systems are the backbone of all functional, cross-functional, and inter-organizational systems in a company. They are designed to efficiently enter, process, and store data. OLTPs support decision making by providing the raw information about transactions and status for an organization. Q3: What are OLAP and the data resource challenge? Data alone is not enough. It does not create value if it is not used. It is important to realize that the competitive advantage of information is realized when organizations use the data they have collected to help make better decisions. Data resource challenge: while data may be collected in OLTP, the data may not be used to improve decision making. ----- The quickest way to explain the data resource challenge is to consider whether a company views its data as an asset. Systems that focus on making OLTP-collected data useful for decision making are often referred to as decision support systems (DSS), or more generally as online analytic processing (OLAP) systems. OLAP provides the ability to sum, count, average, and perform other simple arithmetic operations on groups of data. An OLAP report has measures, or facts, and dimensions. A measure is the data item of interest. For example: it’s the item that is to be summed or averaged or otherwise processed in the OLAP report. Total sales, average sales, and average cost are examples of measures. A dimension is a characteristic of a measure. For example: purchase data, customer type, customer location, and sales region. The presentation of a measure with associated dimensions, often called an OLAP cube, or simply a cube. An OLAP cube and an OLAP report are the same thing. ----- The distinguish characteristic of an OLAP report is that the user can alter the format of the report. ----- With an OLAP report, it is possible to “drill down” into the data. This term means to further divide the data into more detail. ----- Both displays in page 248,249 are valid and useful, depending on the user’s perspective. ----- All this flexibility comes at a cost. ----- Special-purpose products called OLAP servers have been developed to perform OLAP analysis. OLAP tools have become the primary tools used in the area of business intelligence (BI). Q4: What are BI systems and how do they provide competitive advantage? A business intelligence (BI) system is a system that provides information for improving decision making. ----- BI systems vary in their characteristics and capabilities, and in the way they foster competitive advantage. 1. Reporting systems: integrate data
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