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BUS 237 (192)
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Ch 8 notes.docx

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Department
Business Administration
Course
BUS 237
Professor
Maryam Ficociello
Semester
Summer

Description
Chapter 8: Decision Making and Business Intelligence Q1. What the challenges Managers face in making decisions? Information Overload: An overabundance of irrelevant data Information Overload Perabyte: 10 to the power 15 bytes Exabytes:10 to the power 18 bytes Data Quality - The final challenge in decision making is the quality of data - Although data that are critical for successful operations must be complete and accurate, data that are only marginally necessary do not need to be—for example, some systems gather demographic data in the ordering process. But because such data are not needed to fill, ship and bill orders, their quality suffers - Problems of using operational Data for BI systems:  Dirty Data,  Wrong granularity –too  Missing values fine or not enough  Inconsistent data  Too much data –too many  Data not integrated attributes, too many data points Dirty Data: Problematic Data o An example would be values of B for customer gender and of 213 for customer age, a value of 999-999-9999 for a phone number, a part color of gren, and an email address of [email protected] – All of these values can be problematic for data-mining purposes o Inconsistent Data—for example, when an area code changes, the phone number for a given customer before the change will not match the customer’s number after the change Granularity: The level of detail in data. Customer name and account balance is large granularity data. Customer name, balance and the order details and payment history of every customer order is smaller granularity - Refers to the degree of summarization or detail - Coarse data are highly summarized; fine data express precise detail Clickstream Data: E-commerce data that describe a customer’s clicking behaviour. Such data include everything the customer does at the website - Clickstream data is fine; includes everything the customer does at the website Chapter 8: Decision Making and Business Intelligence - Data is better to be fine rather than too course—if data is too fine, data can be made coarser by summing and combining; however, if data is too course, there is no way to separate the data into constituent parts Q2. What is OLTP and How does it support decision making? - Using computers to capture information electronically is often referred to as being “online” Online transaction Processing(OLTP): Collecting data electronically and processing transactions online - 2 basic ways transactions can be processed: (1) If transactions are entered and processed immediately upon entry, the system is operating in “real time” because there is little/no delay in updating the systems with new data (2) Wait for many transactions to pile up before you process them—an example would be, at a gas station, you might collect all the transactions that occurred during the day and then send them at the end of the day to the central office for processing—this is an example of “batch” processing - The choice between these two transaction processes depends of the nature of the transaction, cost of the system and the needs of the organization - Real time is more complex and costs more to implement but provides the most up to date information - OLTP systems are the backbones of all functional, cross functional and interorganizational systems in a company—they support decision making by providing the raw info about transactions and status for an organization Q3. What are OLAP and the Data resource challenge? - Competitive advantage can be realized when organizations use the data they have collected to help make better decisions Data resource challenge: Occurs when data are collected in OLTP but are not used to improve decision making - Quickest way to explain the data resource challenge is to consider whether a company views its data as an asset (a source from which future economic benefits may be obtained) - Data is likely to be thought of as an asset but is not really treated as an important resource Decision Support Systems (DSS): Systems that focus on making data collected in OLTP useful foro decision making - Systems that focs on making OLTP collected data useful for decision making Chapter 8: Decision Making and Business Intelligence Online Analytic Processing(OLAP) systems: A dynamic type of reporting system that provides the ability to sum, count, average, and perform other simple arithmetic operations on groups of data. Such reports are dynamic because users can change the format of the reports while viewing them - The format of an OLAP is dynamic—the viewer of the report can change the report’s structure - An OLAP report has measures(e.g. total sales avg sales, and avg cost), or facts, and dimensions (purchase date, customer type, customer location, and sales region) - OLAP cube and OLAP report are the same thing Drill down: With an OLAP report, to further divide the data into more detail Q4.What are BI systems and How do they provide competitive advantage? Business intelligence (BI) systems: A system that provides the right information, to the right user, at the right time. A tool produces the information but the system ensures that the right information is delivered to the right user at the right time - Characteristics and competitive advantage of BI systems page 251 Reporting Systems: A system that creates information from disparate data sources and delivers that information to the proper users on a timely basis Data-mining Systems: IS that processes data using sophisticated statistical techniques like regression analysis and decision –tree analysis to find patterns
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