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BUS237-Final_Chp8 Summary.pdf

6 Pages

Business Administration
Course Code
BUS 237
Kamal Masri

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Chapter 8 Summary Decision Making and Business Intelligence Q1: What are the challenges managers face in making decisions? • Decision making is a daily occurrence • IS only a piece of the decision making puzzle • Russell Ackoff wrote “Management Misinformation Systems”, and he assumed: • Managers will have no problem making decisions if they get the data they need o For most managers, too many possibilities exist -> decisions = complex, uncertain • Poor decisions made b/c managers lack relevant information o Suffer more from information overload • Managers are aware of the data they need o Reality -> most managers unsure, and so ask for more data than needed Information Overload Petabytes – 10 15bytes 18 Exabytes – 10 bytes • Data is growing at a rate of 30% per year • Information found and made available to the right people at the right time can improve decision • Manager’s goal: to find appropriate data and incorporate it into decision processes Data Quality • Data from operational systems can be processed to create basic reports with few issues • However, raw operational data seldom suitable for sophisticated reporting or data mining • Data is critical for successful operations must be complete and accurate, but data that is only marginally necessary don’t need to be • Major problem categories include: • Dirty data • Missing values • Inconsistent data • Data not integrated • Wrong granularity o Too fine o Not fine enough • Too much data o Too many attributes o Too many data points Dirty data – problematic data • Data-mining applications suffer if many values are missing • Inconsistent data common in data that has gathered over time (i.e. area code, postal codes, etc.) Data granularity – refers to the degree of summarization or detail Coarse data – highly summarized Fine data – express precise details Clickstream data – Very fine data (i.e. capturing customer’s clicking behaviour on websites) • Generally, better to have too fine a granularity than too coarse b/c data can be always made coarser • If granularity is too coarse, then there’s no way to separate data into constituent parts Q2: What is OLTP and how does it support decision making? • Functional information systems are used to capture details about business transactions and then create updated info by processing transaction details • Using computers to capture info electronically is often referred to as being “online” • Online transaction processing (OLTP) system – used when you collect data electronically and process transactions online • Two basic ways transactions can be processed: 1. Real-time processing o Entered and processed immediately after entry o Tend to be more complex o Cost more to implement o Provide most up-to-date info 2. Batch processing o Wait for many transactions to pile up before processing them • Choice to use real-time or batch processing depends on  Nature of transactions  Cost of the system  Needs of the organizations  OLTP systems are backbone of all functional, cross-functional, and interorganizational systems in a company  Designed to efficiently enter, process, and store data  Combines large databases wit input devices (i.e. grocery store scanners)  OLTP support decision making by providing raw info about transactions and status for an organization Q3: What are OLAP and the data resource challenge? • Information is a competitive weapon (source of competitive advantage for an organization) • Info does not create value if it’s not used • Although we think of data as an asset, we’re not treating them as an important resource Data resource challenge – Occurs when data are collected In OLTP but are not used to improve decision making Asset – defined as a resource from which future economic benefits may be obtained Decision support systems (DSS) – Systems that focus on making OLTP collected data useful for decision making Online analytic processing (OLAP) systems – More common name for the DSS, provides ability to sum, count, average, perform other simple arithmetic operations on groups of data  Remarkable characteristic of OLAP reports – format is dynamic  OLAP report has measures/facts and dimensions  Measures – data item of interest (i.e. Total sales, average sales, average cost)  Dimension – Characteristic of a measure (i.e. purchase date, customer type, customer location, sales region) OLAP Cube – another name for OLAP report Drill down – act of further diving the data in an OLAP report into more detail MIS in Use: Sports Decisions Go High Tech (page 250)  Fewer general managers make multi-million-dollar player decisions solely on a scout’s qualitative assessment  Turning to scientific and statistical techniques that capture more data and reduce biases  Decision making in pro sports now includes objective data that are often statistically analyzed to provide insight and reduce flaws  This type of decision making essential in baseball, and is fast adapting in basketball and hockey  Salary cap *fixed amount that a team can spend on their payroll) limits ability of a team to buy up all the available talent o Meaning teams need to make the most effective decisions  Number of managers with advanced degrees in statistic or analytics is increasing Q4: What are BI systems and how do they provide competitive advantage? Business intelligence (BI) system – system that provides info for improving decision making and also fosters a competitive advantage in organizations Summary of characteristics and competitive advantages of four categories of BI systems: Business Intelligence Characteristics Competitive Advantage System Reporting Systems • Integrate and process • Improve decisions by data by sorting, grouping, providing relevant, summing, and formatting accurate, and timely • Produce, administer, and information to the right deliver reports person Data-mining Systems • Use sophisticated • Improve decisions by statistical techniques o find discovering patterns and patterns and relationships relationships in data to predict future outcomes Market-basket analysis – a data- mining system that computes correlations of items on past orders to determine items that are frequently purchased together Knowledge Management • Share knowledge of • Improve decisions by (KM) Systems products, product uses, publishing employee
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