Chapter 8 - Decision Making and Business Intelligence.docx

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Computer Science
Computer Science 1032A/B
Diane Goldstein

CHAPTER 8 – DECISION MAKING AND BUSINESS INTELLIGENCE 1. WHATARE THE CHALLENGES MANAGERS FACE IN MAKING DECISIONS ­ RussellAckoff  article; Management Misinformaton Systems ­ Wrote about 3 assumptions ­ 1. Managers will have no problem making decisions if they get the data they need Ackoff: managers have too many possibilities to choose from, they should not expect decisions to improve even with perfect data uncertainty and complexity surrounding decisions make them challenging ­ 2. Poor decisions are made because managers lack relevant information Ackoff: managers suffer more from an overabundance of irrelevant data aka information overload ­ 3. Managers are aware of the data they need Ackoff: managers are unsure of what data they require, results in information overload Information Overload ­ managers today are facing information overload ­ 403 petabytes of new data was created in 2002 ­ petabyte: roughly the amount of all the printer material ever written ­ data is growing at the rate of 30% a year ­ by 2010, 3.3 Exabytes of data will have been generated ­ exponential growth of data occurs inside organizations as well as outside ­ challenge for managers is to find appropriate data and incorporate them into decision processes ­ information systems can help and hinder this process Data Quality ­ raw operational data are seldom suitable for more sophisticated reporting/data mining ­ fig 8-2  lists major problem categories ­ 1. Although data that are critical for successful operations must be complete and accurate, data that are only marginally necessary do not need to be ­ dirty data: problematic data ­ 2. Missing Values  ex. Non profit org can process donation without knowing donor’s gender or age  data mining app will suffer if values are missing ­ 3. Inconsistent Data  common in data gathered over time, ex. Changed phone number ­ 4. Data not Integrated ­ 5. Wrong Granularity  refers to degree of summarization or detail o coarse data: highly summarized o fine detail express precise detail o ex. Capture customers clicking behavior: clickstream data very fine data, includes everything customers do at website  must throw away millions of clicks o generally better to have too fine granularity than too coarse o fine data can be made coarser by summing and combining  GoogleAnalytics ­ 6. Too much Data 2. WHAT IS OLTPAND HOW DOES IT SUPPORT DECISION MAKING? ­ functional information systems are used to capture details about business transactions and then create updated information by processing transaction details  details about these transactions are crucial because they are efficient and accurate ­ being online  using computers to capture information electronically  most web based apps are examples ­ online transaction processing (OLTP) system: collecting data electronically and processing the transactions online ­ 2 basic ways that transactions can be processed ­ real time  when transactions are entered and processed immediately upon entry, little or no delay in updating the system with new data ­ batch  waiting for many transactions to pule up before processing, system waits until it has a batch of transactions before the data is processed and info is updated ­ real time systems more complex and cost more to implement but provide most up to date information o ex. Ticketmaster ­ OLTP systems backbone of all functional, cross function and interorganizational systems in company ­ Designed to efficiently enter, process and store data ­ Combine large databases with efficient input devices such as grocery store scanners, cash registers, etc to process transactions quickly and accurately ­ OLTPs support decision making by providing raw information about transactions and status for an organization 3. WHATARE OLAPAND THE DATA RESOURCE CHALLENGE ­ data does not create value if it’s not used ­ competitive advantage of information is realized when organizations use the datat hey have collected to help make better decisions ­ data resource challenge: when data may not be used to improve decision making ­ quickest way to explain data resource challenge  consider if company views data as an asset  resource from which future economic benefit may be obtained o but who’s in charge of managing this asset? ­ Although we like to think of data as an asset, we don’t really treat them like an important resource ­ Decision support systems (DSS) or online analytic processing (OLAP): systems that focus on making OLTP collected data useful for decision making ­ OLAP provides ability to sum, count average and perform other simple arithmetic operations on groups of data ­ Format is dynamic  viewer can change the report’s structure (therefore term online) ­ OLAP report has measures/facts (data item of interest; item to be summed, averaged or processed in OLAP report) and dimensions (characteristic of a measures  purchase date, customer type, customer location, etc) ­ Fig 8-3 ­ Presentation of a measure with associated dimensions like fig 8-3 is called OLAP cube  same things as OLAP report ­ Distinguishing characteristic of an OLAP report is that the user can alter the format of the report ­ Can drill down into the data  further divide the data into more detail ­ If database is large, doing necessary calculating, grouping, and sorting for such dynamic displays will require substantial computing power ­ OLAP servers have been developed to perform OLAP analysis 4. WHATARE BI SYSTEMS AND HOW DO THEY PROVIDE COMPETITIVEADVANTAGE? ­ business intelligence (BI) system: system that provides information for improving decision making ­ vary in characteristics and capabilities and in ways they foster comp adv ­ fig 8-6 ­ reporting systems: integrate data from multiple sources and process those data by sorting, grouping, summ
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