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Dr. Chen, Management Information Systems Chapter 9 Business Intelligence Systems Jason C. H. Chen, Ph.D. Professor of MIS School of Business Administration.

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Presentation on theme: "Dr. Chen, Management Information Systems Chapter 9 Business Intelligence Systems Jason C. H. Chen, Ph.D. Professor of MIS School of Business Administration."— Presentation transcript:

1 Dr. Chen, Management Information Systems Chapter 9 Business Intelligence Systems Jason C. H. Chen, Ph.D. Professor of MIS School of Business Administration Gonzaga University Spokane, WA

2 Dr. Chen, Management Information Systems “We’re Sitting On All This Data. I Want to Make It Pay.” Anne wants membership data to: Combine membership data and publicly available data Enable target marketing Increase wedding revenue 2

3 Dr. Chen, Management Information Systems Study Questions Q1: How do organizations use business intelligence (BI) systems? Q2: What are the three primary activities in the BI process? Q3: How do organizations use data warehouses and data marts to acquire data? Q4: How do organizations use typical reporting applications? Q5: How do organizations use typical data mining applications? Q6: What is the role of knowledge management systems? Q7: What are the alternatives for publishing business intelligence? Q8: 2022? 3

4 Dr. Chen, Management Information Systems BUSINESS INTELLIGENCE Business intelligence – information that people use to support/improve their decision-making efforts Principle BI enablers include: –Technology –People –Culture

5 Dr. Chen, Management Information Systems Working, Not Just Harder Overlapping Human/Organizational (Culture, Process)/ Technological factors in BI/KM: PEOPLE TECHNOLOGY ORGANIZATIONAL PROCESSES  Knowledge N Smarter

6 Dr. Chen, Management Information Systems CRM and BI Example A Grocery store in U.K. with the following “patterns” found: Every Thursday afternoon Young Fathers (why?) shopping at store Two of the followings are always included in their shopping list –Diapers and –Beers What other decisions should be made as a store manager (in terms of store layout)? Short term vs. Long term –This is an example of cross-selling –Other types of promotion: up-sell, bundled-sell IT (e.g., BI) helps to find valuable information then decision makers make a timely/right decision for improving/creating competitive advantages.

7 Dr. Chen, Management Information Systems Q/A Can the “patterns” in the grocery store example be produced from its Database? Y/N Why? It only can be produced from its “Data Warehouse” using a kind of “data mining” software. 7

8 Dr. Chen, Management Information Systems 8 Q1: How Do Organizations Use Business Intelligence (BI) Systems? Information systems generate enormous amounts of operational data that contain patterns, relationships, clusters, and trends about customers, suppliers, business partners, and employees that can facilitate management, especially planning and forecasting. Business intelligence (BI) systems produce such information from operational data. Data communications and data storage are essentially free, enormous amounts of data (Big Data) are created and stored every day.  12,000 gigabytes per person of data, worldwide in 2009

9 Dr. Chen, Management Information Systems Why do organizations need business intelligence? BI systems are computer programs provide valuable information for decision making. Three primary BI systems: –__________ tools read data, process them, and format the data into structured reports (e.g., sorting, grouping, summing, and averaging) that are delivered to users. They are used primarily for assessment. RFM is one of the tool for reporting. –___________ tools process data using statistical, regression, decision tree, and market basket techniques to discover hidden patterns and relationships, and make predictions based on the results –_______________________ tools store employee knowledge, make it available to whomever needs it. These tools are distinguished from the others because the source of the data is human knowledge. Reporting Data-mining Knowledge management

10 Dr. Chen, Management Information Systems 10 Fig 9-1: Structure of a Business Intelligence System [1] [2] [3]

11 Dr. Chen, Management Information Systems Q/A 11 Which of the following is true of source data for a BI system? A) It refers to the organization's metadata. B) It refers to data that the organization purchases from data vendors. C) It refers to the level of detail represented by the data. D) It refers to the hierarchical arrangement of criteria that predict a classification or a value. Answer: B

12 Dr. Chen, Management Information Systems 12 Tools vs. Applications vs. Systems BI tool (e.g., decision-tree analysis) is one or more computer programs. BI tools implement the logic of a particular procedure or process. BI application is the use of a tool on a particular type of data for a particular purpose. BI system is an information system having all five components (what are they?) that delivers results of a BI application to users who need those results.

13 Dr. Chen, Management Information Systems Example Uses of Business Intelligence 13 Fig 9-2:Example Uses of Business Intelligence [1] [2] [3] [4] (Decision Support Systems)

14 Dr. Chen, Management Information Systems Q2: What Are the Three Primary Activities in the Business Intelligence Process? The primary activities in the BI process are: –1. ______________ The process of obtaining, cleaning, organizing relating, and cataloging source data. –2. __________ The process of creating BI analysis: reporting, data mining, and knowledge management. –3. ____________ The process of delivering BI to the knowledge workers who need it. 14 Data acquisition BI analysis Publish results

15 Dr. Chen, Management Information Systems What Are the Three Primary Activities in the Business Intelligence Process? 15 Fig 9-3: Three Primary Activities in the BI Process [1][2][3] IPO The principle is the same as the “simple” model we learned before. What is it?

16 Dr. Chen, Management Information Systems Using BI for Problem-solving at GearUp: Process and Potential Problems 1.Obtain commitment from vendor 2.Run sales event 3.Sell as many items as possible 4.Order amount actually sold 5.Receive partial order and damaged items 6.If received less than ordered, ship partial order to customers 7.Some customers cancel orders 16

17 Dr. Chen, Management Information Systems Tables Used for BI Analysis at GearUp 17 Fig 9-4: Tables Used for BI Analysis at GearUp

18 Dr. Chen, Management Information Systems GearUp Analysis: Item Summary and Lost Sales Summary Reports 18 Fig 9-6: Lost Sales Summary Report Fig 9-5: Extract of the Item_Siummary_Data

19 Dr. Chen, Management Information Systems Short and Damaged Shipments Details Report 19 Fig 9-7: Lost Sales Detail Report

20 Dr. Chen, Management Information Systems Publish Results Options Print and distribute via or collaboration tool Publish on Web server or SharePoint Publish on a BI server Automate results via Web service 20

21 Dr. Chen, Management Information Systems 3: How Do Organizations Use Data Warehouses and Data Marts to Acquire Data? Why extract operational data for BI processing?  Security and control  Operational not structured for BI analysis  BI analysis degrades operational server performance 21 T/F: Placing BI applications on operational servers can dramatically increase system performance. Answer: FALSE Operational data is structured for fast and reliable “transaction processing” (e.g., payroll).

22 Dr. Chen, Management Information Systems Data Base, Data Warehouse and Data Marts Data base: An organized collection of logically related (current) data files. Data Warehouse: A data warehouse stores data from current and previous years (historical data) that have been extracted from the various operational and management database of an organization. Data mart: a subset of data warehouse that holds specific subsets of data for one particular functional area or project.

23 Dr. Chen, Management Information Systems Components of a Data Warehouse Fig 9-11 Components of a Data Warehouse Data warehouses and data marts address the problems companies have with missing data values and inconsistent data. They also help standardize data formats between operational data and data purchased from third-party vendors. These facilities prepare, store, and manage data specifically for data mining and analyses. operational data ETL ETL: Extract, Transformation, Load

24 Dr. Chen, Management Information Systems Data Marts and the Data Warehouse Organizational Data Warehouse Legacy systems feed data to the warehouse. The warehouse feeds specialized information to departments (data marts). Finance Data Mart Accounting Data Mart Marketing Data Mart Sales Data Mart Operational Data Store Legacy Systems ETL ETL: Extract, Transformation, Load

25 Dr. Chen, Management Information Systems Examples of Consumer Data that Can Be Purchased 25 Fig 9-12 Examples of Consumer Data for Sale

26 Dr. Chen, Management Information Systems Possible Problems with Source (Operational) Data 26 Fig 9-13 Possible Problems with Source (Operational) Data

27 Dr. Chen, Management Information Systems Information Cleansing or Scrubbing Standardizing Customer name from Operational Systems

28 Dr. Chen, Management Information Systems Data Warehouses vs. Data Marts 28 Fig 9-14 Data mart Examples Here’s the difference between a data warehouse and a data mart:  A data warehouse stores operational data and purchased data. It cleans and processes data as necessary. It serves the entire organization.  A data mart is smaller than a data warehouse and addresses a particular component or functional area of an organization.

29 Dr. Chen, Management Information Systems 4. How Do Organizations Use Typical Reporting Applications Four Basic operations: 1. Sorting 2. Filtering 3. Grouping 4. Calculating 5. Formatting We will use a ‘reporting application’ to analyze and rank customers based on their purchasing patterns to help company make better decision for increasing company’s revenue. 29

30 Dr. Chen, Management Information Systems What are typical reporting applications? RFM Analysis allows you to analyze and rank customers according to purchasing patterns as this figure shows. –Recency: How recently a customer purchased items? => leads and opportunities –Frequency: How frequently a customer purchased items? => retention –Monetary Value: How much a customer spends on each purchase? => profitability RFM Analysis –Sort the data by date (for recency), times (for frequency), and purchase amount (for money), respectively –Divide the sorted data into five groups –Assign 1 to top 20%, 2 to next 20%, 3 to the third 20%, 4 to the fourth 20% and 5 to the bottom 20%. –The the score, the better the customer. lower

31 Dr. Chen, Management Information Systems What does RFM analysis Tell? Example RFM Scores RFM Analysis allows you to analyze and rank customers according to purchasing patterns as this figure shows. –R = how recently a customer purchased your products –F = how frequently a customer purchases your products –M = how much money a customer typically spends on your products The the score, the better the customer, and, consequently, the more profit the company will be. Fig 9-15 Example of RFM Score Data lower

32 Dr. Chen, Management Information Systems 32 Interpreting RFM Score Results Ajax has ordered recently and orders frequently. M score of 3 indicates it does not order most expensive goods.  A good and regular customer but need to attempt to up-sell more expensive goods to Ajax Bloominghams has not ordered in some time, but when it did, ordered frequently, and orders were of highest monetary value.  May have taken its business to another vendor. Sales team should contact this customer immediately. Caruthers has not ordered for some time; did not order frequently; did not spend much.  Sales team should not waste any time on this customer. Davidson in middle  Set up on automated contact system or use the Davidson account as a training exercise 80/20 Rule (Pareto Principle)

33 Dr. Chen, Management Information Systems Q/A 33 U.S. Grocery Corp. is a large grocery chain store. FOODFARM, one of the customers of U.S. Grocery Corp. holds an RFM score of 111. Which of the following characteristics relates FOODFARM with its RFM score? A) FOODFARM has ordered recently and orders frequently, but it orders the least expensive goods. B) FOODFARM has not ordered in some time, but when it did order in the past it ordered frequently, and its orders were of the highest monetary value. C) FOODFARM has not ordered for some time, it did not order frequently, and, when it did order, it bought the least-expensive items. D) FOODFARM has ordered recently and orders frequently, and it orders the most expensive goods. Answer: D

34 Dr. Chen, Management Information Systems OLAP and its Applications Online Analytical Processing (OLAP), a second type of reporting tool, is more generic than RFM. OLAP provides you with the dynamic ability to sum, count, average, and perform other arithmetic operations on groups of data. Reports, also called OLAP cubes. What software and function that enable you to create OLAP and its applications? ANSWER –EXCEL with –Pivot table

35 Dr. Chen, Management Information Systems Online Analytical Processing (OLAP) Online Analytical Processing (OLAP) cubes, use –Measures which are data items of interest. In the figure below a measure is Store Sales Net. –Dimensions which are characteristics of a measure. In the figure below a dimension is Product Family. Fig 9-16 Example Grocery Sales OLAP Report OLAP Product Family by Store Type

36 Dr. Chen, Management Information Systems Example Expanded Grocery Sales OLAP Report Figure Fig 9-17: Example of Expanded Grocery Sales OLAP Report

37 Dr. Chen, Management Information Systems Example of Drilling Down into Expanded Grocery Sales OLAP Report 37 Fig 9-18: Example of Drilling Down into Expanded Grocery Sales OLAP Report

38 Dr. Chen, Management Information Systems Fig 9 (Extra): Role of OLAP Server & OLAP Database OLAP servers are special products that 1) read data from an operational database, 2) perform some preliminary calculations, and then3) store the results in an OLAP database Third-party vendors provide software for more extensive graphical displays

39 Dr. Chen, Management Information Systems On-Line Analytic Processing (OLAP) Enables mangers and analysts to interactively examine and manipulate large amounts of detailed and consolidated data from different dimensions. Analytical Processing: –Drill-up (Consolidation) – ability to move from detailed data to aggregated data Profit by Product >>> Product Line >>> Division –Drill-down – ability to move from summary/general to lower/specific levels of detail Revenue by Year >>> Quarter >>>>Week >>>Day –Slice and Dice – ability to look across dimensions Sales by Region Sales Profit and Revelers by Product Line

40 Dr. Chen, Management Information Systems Slicing a data cube CUSTOMER REGION

41 Dr. Chen, Management Information Systems Data Base, Data Warehouse and Data Marts Data base: An organized collection of logically related (current) data files. Data Warehouse: A data warehouse stores data from current and previous years (historical data) that have been extracted from the various operational and management database of an organization. Data mart: a subset of data warehouse that holds specific subsets of data for one particular functional area or project.

42 Dr. Chen, Management Information Systems Database vs. Datawarehouse DBMS Database Datawarehouse ???

43 Dr. Chen, Management Information Systems Database vs. Datawarehouse DBMS Database Datawarehouse Data Mining

44 Dr. Chen, Management Information Systems How do BI Tools Obtain Data? ETL ETL: Extract, Transformation, Load

45 Dr. Chen, Management Information Systems Fig 9-19 Data Mining Origins Businesses use statistical techniques to find patterns and relationships among data and use it for classification and prediction. Data mining techniques are a blend of statistics and mathematics, and artificial intelligence (AI) and machine-learning. Data-mining Applications Data Warehouse

46 Dr. Chen, Management Information Systems Unsupervised vs. Supervised Data Mining Data mining is an automated process of discovery and extraction of hidden and/or unexpected patterns of collected data in order to create models for decision making that predict future behavior based on analyses of past activity. There are two types of data-mining techniques: –Unsupervised data-mining characteristics: No model or hypothesis exists before running the analysis Analysts apply data-mining techniques and then observe the results Analysts create a hypotheses after analysis is completed Cluster analysis (and decision tree), a common technique in this category groups entities together that have similar characteristics –Supervised data-mining characteristics: Analysts develop a model prior to their analysis Apply statistical techniques such as Market Basket Analysis to estimate parameters of a model Regression analysis is a technique in this category that measures the impact of a set of variables on another variable Neural networks predict values and make classifications. Used for making predictions

47 Dr. Chen, Management Information Systems Unsupervised vs. Supervised Data Mining Unsupervised No model before running analysis Hypotheses created after analysis Cluster analysis to find groups Supervised Model created before analysis Hypotheses created before analysis Regression analysis: make predictions 47

48 Dr. Chen, Management Information Systems Neural Networks Used for predicting values and making classifications Complicated set of nonlinear equations Go to and search for “neural network”http://kdnuggets.com 48

49 Dr. Chen, Management Information Systems Probability for BI – Market Basket Analysis (Upselling and Cross-selling) 49 Support - The probability of two items (A&B) will be purchased together. P(A&B) = P(A&B)/Total # of transactions Confidence - Conditional probability is the probability that an event (A) will occur, when another event (B) is known to occur or to have occurred. If the events are A and B respectively, this is said to be "the probability of A given B. P(A | B) = P(A&B)/P(B)

50 Dr. Chen, Management Information Systems Market Basket Analysis at a Dive Shop (Total # of Transactions (TOT)= 400) 50 Fig 9-20 Market-Basket Analysis at a Dive Shop A: Fins B: Mask P(A&B)/TOT P(Fins & Mask) = 250/400=0.625 #times an item will be purchased when a customer entering the store (s1) Purchase Mask and Fins together, (c1) Proportion of customers who bought a mask also bought fins (buying fins given s/he bought mask) P(A | B) = P(A&B)/P(B) P(Fins | Mask)= P(Fins&Mask)/P( Mask)=250/270 =.926 the lift of fins and mask P(A | B) /P(A) P(fins | mask)/P(fins)= confidence/base probability=.92 6/.7=1.32

51 Dr. Chen, Management Information Systems 51 Market-Basket Analysis is a supervised data-mining tool for determining sales patterns. It helps businesses create cross-selling opportunities (i.e., buying relevant products together). Three terms used with this type of analysis are:  Support: the probability that two items will be purchased together (e.g., Fins and Mask will be purchased together)  Confidence: a conditional probability estimate (e.g., proportion of the customers who bought a mask also bought fins)  Lift: ratio of confidence to the base probability (e.g., ratio between customers of buying fins after buying mask and those buying fins of walking into the store). It shows that how much the based probability increases or decreases when other products are purchased. Total # of transactions (TOT) = 400 times Probability of an item that customer will purchase: P(A)/TOT, e.g., e.g., (e1) probability of customers entering into the store and buying mask is P(Mask)=270/400=0.675 (e2) probability of customers entering into the store and buying fins is P(Fins)=280/400=0.7 Support : P (A&B)/TOT e.g., (s1) Purchase Mask and Fins together, P(Fins & Mask) = 250/400=0.625 (s2) Purchase Tank and Dive computer together: P(Tank & Dive computer)=30/400=0.075 Confidence: P(A | B) = P(A&B)/P(B) e.g., (c1) Proportion of customers who bought a mask also bought fins (buying fins given s/he bought mask) P(Fins | Mask)= P(Fins&Mask)/P(Mask)=250/270=0.926 We then compare (e2) and (c1) if someone buys a mask, the likelihood that he or she will also buy fins increases substantially from.7 to.926. Q: If you are a store manager, how will you train your sales personnel? A: Train them to try to sell fins to anyone buying a mask. (________ selling). Cross- A: Fins; B: Mask

52 Dr. Chen, Management Information Systems 52 (continue) Market-Basket Analysis is a supervised data-mining tool for determining sales patterns. It helps businesses create cross-selling opportunities (i.e., buying relevant products together). Three terms used with this type of analysis are:  Support: the probability that two items will be purchased together (e.g., Fins and Mask will be purchased together)  Confidence: a conditional probability estimate (e.g., proportion of the customers who bought a mask also bought fins)  Lift: ratio of confidence to the base probability (e.g., ratio between customers of buying fins after buying mask and those buying fins of walking into the store). It shows that how much the based probability increases or decreases when other products are purchased e.g., (e2) probability of customers entering into the store and buying fins is P(Fins)=280/400=0.7 Confidence (cont.) P(A | B) = P(A&B)/P(B) e.g., (c2) Proportion of customers who bought a dive computer also bought fins (i.e., buying fins, given she or he bought a dive computer) P(Fins | Dive computer)= P(Fins&Dive computer)/P(Dive computer)=20/120=0.167 Thus, someone buys a dive computer, the likelihood that she will also buy fins falls from 0.7 to Q: If you are a store manager, how will you take any action on these types of customers? A: ______ Lift : P(A | B) /P(A) e.g., the lift of fins and mask, P(fins | mask)/P(fins)=confidence/base probability=.926/.7=1.32. Thus, the likelihood that people buy fins when they buy a mask increases by 32 percent. Surprisingly, it turns out the lift of fins and a mask is the same as the lift of a mask and fins. Both are 1.32 Please note that this analysis only shows shopping carts with two items. We cannot say from this data what the likelihood is that customer, given that they bought a mask, will buy both weights and fins NO

53 Dr. Chen, Management Information Systems Q/A 53 In marketing transactions, the fact that customers who buy product X also buy product Y creates a(n) ________ opportunity. That is, "If they're buying X, sell them Y," or "If they're buying Y, sell them X." A) cross-selling B) value added selling C) break-even D) portfolio Answer: A

54 Dr. Chen, Management Information Systems Decision Tree Example for MIS Classes (hypothetical data) A decision tree is a hierarchical arrangement of criteria that predicts a classification or value. It’s an unsupervised data-mining technique that selects the most useful attributes for classifying entities on some criterion. It uses if…then rules in the decision process. Here are two examples. Fig 9-21 Decision Tree Examples for MIS Class (Hypothetical Data) If student is a junior and works in a restaurant, then predict grade 3.0 If student is a senior and is a nonbusiness major, then predict grade 3.0 If student is a junior and does not work in a restaurant, then predict grade 3.0 If student is a senior and is a business major, then make prediction > < --- < no

55 Dr. Chen, Management Information Systems Summary of Decision Tree Analysis A decision tree is a hierarchical arrangement of criteria that predicts a classification or value. It’s an unsupervised data-mining technique that selects the most useful attributes for classifying entities on some criterion. It uses if…then rules in the decision process. Here are two examples. Fig 9-22 Credit Score Decision Tree Fig 9-21 Decision Tree Examples for MIS Class (Hypothetical Data)

56 Dr. Chen, Management Information Systems 56 A Decision Tree for a Loan Evaluation Classifying likelihood of default Examined 3,485 loans 28 percent of those defaulted Evaluation criteria A.Percentage of loan past due less than 50 percent =.94, no default B.Percentage of loan past due greater than 50 percent =.89, default Subdivide groups A and B each into three classifications: CreditScore, MonthsPastDue, and CurrentLTV

57 Dr. Chen, Management Information Systems 57 A Decision Tree for a Loan Evaluation Resulting rules If the loan is more than half paid, then accept the loan. If the loan is less than half paid and  If CreditScore is greater than and If CurrentLTV is less than.94, then accept the loan. Otherwise, reject the loan. Use this analysis to structure a marketing campaign to appeal to a particular market segment Decision trees are easy to understand and easy to implement using decision rules. Some organizations use decision trees to select variables to be used by other types of data-mining tools.

58 Dr. Chen, Management Information Systems Fig 9-22: Credit Score Decision Tree Figure CE14-4 more than half paid (Accepted) less than half paid Accepted and or otherwise reject the loan.

59 Dr. Chen, Management Information Systems What are typical data-mining applications?

60 Dr. Chen, Management Information Systems Q6. What Is the Role of Knowledge Management Systems? 1. KM fosters innovation by encourage free flow of ideas. 2. KM improves customer service by streamlining response time. 3. BIM boosts revenues by getting products and services to market faster. 4. KM enhances employee retention rates by recognizing the value of employees’ knowledge (sharing) and rewarding them for it. 5. KM streamlines operations and reduce costs by eliminating redundant or unnecessary processes. 60

61 Dr. Chen, Management Information Systems Sharing Document Content Indexing - most important content function in KM applications Only authorized people (employees) are allowed to access to available “Indexing” systems Real Simple Syndication (RSS) - subscribing to content sources e.g., With a program called RSS reader, you can subscribe to magazines, blogs, Web sites, and other content sources. Blogs - place where employees share their knowledge that may include RSS feeds 61

62 Dr. Chen, Management Information Systems KNOWLEDGE MANAGEMENT The process of creating value from intellectual capital and sharing that knowledge with employees, managers, suppliers, customers, and others who need it. Reporting and data mining are used to create new information from data, knowledge-management systems concern the sharing of knowledge that is known to exist. Knowledge management (KM) – the process of capturing, classifying, evaluating, retrieving, and sharing information assets in a way that provides context for effective decisions and actions. Knowledge management system (KMS) – an information system that supports the capturing and use of an organization’s “know-how”

63 Dr. Chen, Management Information Systems Tacit vs. Explicit Knowledge Intellectual and knowledge-based assets fall into two categories _______ knowledge is personal, context- specific and hard to formalize and communicate ________ knowledge can be easily collected, organized and transferred through digital means. Tacit Explicit

64 Dr. Chen, Management Information Systems Tacit and Explicit KNOWLEDGE Oral Communication “Tacit” Knowledge 50-95% Information Request “Explicit” Knowledge Explicit Knowledge Base % Information Feedback

65 Dr. Chen, Management Information Systems Explicit and Tacit Knowledge Reasons why organizations launch knowledge management programs

66 Dr. Chen, Management Information Systems The Four Modes of Knowledge Conversion Tacit Knowledge Explicit Knowledge Tacit Knowledge Explicit Knowledge A. Socialization (Sympathized Knowledge) C. Internalization (Operational Knowledge) B. Externalization (Conceptual Knowledge) D. Combination (Systematic Knowledge) Transferring tacit knowledge through shared experiences, apprenticeships, mentoring relationships, on–the-job training, “Talking at the water cooler” Articulating and thereby capturing tacit knowledge through use of metaphors, analogies, and models Converting explicit knowledge into tacit knowledge; learning by doing; studying previously captured explicit knowledge (manuals, documentation) to gain technical know-how Combining existing explicit knowledge through exchange and synthesis into new explicit knowledge FROM TO Source: Ikujiro Nonaka and Hirotaka Takeuchi, The Knowledge-Creating Company, 1995 Which mode is the one for classroom processes? _____ C

67 Dr. Chen, Management Information Systems Expert Systems Encode human knowledge as Rule-based systems (IF/THEN) Rules created by interviewing experts (culture issue) Major problems with ES:  Expensive to develop  Unpredictable maintenance  Over hyped 67

68 Dr. Chen, Management Information Systems What are Characteristics of Expert Systems? They capture human expertise and format it for use by nonexperts. They are rule-based systems that use if…then rules to store the expert’s knowledge. They gather data from people rather than using data-mining techniques. They are difficult and expensive to develop. They are difficult to maintain because the rules are constantly changing. They have been unable to live up to the high expectations set by their name. Examples –Medical Expert Systems and – Legal Expert Systems etc.

69 Dr. Chen, Management Information Systems Pharmacy Alert - Expert Systems for Pharmacies Fig 9-25 Alert from Pharmacy Clinical Decision Support System This is an example of the output from a medical expert system that is part of a decision support system. Based on the system’s rules, an alert (for safety) is issued if the system detects a problem with a patient’s prescriptions.

70 Dr. Chen, Management Information Systems Q7 What Are the Alternatives for Publishing Business Intelligence? 70 Fig 9-26 BI Publishing Alternatives

71 Dr. Chen, Management Information Systems Components of a Generic Business Intelligence System Fig 9-27 Components of Generic Business Intelligence System This figure shows the components of a generic BI system. A BI application server delivers results in a variety of formats to devices for consumption by BI users. A BI server provides two functions: management and delivery.

72 Dr. Chen, Management Information Systems What are the Management Functions of a BI Server? The management function of a BI server –Maintain metadata about the authorized allocation of BI results to users. –It tracks what results are available, –It tracks who is authorized to view them, and –It tracks when the results are provided to users. Options for managing BI results: –Users can pull their results from a Web site using a portal server with a customizable user interface. –A server can automatically push information to users through alerts which are messages announcing events as they occur. –A report server, a special server dedicated to reports, can supply users with information. Which option is for the “Grocery Store (UK)” case?Push

73 Dr. Chen, Management Information Systems DATA MINING Data-mining software includes many forms of AI such as neural networks and expert systems

74 Dr. Chen, Management Information Systems Other Data Mining Examples A telephone company used a data mining tool to analyze their customer ’ s data warehouse. The data mining tool found about 10,000 supposedly residential customers that were expending over $1,000 monthly in phone bills. After further study, the phone company discovered that they were really small business owners trying to avoid paying business rates *

75 Dr. Chen, Management Information Systems Data Mining Examples (cont.) 65% of customers who did not use the credit card in the last six months are 88% likely to cancel their accounts. If age $25,000 then the minimum loan term is 10 years. 82% of customers who bought a new TV 27" or larger are 90% likely to buy an entertainment center within the next 4 weeks.

76 Dr. Chen, Management Information Systems Essential Value Propositions for a Successful Company Business ________ _______ Competency –Outsourcing –Crowdsourcing –Offshoring ________ –Set corporate goals and get executive sponsorship for the initiative Model Core Execution

77 Dr. Chen, Management Information Systems Any Sustainable Knowledge? Most sustainable Knowledge is “Learning to Learn and Learning to Change.” CAPACITY TO LEARN and how to adapt to change

78 Dr. Chen, Management Information Systems Q8: 2022 Companies will know more about your purchasing habits and psyche. Social singularity — machines can build their own information systems. Will machines possess and create information for themselves? 78

79 Dr. Chen, Management Information Systems End of Chapter 9 79


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