Presentation on theme: "Chapter 9 Business Intelligence Systems"— Presentation transcript:
1 Chapter 9 Business Intelligence Systems Jason C. H. Chen, Ph.D.Professor of MISSchool of Business AdministrationGonzaga UniversitySpokane, WA 99258
2 “We’re Sitting On All This Data. I Want to Make It Pay.” Anne wants membership data to:Combine membership data and publicly available dataEnable target marketingIncrease wedding revenue
3 Study QuestionsQ1: 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?
4 BUSINESS INTELLIGENCE Business intelligence – information that people use to support/improve their decision-making effortsPrinciple BI enablers include:TechnologyPeopleCultureTechnologyEven the smallest company with BI software can do sophisticated analyses today that were unavailable to the largest organizations a generation ago. The largest companies today can create enterprisewide BI systems that compute and monitor metrics on virtually every variable important for managing the company. How is this possible? The answer is technology—the most significant enabler of business intelligence.PeopleUnderstanding the role of people in BI allows organizations to systematically create insight and turn these insights into actions. Organizations can improve their decision making by having the right people making the decisions. This usually means a manager who is in the field and close to the customer rather than an analyst rich in data but poor in experience. In recent years “business intelligence for the masses” has been an important trend, and many organizations have made great strides in providing sophisticated yet simple analytical tools and information to a much larger user population than previously possible.CultureA key responsibility of executives is to shape and manage corporate culture. The extent to which the BI attitude flourishes in an organization depends in large part on the organization’s culture. Perhaps the most important step an organization can take to encourage BI is to measure the performance of the organization against a set of key indicators. The actions of publishing what the organization thinks are the most important indicators, measuring these indicators, and analyzing the results to guide improvement display a strong commitment to BI throughout the organization.
5 Working , Not Just Harder SmarterOverlapping Human/Organizational (Culture, Process)/ Technological factors in BI/KM:PEOPLEORGANIZATIONALPROCESSESOverlapping Human/Organizational/ Technological factors in KM:People (workforce)Organizational ProcessesTechnology (IT infrastructure)IS – IT, Organization and ManagementTECHNOLOGYKnowledgeN
6 CRM and BI ExampleA Grocery store in U.K. with the following “patterns” found:Every Thursday afternoonYoung Fathers (why?) shopping at storeTwo of the followings are always included in their shopping listDiapers andBeersWhat other decisions should be made as a store manager (in terms of store layout)?Short term vs. Long termThis is an example of cross-sellingOther types of promotion: up-sell, bundled-sellIT (e.g., BI) helps to find valuable information then decision makers make a timely/right decision for improving/creating competitive advantages.
7 Q/ACan the “patterns” in the grocery store example be produced from its Database?Y/NWhy?It only can be produced from its “Data Warehouse” using a kind of “data mining” software.
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 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.ReportingData-miningReporting – routine decisionData mining – analysisKnowledge – expertise to be retained and reused right awayKnowledge management
10 teFig 9-1: Structure of a Business Intelligence System
11 Q/A 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 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.Five components: H/SW, data, procedure and people
13 Example Uses of Business Intelligence (Decision Support Systems)Fig 9-2:Example Uses of Business Intelligence
14 The primary activities in the BI process are: 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.Data acquisitionBI analysisPublish results
15 What Are the Three Primary Activities in the Business Intelligence Process? The principle is the same as the “simple” model we learned before. What is it?Fig 9-3: Three Primary Activities in the BI Process
16 Using BI for Problem-solving at GearUp: Process and Potential Problems Obtain commitment from vendorRun sales eventSell as many items as possibleOrder amount actually soldReceive partial order and damaged itemsIf received less than ordered, ship partial order to customersSome customers cancel orders
17 Tables Used for BI Analysis at GearUp Fig 9-4: Tables Used for BI Analysis at GearUp
18 GearUp Analysis: Item Summary and Lost Sales Summary Reports Fig 9-5: Extract of the Item_Siummary_DataFig 9-6: Lost Sales Summary Report
19 Short and Damaged Shipments Details Report Fig 9-7: Lost Sales Detail Report
20 Publish ResultsOptionsPrint and distribute via or collaboration toolPublish on Web server or SharePointPublish on a BI serverAutomate results via Web service
21 Why extract operational data for BI processing? 3: How Do Organizations Use Data Warehouses and Data Marts to Acquire Data?Why extract operational data for BI processing?Security and controlOperational not structured for BI analysisBI analysis degrades operational server performanceOperational data is structured for fast and reliable transaction processing.Placing BI applications on operational servers can dramatically REDUCE system performanceT/F: Placing BI applications on operational servers can dramatically increase system performance.Answer:FALSEOperational data is structured for fast and reliable “transaction processing” (e.g., payroll).
22 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 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 dataETLETL: Extract, Transformation, LoadFig 9-11 Components of a Data Warehouse
24 Data Marts and the Data Warehouse Legacy systems feed data to the warehouse.The warehouse feeds specialized information to departments (data marts).Operational Data StoreLegacy SystemsFinanceData MartAccountingMarketingSalesETLOrganizationalDataWarehouseETLETL: Extract, Transformation, Load
25 Examples of Consumer Data that Can Be Purchased Fig 9-12 Examples of Consumer Data for Sale
26 Possible Problems with Source (Operational) Data Fig Possible Problems with Source (Operational) Data
27 Information Cleansing or Scrubbing Standardizing Customer name from Operational SystemsAsk your students if they have ever received more than one piece of identical mail, such as a flyer, catalog, or applicationIf so, ask them why this might have occurredCould it have occurred because their name was in many different disparate systems?What is the cost to the business of sending multiple identical marketing materials to the same customers?ExpenseRisk of alienating customersPat (or Patti) Burton information was entered in different ways and saved in different operational systems (i.e., Sales, Customer Service and Billing). They are, therefore, cleansed by a ‘Cleaning’ software and the cleaned/accurate information was saved in the Customer Information.They should be created and saved in a single repository (DB) and in a single/consistent form
28 Data Warehouses vs. Data Marts 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.Fig 9-14 Data mart Examples
29 4. How Do Organizations Use Typical Reporting Applications Four Basic operations:Sorting FilteringGrouping CalculatingFormattingWe 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.
30 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 opportunitiesFrequency: How frequently a customer purchased items? => retentionMonetary Value: How much a customer spends on each purchase? => profitabilityRFM AnalysisSort the data by date (for recency), times (for frequency), and purchase amount (for money), respectivelyDivide the sorted data into five groupsAssign 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 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 productsF = how frequently a customer purchases your productsM = how much money a customer typically spends on your productsThe the score, the better the customer, and, consequently, the more profit the company will be.lowerFig 9-15 Example of RFM Score Data
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 AjaxBloominghams 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 middleSet up on automated contact system or use the Davidson account as a training exercise80/20 Rule (Pareto Principle)
33 Q/AU.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 itdid 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 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?ANSWEREXCEL withPivot table
35 Online Analytical Processing (OLAP) Online Analytical Processing (OLAP) cubes, useMeasures 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 Example Grocery Sales OLAP ReportOLAP Product Family by Store Type
36 Example Expanded Grocery Sales OLAP Report Figure 9-17Fig 9-17: Example of Expanded Grocery Sales OLAP Report
37 Example of Drilling Down into Expanded Grocery Sales OLAP Report Fig 9-18: Example of Drilling Down into Expanded Grocery Sales OLAP Report
38 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 databaseThird-party vendors provide software for more extensive graphical displays
39 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 dataProfit by Product >>> Product Line >>> DivisionDrill-down – ability to move from summary/general to lower/specific levels of detailRevenue by Year >>> Quarter >>>>Week >>>DaySlice and Dice – ability to look across dimensionsSales by Region SalesProfit and Revelers by Product Line
40 Slicing a data cubeREGIONHoffer’s text (chapter 11)CUSTOMER
41 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 Database vs. Datawarehouse DBMSDatawarehouse???
43 Database vs. Datawarehouse DBMSDatawarehouseData Mining
44 How do BI Tools Obtain Data? ETLETL: Extract, Transformation, Load
45 Data-mining Applications 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 WarehouseFig 9-19 Data Mining Origins
46 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 analysisAnalysts apply data-mining techniques and then observe the resultsAnalysts create a hypotheses after analysis is completedCluster analysis (and decision tree), a common technique in this category groups entities together that have similar characteristicsSupervised data-mining characteristics:Analysts develop a model prior to their analysisApply statistical techniques such as Market Basket Analysis to estimate parameters of a modelRegression analysis is a technique in this category that measures the impact of a set of variables on another variableNeural networks predict values and make classifications.Used for making predictionsData mining can begin at a summary information level (coarse granularity) and progress through increasing levels of detail (drilling down), or the reverse (drilling up)Data-mining tools include query tools, reporting tools, multidimensional analysis tools, statistical tools, and intelligent agentsAsk your students to provide an example of what an accountant might discover through the use of data-mining toolsAns: An accountant could drill down into the details of all of the expense and revenue finding great business intelligence including which employees are spending the most amount of money on long-distance phone calls to which customers are returning the most productsCould the data warehousing team at Enron have discovered the accounting inaccuracies that caused the company to go bankrupt?If the did spot them, what should the team have done?
47 Unsupervised vs. Supervised Data Mining No model before running analysisHypotheses created after analysisCluster analysis to find groupsModel created before analysisHypotheses created before analysisRegression analysis: make predictions
48 Used for predicting values and making classifications Neural NetworksUsed for predicting values and making classificationsComplicated set of nonlinear equationsGo to and search for “neural network”
49 Probability for BI – Market Basket Analysis (Upselling and Cross-selling) Support - The probability of two items (A&B) will be purchased together.P(A&B) = P(A&B)/Total # of transactionsConfidence - 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 #times an item will be purchased when a customer entering the store Market Basket Analysis at a Dive Shop (Total # of Transactions (TOT)= 400)(s1) Purchase Mask and Fins together,A: FinsB: MaskP(A&B)/TOTP(Fins & Mask) = 250/400=0.625P(A | B) = P(A&B)/P(B)P(Fins | Mask)= P(Fins&Mask)/P(Mask)=250/270 = .926(c1) Proportion of customers who bought a mask also bought fins (buying fins given s/he bought mask)P(A | B) /P(A)P(fins | mask)/P(fins)= confidence/base probability=.926/.7=1.32the lift of fins and maskFig 9-20 Market-Basket Analysis at a Dive Shop
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 timesProbability 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=0.7Support : P (A&B)/TOTe.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=0.075Confidence: 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=0.926We 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).A: Fins; B: MaskCross-
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 purchasede.g., (e2) probability of customers entering into the store and buying fins is P(Fins)=280/400=0.7Confidence (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=0.167Thus, someone buys a dive computer, the likelihood that she will also buy fins falls from 0.7 to 0.167Q: 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 1.32Please 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 finsNO
53 Q/AIn 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-sellingB) value added sellingC) break-evenD) portfolioAnswer:A
54 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.If student is a junior and works in a restaurant, then predict grade>If student is a senior and is a nonbusiness major, then predict grade<---If student is a junior and does not work in a restaurant, then predict gradeSince major is not significant to JUNIORS in the study.Make no prediction since they are 50/50The more different groups, the better the classification will be.In this example, we are classifying students depending on whether their grade was greater than 3.0 or less or equal to 3.0.For example, if every student who lived off campus earned a grade higher than 3.0, and every student who lived on campus earned a grade lower than 3.0then the program would use the variable live-off-campus or live-on-campus to classify students.<---If student is a senior and is a business major, then make predictionnoFig 9-21 Decision Tree Examples for MIS Class (Hypothetical Data)
55 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.Since major is not significant to JUNIORS in the study.Make no prediction since they are 50/50Fig 9-21 Decision Tree Examples for MIS Class (Hypothetical Data)Fig 9-22 Credit Score Decision Tree
56 A Decision Tree for a Loan Evaluation Classifying likelihood of defaultExamined 3,485 loans28 percent of those defaultedEvaluation criteriaPercentage of loan past due less than 50 percent = .94, no defaultPercentage of loan past due greater than 50 percent = .89, defaultSubdivide groups A and B each into three classifications: CreditScore, MonthsPastDue, and CurrentLTV
57 A Decision Tree for a Loan Evaluation Resulting rulesIf 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 andIf 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 segmentDecision 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 Fig 9-22: Credit Score Decision Tree more than half paid (Accepted)orless than half paidandAcceptedFigure CE14-4otherwisereject the loan.
60 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.
61 Sharing Document Content Indexing - most important content function in KM applicationsOnly authorized people (employees) are allowed to access to available “Indexing” systemsReal Simple Syndication (RSS) - subscribing to content sourcese.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
62 KNOWLEDGE MANAGEMENTThe 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”Why is knowledge one of the real competitive advantages?It is difficult to duplicate knowledgeIt can take years to acquireIt is a personal assetWhat if an organization could capture all of a persons knowledge using technology?You would no longer need that person in the organization
63 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.TacitExplicitFour modes of K conversion between Tacit K and explicit KTacit to Tacit - Socialization (Sympathized Knowledge)Tacit to explicit - Externalization (Conceptual Knowledge)Explicit to tacit – Internalization (Operational Knowledge)Explicit to explicit – Combination (Systematic Knowledge)
64 Tacit and Explicit KNOWLEDGE Oral Communication“Tacit” Knowledge50-95%Information RequestExplicit Knowledge Base5 -50 %“Explicit” KnowledgeIntellectual and knowledge-based assets fall into two categoriesExplicit knowledge – consists of anything that can be documented, archived, and codified, often with the help of ITTacit knowledge - knowledge contained in people’s headsFour modes of K conversion between Tacit K and explicit KTacit to Tacit - Socialization (Sympathized Knowledge)Tacit to explicit - Externalization (Conceptual Knowledge)Explicit to tacit – Internalization (Operational Knowledge)Explicit to explicit – Combination (Systematic Knowledge)Information Feedback
65 Explicit and Tacit Knowledge Reasons why organizations launch knowledge management programsIntellectual and knowledge-based assets fall into two categoriesExplicit knowledge – consists of anything that can be documented, archived, and codified, often with the help of ITTacit knowledge - knowledge contained in people’s headsWhat types of knowledge management programs could your college pursue to help new students adapt to the college?Effective study habitsWriting rulesResearch databaseCourse evaluations
66 The Four Modes of Knowledge Conversion TOExplicit KnowledgeTacit KnowledgeA. Socialization(Sympathized Knowledge)B. Externalization(Conceptual Knowledge)Tacit KnowledgeTransferring 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 modelsFROMC. Internalization(Operational Knowledge)D. Combination(Systematic Knowledge)Converting explicit knowledge into tacit knowledge; learning by doing; studying previously captured explicit knowledge (manuals, documentation) to gain technical know-howCombining existing explicit knowledge through exchange and synthesis into new explicit knowledgeFour modes of K conversion between Tacit K and explicit KTacit to Tacit - Socialization (Sympathized Knowledge) [sharing K thru conversation]Tacit to explicit - Externalization (Conceptual Knowledge) [studying/learning from lectures HTML hws]Explicit to tacit – Internalization (Operational Knowledge) [reading text your own knowledge]Explicit to explicit – Combination (Systematic Knowledge) [Many text books/ Google search your paper]Explicit KnowledgeCWhich mode is the one for classroom processes? _____Source: Ikujiro Nonaka and Hirotaka Takeuchi, The Knowledge-Creating Company, 1995
67 Expert Systems Encode human knowledge as Rule-based systems (IF/THEN) Rules created by interviewing experts (culture issue)Major problems with ES:Expensive to developUnpredictable maintenanceOver hyped
68 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.ExamplesMedical Expert Systems andLegal Expert Systems etc.
69 Pharmacy Alert - Expert Systems for Pharmacies 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.Fig 9-25 Alert from Pharmacy Clinical Decision Support System
70 Q7 What Are the Alternatives for Publishing Business Intelligence? Fig BI Publishing Alternatives
71 Components of a 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.Fig 9-27 Components of Generic Business Intelligence System
72 What are the Management Functions of a BI Server? The management function of a BI serverMaintain metadata about the authorized allocation of BI results to users.It tracks what results are available,It tracks who is authorized to view them, andIt 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.Answer: “PULL” – automatically deliveredWhich option is for the “Grocery Store (UK)” case?Push
73 DATA MININGData-mining software includes many forms of AI such as neural networks and expert systemsData-mining tools apply algorithms to information sets to uncover inherent trends and patterns in the informationAnalysts use this information to develop new business strategies and business solutionsAsk your students to identify an organization that would “not” benefit from investing in data warehousing and data-mining toolsAns: NoneCLASSROOM EXERCISEAnalyzing Multiple Dimensions of InformationJump! is a company that specializes in making sports equipment, primarily basketballs, footballs, and soccer balls. The company currently sells to four primary distributors and buys all of its raw materials and manufacturing materials from a single vendor. Break your students into groups and ask them to develop a single cube of information that would give the company the greatest insight into its business (or business intelligence).Product A, B, C, and DDistributor X, Y, and ZPromotion I, II, and IIISalesSeasonDate/TimeSalesperson Karen and JohnVendor Smithson
74 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 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 < 30 and income <= $25,000 and credit rating < 3 and credit amount > $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 Essential Value Propositions for a Successful Company Business _______________ CompetencyOutsourcingCrowdsourcingOffshoring________Set corporate goals and get executive sponsorship for the initiativeModelCoreFirst, you have to have a business model, then, the company needs to set corporate goals and get executive sponsorship for the initiative.""Start with your business objectives of the product or service the company is selling, figure out where it is in the lifecycle, and determine which phase of CRM to focus on, for example, the company should determine whether it wants to focus on acquiring customers, retaining customers or up-selling and cross selling to customers."Examples: Dell vs. Gateway and Toyota vs. GM/FORDExecution
77 Any Sustainable Knowledge? Most sustainable Knowledge is“Learning to Learn and Learning to Change.”CAPACITY TO LEARN and how to adapt to change
78 Companies will know more about your purchasing habits and psyche. Q8: 2022Companies 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?