Chapter 9 Business Intelligence Systems

1. Chapter 9Business Intelligence Systems 1

2. This chapter surveys the most common business intelligence and knowledge-management applications, discusses the need and purpose for data warehouses, and explains how business intelligence applications are delivered to users as business intelligence systems. Along the way, you�ll learn tools and techniques that MRV can use to identify the guides that contribute the most (and least) to its competitive strategy. We�ll wrap up by discussing some of the potential benefits and risks of mining credit card data. Chapter Preview

3. Study Questions Q1 Why do organizations need business intelligence? Q2 What business intelligence systems are available? Q3 What are typical reporting applications? Q4 What are typical data-mining applications? Q5 What is the purpose of data warehouses and data marts? Q6 What are typical knowledge-management applications? Q7 How are business intelligence applications delivered? Q8 2020?

4. BUSINESS INTELLIGENCE Business intelligence � information that people use to support/improve their decision-making efforts Principle BI enablers include: Technology People Culture Technology Even 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. People Understanding 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. Culture A 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.Technology Even 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. People Understanding 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. Culture A 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 Overlapping Human/Organizational (Culture, Process)/ Technological factors in BI/KM: Overlapping Human/Organizational/ Technological factors in KM: People (workforce) Organizational Processes Technology (IT infrastructure) IS � IT, Organization and Management Overlapping Human/Organizational/ Technological factors in KM: People (workforce) Organizational Processes Technology (IT infrastructure) IS � IT, Organization and Management

6. CRM and BI Example A Grocery store in U.K. 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 IT (e.g., BI) helps to find valuable information then decision makers make a timely/right decision for improving/creating competitive advantages.

7. Why Do Organizations Need Business Intelligence? Information systems generate enormous amounts of operational data that contain patterns, relationships, clusters, and other information that can facilitate management, especially planning and forecasting. Business intelligence systems produce such information from operational data. Data communications and data storage are essentially free, enormous amounts of data are created and stored every day. 12,000 gigabytes per person of data, worldwide in 2009

8. How Big Is an Exabyte? (See video) Fig 9-1 How Big is an Exabyte?

9. Study Questions Q1 Why do organizations need business intelligence? Q2 What business intelligence systems are available? Q3 What are typical reporting applications? Q4 What are typical data-mining applications? Q5 What is the purpose of data warehouses and data marts? Q6 What are typical knowledge-management applications? Q7 How are business intelligence applications delivered? Q8 2020?

10. Business Intelligence (BI) Tools BI systems provide valuable information for decision making. (BI video) Three primary BI systems: Reporting Tools Integrate data from multiple systems Sorting, grouping, summing, averaging, comparing data RFM is one of the tool for reporting. Data-mining Tools Use sophisticated statistical techniques, regression analysis, and decision tree analysis Used to discover hidden patterns and relationships Market-basket analysis

11. Business Intelligence Tools Knowledge-management tool Create value by collecting and sharing human knowledge about products, product uses, best practices, other critical knowledge Used by employees, managers, customers, suppliers, others who need access to company knowledge

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 peopleFive components: H/SW, data, procedure and people

13. Study Questions Q1 Why do organizations need business intelligence? Q2 What business intelligence systems are available? Q3 What are typical reporting applications? Q4 What are typical data-mining applications? Q5 What is the purpose of data warehouses and data marts? Q6 What are typical knowledge-management applications? Q7 How are business intelligence applications delivered? Q8 2020?

14. Basic Reporting Operations Reporting tools produce information from data using five basic operations: Sorting Grouping Calculating Filtering Formatting

15. List of Sales Data

16. Data Sorted by Customer Name

17. 9-17 The figure on the left shows the raw sales data sorted by customer names. The figure on the right shows data that�s been sorted and grouped.

18. Sales Data Filtered to ShowRepeat Customers and Formatted for Easier Understanding Fig 9-5 Sales Data Filtered to Show Repeat Customers

19. 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.

20. What does RFM analysis Tell? 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.

21. 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

22. RFM Tools Classify Customers? Divides customers into five groups and assigns a score from 1 to 5 R score 1 = top 20 percent in most recent orders R score 5 = bottom 20 percent (longest since last order) F score 1 = top 20 percent in most frequent orders F score 5 = bottom 20 percent least frequent orders M score 1 = top 20 percent in most money spent M score 5 = bottom 20 percent in amount of money spent

23. 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.

24. Interpreting RFM Score Results 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

25. Online Analytical Processing (OLAP) OLAP, a second type of reporting tool, is more generic than RFM. OLAP provides the ability to sum, count, average, and perform other simple arithmetic operations on groups of data. Remarkable characteristic of OLAP reports is that they are dynamic. The viewer of the report can change report�s format, hence the term online.

26. How Are OLAP Reports Dynamic? OLAP reports Simple arithmetic operations on data Sum, average, count, and so on Dynamic User can change report structure View online Measure Data item to be manipulated�total sales, average cost Dimension Characteristic of measure�purchase date, customer type, location, sales region

27. OLAP: Summary Online Analytical Processing (OLAP) is more generic than RFM and provides you with the dynamic ability to sum, count, average, and perform other arithmetic operations on groups of data. Reports, also called 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.

28. OLAP Reports OLAP cube Presentation of measure with associated dimensions a.k.a. OLAP report Users can alter format. Users can drill down into data. Divide data into more detail May require substantial computing power

29. Fig 9-8 OLAP Product Family & Store Location by Store Type

30. Fig 9-9 OLAP Product Family & Store Location by Store Type, Drilled Down to Show Stores in California

31. Fig 9-10 Role of OLAP Server & OLAP Database

32. 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

33. Hoffer�s text (chapter 11)Hoffer�s text (chapter 11)


35. 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.

36. Database vs. Datawarehouse

37. Database vs. Datawarehouse

38. How do BI Tools Obtain Data?

39. Fig 9-11 Convergence Disciplines for Data Mining What are typical data-mining applications?

40. What are typical data-mining applications? 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, 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 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 Data 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 agents Ask your students to provide an example of what an accountant might discover through the use of data-mining tools Ans: 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 products Could 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? Data 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 agents Ask your students to provide an example of what an accountant might discover through the use of data-mining tools Ans: 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 products Could 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?

41. Decision Tree Analysis of MIS Class Grades Student�s characteristics Class (junior or senior), major, employment, age, club affiliations, and other characteristics Values used to create groups that were as different as possible on the classification GPA above or below 3.0 Results Best criterion�Class Next subdivide Seniors and Juniors into more pure groups Seniors�business and non-business majors Juniors�restaurant employees and non-restaurant employees Best classifier is whether the junior worked in a restaurant

42. Create Set of If/Then Decision Rules If student is a junior and works in a restaurant, then predict grade > 3.0. If student is a senior and is a non-business 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 no prediction.

43. Decision Trees Decision tree Hierarchical arrangement of criteria that predict a classification or value Unsupervised data-mining technique Basic idea of a decision tree Select attributes most useful for classifying something on some criteria that create disparate groups More different or pure the groups, the better the classification

44. 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 50/50 The 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. Since major is not significant to JUNIORS in the study. Make no prediction since they are 50/50 The 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.

45. Decision Tree Figure CE16-3

46. Decision Tree for Loan Evaluation Common business application Classify loan applications by likelihood of default Rules identify loans for bank approval Identify market segment Structure marketing campaign Predict problems

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

48. 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 572.6 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.

49. Fig 9-14: Credit Score Decision Tree

50. Market-Basket Analysis Market-basket analysis is a supervised data-mining technique for determining sales patterns. Uses statistical methods to identify sales patterns in large volumes of data Shows which products customers tend to buy together Used to estimate probability of customer purchase Helps identify cross-selling opportunities "Customers who bought book X also bought book Y�

51. Market-Basket Analysis is a data-mining tool for determining sales patterns. It helps businesses create cross-selling opportunities (i.e., buying relevant products together). Or even promote bundle-selling (for buying �fins�) with discount for those customers bought �mask� since they are likely to buy �fins� after purchasing �mask�. P(Fins)=280/1000=.28 P(Mask)=270/1000=.27 Market-Basket Analysis is a data-mining tool for determining sales patterns. It helps businesses create cross-selling opportunities (i.e., buying relevant products together). Or even promote bundle-selling (for buying �fins�) with discount for those customers bought �mask� since they are likely to buy �fins� after purchasing �mask�. P(Fins)=280/1000=.28 P(Mask)=270/1000=.27

52. Market-Basket Terminology Support Probability that two items will be bought together Fins and masks purchased together 150 times, thus support for fins and a mask is 150/1,000, or 15 percent Support for fins and weights is 60/1,000, or 6 percent Support for fins along with a second pair of fins is 10/1,000, or 1 percent

53. Market-Basket Terminology Lift Ratio of confidence to base probability of buying item Shows how much base probability increases or decreases when other products are purchased Example: Lift of fins and a mask is confidence of fins given a mask, divided by the base probability of fins. Lift of fins and a mask is .5556/.28 = 1.98

54. Market-Basket Terminology Confidence What proportion of the customers who bought a mask also bought fins? Conditional probability estimate Example: Probability of buying fins = 28% Probability of buying swim mask = 27% After buying fins, Probability of buying mask = 150/270 or 55.56% Likelihood that a customer will also buy fins almost doubles, from 28% to 55.56%. Thus, all sales personnel should try to sell fins to anyone buying a mask.

55. Regression Analysis CellphoneWeekendMinutes = 12 + (17.5 * CustomerAge) + (23.7 * NumberMonthsOfAccount) Using this equation, analysts can predict number of minutes of weekend cell phone use by summing 12, plus 17.5 times the customer�s age, plus 23.7 times the number of months of the account. Considerable skill is required to interpret the quality of such a model

56. Neural Networks Neural networks Popular supervised data-mining technique used to predict values and make classifications such as �good prospect� or �poor prospect� customers Complicated set of nonlinear equations See kdnuggets.com to learn more

57. What are typical data-mining applications?

58. DATA MINING Data-mining software includes many forms of AI such as neural networks and expert systems Data-mining tools apply algorithms to information sets to uncover inherent trends and patterns in the information Analysts use this information to develop new business strategies and business solutions Ask your students to identify an organization that would �not� benefit from investing in data warehousing and data-mining tools Ans: None CLASSROOM EXERCISE Analyzing Multiple Dimensions of Information Jump! 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 D Distributor X, Y, and Z Promotion I, II, and III Sales Season Date/Time Salesperson Karen and John Vendor Smithson Data-mining tools apply algorithms to information sets to uncover inherent trends and patterns in the information Analysts use this information to develop new business strategies and business solutions Ask your students to identify an organization that would �not� benefit from investing in data warehousing and data-mining tools Ans: None CLASSROOM EXERCISE Analyzing Multiple Dimensions of Information Jump! 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 D Distributor X, Y, and Z Promotion I, II, and III Sales Season Date/Time Salesperson Karen and John Vendor Smithson

59. Data Mining Analysis Data mining � the process of analyzing data to extract information not offered by the raw data alone To perform data mining users need data-mining tools Data-mining tool � uses a variety of techniques to find patterns and relationships in large volumes of information and infers rules that predict future behavior and guide decision making An example Grocery Store in UK Data 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 agents Ask your students to provide an example of what an accountant might discover through the use of data-mining tools Ans: 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 products Could 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? Data 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 agents Ask your students to provide an example of what an accountant might discover through the use of data-mining tools Ans: 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 products Could 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?

60. 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 *

61. 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.


63. What Is the Purpose of Data Warehouses and Data Marts? Purpose: (video) To extract and clean data from various operational systems and other sources To store and catalog data for BI processing Extract, clean, prepare data Stored in data-warehouse DBMS


65. What is the purpose of data warehouses and data marts? Fig 9-15 Components of a Data Warehouse

67. Data Warehouse Data Sources Internal operations systems External data purchased from outside sources Data from social networking, user-generated content applications Metadata concerning data stored in data-warehouse meta database Clickstream data of customers� clicking behavior on a Web site


70. Fig 9-16: Example Typical of Customer Credit Data

71. Problems with Operational Data Dirty data�mistakes in spelling or punctuation, incorrect data associated with a field, incomplete or outdated data or even data that is duplicated in the database.

72. Examples of Dirty Data A value of �B� for customer gender 213 for customer age Value of 999�999�9999 for a U.S. phone number Part color of �gren� mail address of WhyMe@GuessWhoIAm.org.

73. Problems with Operational Data Too much data causes: Curse of dimensionality Problem caused by the exponential increase in volume associated with adding extra dimensions to a (mathematical) space. Too many rows or data points With more attributes, the easier it is to build a model that fits the sample data but that is worthless as a predictor. Major activities in data mining concerns efficient and effective ways of selecting attributes.

74. Data Warehouses and Data Marts? Fig 9-18 Data Mart Examples

75. Data Warehouses vs. Data Marts Data mart is a collection of data (video) Created to address particular needs Business function Problem Opportunity Smaller than data warehouse Users may not have data management expertise Need knowledgeable analysts for specific function Data extracted from data warehouse for a functional area

76. Study Questions Q1 Why do organizations need business intelligence? Q2 What business intelligence systems are available? Q3 What are typical reporting applications? Q4 What are typical data-mining applications? Q5 What is the purpose of data warehouses and data marts? Q6 What are typical knowledge management applications? Q7 How are business intelligence applications delivered? Q8 2020?

77. 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. Why is knowledge one of the real competitive advantages? It is difficult to duplicate knowledge It can take years to acquire It is a personal asset What if an organization could capture all of a persons knowledge using technology? You would no longer need that person in the organization Why is knowledge one of the real competitive advantages? It is difficult to duplicate knowledge It can take years to acquire It is a personal asset What if an organization could capture all of a persons knowledge using technology? You would no longer need that person in the organization

78. 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. Four modes of K conversion between Tacit K and explicit K Tacit to Tacit - Socialization (Sympathized Knowledge) Tacit to explicit - Externalization (Conceptual Knowledge) Explicit to tacit � Internalization (Operational Knowledge) Explicit to explicit � Combination (Systematic Knowledge) Four modes of K conversion between Tacit K and explicit K Tacit to Tacit - Socialization (Sympathized Knowledge) Tacit to explicit - Externalization (Conceptual Knowledge) Explicit to tacit � Internalization (Operational Knowledge) Explicit to explicit � Combination (Systematic Knowledge)

79. Tacit and Explicit KNOWLEDGE Intellectual and knowledge-based assets fall into two categories Explicit knowledge � consists of anything that can be documented, archived, and codified, often with the help of IT Tacit knowledge - knowledge contained in people�s heads Four modes of K conversion between Tacit K and explicit K Tacit to Tacit - Socialization (Sympathized Knowledge) Tacit to explicit - Externalization (Conceptual Knowledge) Explicit to tacit � Internalization (Operational Knowledge) Explicit to explicit � Combination (Systematic Knowledge)Intellectual and knowledge-based assets fall into two categories Explicit knowledge � consists of anything that can be documented, archived, and codified, often with the help of IT Tacit knowledge - knowledge contained in people�s heads Four modes of K conversion between Tacit K and explicit K Tacit to Tacit - Socialization (Sympathized Knowledge) Tacit to explicit - Externalization (Conceptual Knowledge) Explicit to tacit � Internalization (Operational Knowledge) Explicit to explicit � Combination (Systematic Knowledge)

80. Explicit and Tacit Knowledge Reasons why organizations launch knowledge management programs Intellectual and knowledge-based assets fall into two categories Explicit knowledge � consists of anything that can be documented, archived, and codified, often with the help of IT Tacit knowledge - knowledge contained in people�s heads What types of knowledge management programs could your college pursue to help new students adapt to the college? Effective study habits Writing rules Research database Course evaluationsIntellectual and knowledge-based assets fall into two categories Explicit knowledge � consists of anything that can be documented, archived, and codified, often with the help of IT Tacit knowledge - knowledge contained in people�s heads What types of knowledge management programs could your college pursue to help new students adapt to the college? Effective study habits Writing rules Research database Course evaluations

81. The Four Modes of Knowledge Conversion Four modes of K conversion between Tacit K and explicit K Tacit 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] Four modes of K conversion between Tacit K and explicit K Tacit 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]

82. Primary Benefits of KM 1. KM fosters innovation by encouraging the free flow of ideas. 2. KM improves customer service by streamlining response time. 3. KM boosts revenues by getting products and services to market faster. 4. KM enhances employee retention rates by recognizing the value of employees� knowledge and rewarding them for it. 5. KM streamlines operations and reduces costs by eliminating redundant or unnecessary processes. KM preserves organizational memory by capturing and storing the lessons learned and best practices of key employees.

83. Sharing of Document Content and Employee Knowledge Sharing Document Content Collaboration systems are concerned with document creation and change management, KM applications are concerned with maximizing content use.

84. Two Typical Knowledge-Management Applications Two key technologies for sharing content in KM systems: Indexing�most important content function in KM applications that provide easily accessible and robust means of determining if content exists and a link to obtain the content. Used in conjunction with search functions.

85. Two Typical Knowledge-Management Applications RSS 2. (Real Simple Syndication)�a standard for subscribing to content sources on Web sites. An RSS Reader program helps users to: Subscribe to content sources. Periodically check sources for new or updated content through RSS feeds. Place content summaries in an RSS inbox with link to the full content. Think of RSS as an email system for content Data source must provide what is termed an RSS feed, which simply means that the site posts changes according to one of the RSS standards.

86. Fig 9-19: Interface of a Typical RSS Reader

87. Fig 20: Blog Posts of SharePoint Team Member

88. Expert Systems Another form of knowledge management are expert systems with the following characteristics: Expert systems attempt to capture human expertise and put it into a format that can be used by nonexperts. Expert systems are rule-based systems that use If?Then rules similar to those created by decision-tree analysis, except they are created from human experts instead of data-mining systems. Expert systems gather data from people rather than using data-mining techniques

89. Problems of Expert Systems Difficult and expensive to develop. They require many labor hours from both experts in the domain under study and designers of expert systems. High opportunity cost of tying up domain experts. Difficult to maintain. Nature of rule-based systems creates unexpected consequences when adding a new rule in middle of hundreds of others. A small change can cause very different outcomes. No expert system has the same diagnostic ability as knowledgeable, skilled, and experienced doctors. Rules/actions change frequently.

90. Expert Systems for Pharmacies Used as a safety net to screen decisions of doctors and other medical professionals. These systems help to achieve hospital�s goal of state-of-the-art, error-free care. DoseChecker, verifies appropriate dosages on prescriptions issued in the hospital. PharmADE, ensures that patients are not prescribed drugs that have harmful interactions. Pharmacy order-entry system invokes these applications as a prescription is entered. If either system detects a problem with the prescription, it generates an alert.

91. Pharmacy Alert


93. How Are Business Intelligence Applications Delivered?

94. 9-94 What Are the Management Functions of a BI Server? The management function of a BI server maintains metadata about the authorized allocation of BI results to users. It tracks what results are available, who is authorized to view them, and when the results are provided to users. Here are 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.

95. What Are the Management Functions of a BI Server? Maintains metadata about authorized allocation of BI results to users Tracks what results are available, what users are authorized to view those results, and schedule to provide results to authorized users. Adjusts allocations as available results change and users come and go.

96. BI Servers Vary in Complexity and Functionality Some BI servers are simply Web sites from which users can download, or pull BI application results. For example, a BI Web server might post results of an RFM analysis for salespeople to query to obtain RFM scores for their customers. Management function for such a site would simply be to track authorized users and restrict access.

97. Fig 9-23 Sample Portal, Provided by iGoogle BI Servers Vary in Complexity and Functionality and could operate as a portal server.

98. BI Portals Portals might provide common data such as local weather, and links to company news, and to BI application results such as reports on daily sales, operations, new employees, and results of data-mining applications. Authorized users are allowed to place reports, data-mining results, or other BI application results on their customized pages. BI application server pushes the subscribed results to the user.

99. Report Server A special case of a BI application server that serves only reports BI application servers track results, users, authorizations, page customizations, subscriptions, alerts, and data for any other functionality provided.

100. What Are the Delivery Functions of a BI Server? Track authorized users Track the schedule for providing results to users Issue exception alerts that notify users of an exceptional event Procedures used depends on the nature of the BI system Procedures tend to be more flexible than those in an operational system because users of a BI system tend to be engaged in work that is neither structured nor routine Procedures are determined by unique requirements of users BI results can be delivered to �any� device, such as computers, PDAs, phones, other applications such as Microsoft Office, and as a SOA service

101. Essential Value Propositions for a Successful Company Business Competency Set corporate goals and get executive sponsorship for the initiative First, 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/FORDFirst, 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/FORD

102. Any Sustainable Knowledge? Most sustainable Knowledge is �Learning to Learn and Learning to Change.�


104. 2020? Through data mining, companies, known as �data aggregators�, will know more about your purchasing psyche than you, your mother, or your analyst. If you use your card to purchase �secondhand clothing, retread tires, bail bond services, massages, casino gambling or betting� you alert the credit card company of potential financial problems and, as a result, it may cancel your card or reduce your credit limit. Absent laws to the contrary, by 2020 your credit card data will be fully integrated with personal and family data maintained by the data aggregators (like Acxiom and ChoicePoint). By 2020, some online retailers will know a lot more about you, data aggregators, and most consumer�s purchases than we�ll know ourselves.

105. END of Chapter 9 105

Chapter 9 Business Intelligence Systems

Chapter 9 Business Intelligence Systems

Presentation Transcript

Thinking and Intelligence Chapter 9

Chapter 9 Business Intelligence Systems

Using MIS 2e Chapter 9: Business Intelligence Systems David Kroenke

CHAPTER 9 INTELLIGENCE AND CREATIVITY

Chapter 9: Primate Intelligence

Business Intelligence (BI) Systems

Chapter 9 Business Intelligence Systems

Chapter 10: Business Intelligence

Chapter 6 - Enhancing Business Intelligence Using Information Systems

Chapter 12 Business Intelligence

Chapter 9 - Intelligence

Chapter 9: Intelligence & Language

Chapter 9 Intelligence and Creativity

CHAPTER 9 INTELLIGENCE AND CREATIVITY

Chapter 1: DECISION SUPPORT SYSTEMS AND BUSINESS INTELLIGENCE

Chapter 9. Systems

Chapter 9 – Business Intelligence

INTELLIGENCE Chapter 9

CHAPTER SIX DATA: BUSINESS INTELLIGENCE

Modelling Business Systems 9

Business Intelligence Systems

Chapter 9 Business Intelligence Systems