Chapter 9 Business Intelligence and Knowledge Management


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Chapter 9 Business Intelligence and Knowledge Management

  1. 1. Chapter 9 Business Intelligence and Knowledge Management
  2. 2. Agenda <ul><li>Business Intelligence System </li></ul><ul><li>Reporting system </li></ul><ul><li>Data Warehouse </li></ul><ul><li>Data Mart </li></ul><ul><li>Knowledge Management Systems </li></ul><ul><li>Discussion and Case Study </li></ul>
  3. 3. Business Intelligence System <ul><li>Need </li></ul><ul><ul><li>Inexpensive storage </li></ul></ul><ul><ul><li>Drowning in data (terabyte - 12, petabyte - 15, exabyte - 18) </li></ul></ul><ul><ul><li>Starving for useful information </li></ul></ul><ul><li>Purpose </li></ul><ul><ul><li>Provide the right information, to the right user, at the right time for actions </li></ul></ul><ul><li>Business intelligence tool </li></ul><ul><ul><li>Searching business data for finding patterns </li></ul></ul><ul><ul><li>Types: reporting tool and data-mining tool </li></ul></ul>
  4. 4. Reporting Tool <ul><li>Programs </li></ul><ul><ul><li>Read data from sources </li></ul></ul><ul><ul><li>Sort and group data </li></ul></ul><ul><ul><li>Calculate simple totals and averages </li></ul></ul><ul><ul><li>Produce reports </li></ul></ul><ul><ul><li>Deliver reports to the users </li></ul></ul><ul><li>For business assessment: a customer canceling an important order </li></ul>
  5. 5. Data-mining Tool <ul><li>Programs </li></ul><ul><ul><li>Use sophisticated statistical techniques and complex mathematics </li></ul></ul><ul><ul><li>Search for patterns and relationships among data </li></ul></ul><ul><li>For business prediction using probability </li></ul><ul><ul><li>Calculating the probability of a customer defaulting on a loan </li></ul></ul><ul><ul><li>Assessing new loan applications </li></ul></ul>
  6. 6. Reporting System - I <ul><li>Purpose </li></ul><ul><ul><li>Create meaningful information from disparate data sources and to deliver that information to the proper user on a timely basis </li></ul></ul><ul><li>Operation </li></ul><ul><ul><li>Filtering data </li></ul></ul><ul><ul><li>Sorting data </li></ul></ul><ul><ul><li>Grouping data </li></ul></ul><ul><ul><li>Making simple calculations </li></ul></ul><ul><li>Component </li></ul><ul><ul><li>A database of reporting metadata with description of reports, users, groups, roles, events, and other entities in the reporting activity </li></ul></ul>
  7. 7. Reporting System - II <ul><li>Report type </li></ul><ul><ul><li>Static </li></ul></ul><ul><ul><li>Dynamic </li></ul></ul><ul><ul><li>Query </li></ul></ul><ul><ul><li>Online analytical process (dynamic grouping structure) </li></ul></ul><ul><li>Report media </li></ul><ul><ul><li>Paper </li></ul></ul><ul><ul><li>Voice </li></ul></ul><ul><ul><li>Digital: screen, digital dashboard, Web service, email alert </li></ul></ul>
  8. 8. Reporting System - III <ul><li>Report mode </li></ul><ul><ul><li>Push: preset schedule </li></ul></ul><ul><ul><li>Pull: user request </li></ul></ul><ul><li>Function </li></ul><ul><ul><li>Authoring: connecting to data sources, creating report structure, and formatting report </li></ul></ul><ul><ul><li>Management: who, what, when, by what mean, user account, and user group </li></ul></ul><ul><ul><li>Delivery: push or pull, method, time </li></ul></ul><ul><li>Example </li></ul><ul><ul><li>RFM analysis </li></ul></ul><ul><ul><li>Online analytical processing (OLAP) </li></ul></ul>
  9. 9. RFM Analysis <ul><li>Analyzing and ranking customers according to their purchasing patterns </li></ul><ul><ul><li>How recently (R) a customer has ordered </li></ul></ul><ul><ul><li>How frequently (F) a customer orders </li></ul></ul><ul><ul><li>How much money (M) the customer spends per order </li></ul></ul>
  10. 10. RFM Score <ul><li>The program first sorts customer purchase records by the date of their most recent (R) purchase </li></ul><ul><li>The program then divides the customers into five groups and gives customers in each group a score of 1 to 5. </li></ul><ul><ul><li>The top 20% of the customers having the most recent orders are given an R score 1 (highest). </li></ul></ul><ul><li>The program then re-sorts the customers on the basis of how frequently they order. </li></ul><ul><ul><li>The top 20% of the customers who order most frequently are given a F score of 1 (highest). </li></ul></ul><ul><li>Finally the program sorts the customers again according to the amount spent on their orders. </li></ul><ul><ul><li>The 20% who have ordered the most expensive items are given an M score of 1 (highest). </li></ul></ul>
  11. 11. OLAP <ul><li>Characteristics </li></ul><ul><ul><li>Provide the ability to sum, count, average, and other simple arithmetic operations on groups of data </li></ul></ul><ul><ul><li>Display the current state of the business </li></ul></ul><ul><ul><li>The viewer can dynamically the report’s format </li></ul></ul><ul><ul><li>Drill down (detail data) </li></ul></ul><ul><li>Component </li></ul><ul><ul><li>Measure: the data item of interest (total, average) </li></ul></ul><ul><ul><li>Dimension: a characteristic of a measure (customer type, sales region) </li></ul></ul><ul><li>OLAP server & OLAP database: store results from operational databases </li></ul>
  12. 12. Role of OLAP Server and OLAP Database
  13. 13. Problems with Operational Data <ul><li>Problematic data (dirty data) </li></ul><ul><ul><li>Missing elements </li></ul></ul><ul><ul><li>Inconsistent data </li></ul></ul><ul><li>Nonintegrated data </li></ul><ul><li>Too fine or too coarse (clickstream data) </li></ul><ul><li>Wrong granularity (format) </li></ul><ul><li>Curse of dimensionality: the more attributes, the easier to build a model to fit the sample data but worthless as a predictor </li></ul>
  14. 14. Data Warehouse <ul><li>Programs read operational data and extract, clean, and prepare data for business intelligence processing </li></ul><ul><li>Data-warehouse DBMS </li></ul><ul><ul><li>Extract and provide data to business intelligence tools such as data-mining programs </li></ul></ul><ul><ul><li>Internal data and purchased from outside sources </li></ul></ul><ul><ul><li>Metadata: source, format, assumption, constraint, and other facts about the data </li></ul></ul>
  15. 15. Components of a Data Warehouse
  16. 16. Data Mart <ul><li>A data collection, smaller than the data warehouse, to address a particular component or functional area of the business </li></ul><ul><li>Expensive to create, staff, and operate data warehouse and data mart </li></ul>
  17. 17. Data Mart Examples
  18. 18. Data Mining <ul><li>The application of statistical and mathematic techniques to find patterns and relationships among data for classifying and predicting </li></ul><ul><li>From artificial intelligence and machine-learning </li></ul><ul><li>Type </li></ul><ul><ul><li>Unsupervised data mining </li></ul></ul><ul><ul><li>Supervised data mining </li></ul></ul>
  19. 19. Convergence Disciplines for Data Mining
  20. 20. Unsupervised Data Mining <ul><li>No model or hypothesis before running the analysis </li></ul><ul><li>Apply the data-mining technique to the data and observe the results </li></ul><ul><li>Create hypotheses after the analysis to explain the patterns found </li></ul><ul><li>Cluster analysis </li></ul><ul><ul><li>Find groups of similar customers from customer order and demographic data </li></ul></ul><ul><li>Decision Tree </li></ul><ul><ul><li>A hierarchical arrangement of criteria to predict a classification or a value </li></ul></ul><ul><ul><li>Loan-decision rules </li></ul></ul>
  21. 21. Supervised Data Mining <ul><li>Develop a model prior to the analysis and apply statistical techniques to data to estimate parameters of the model </li></ul><ul><li>Regression analysis </li></ul><ul><ul><li>Measure the impact of a set of variables on another variable </li></ul></ul><ul><li>Neural network </li></ul><ul><ul><li>Predict values and make classifications such as “good prospect” or “poor prospect” customers. </li></ul></ul>
  22. 22. Market-Basket Analysis <ul><li>A data-mining technique for determining sales patterns </li></ul><ul><li>Show the products that customers tend to buy together </li></ul><ul><li>Support: the probability that two items will be purchased together </li></ul><ul><li>A standard CRM analysis </li></ul>
  23. 23. Knowledge Management (KM) <ul><li>The process of creating value from intellectual capital and sharing that knowledge with employees, managers, suppliers, customers, and others </li></ul><ul><li>Emphasis is on people, their knowledge, and effective means for sharing that knowledge with others </li></ul><ul><li>Preserve organizational memory by capturing and storing the lessons learned and best practices of key employees </li></ul><ul><li>Enable employees and others to leverage organizational knowledge to work smarter </li></ul>
  24. 24. Benefits of KM <ul><li>Free flow of ideas (innovation) </li></ul><ul><li>Storing lesson learned and best practice </li></ul><ul><li>Better customer service </li></ul><ul><li>Boosting profit by getting product to the market faster </li></ul><ul><li>Increasing employee retention </li></ul><ul><li>Reducing cost by eliminating redundant and unnecessary process </li></ul>
  25. 25. KM Content Management - I <ul><li>Track organizational documents, Web pages, graphics, and related materials </li></ul><ul><li>Concern with the creation, management, and delivery of documents for a specific KM purpose </li></ul>
  26. 26. KM Content Management -II <ul><li>Problems </li></ul><ul><ul><li>Complicated and huge </li></ul></ul><ul><ul><li>Dependency relationship between documents </li></ul></ul><ul><ul><li>Perishable document contents </li></ul></ul><ul><ul><li>Multinational languages </li></ul></ul><ul><li>Delivering methods </li></ul><ul><ul><li>Pull using index and search engine </li></ul></ul><ul><ul><li>Web browsers </li></ul></ul>
  27. 27. Knowledge Sharing <ul><li>Portals, discussion groups, and email </li></ul><ul><ul><li>Idea publishing </li></ul></ul><ul><ul><li>Bulletin board </li></ul></ul><ul><ul><li>Frequent ask question </li></ul></ul><ul><li>Collaborations system </li></ul><ul><ul><li>Web broadcast </li></ul></ul><ul><ul><li>Video conference </li></ul></ul><ul><ul><li>Net meeting </li></ul></ul><ul><li>Expert system </li></ul><ul><ul><li>Decision tree with narrow domain and complex rules </li></ul></ul><ul><ul><li>Expensive and difficult to create and maintain </li></ul></ul>
  28. 28. Issues of Knowledge Sharing <ul><li>Problems </li></ul><ul><ul><li>Competition </li></ul></ul><ul><ul><li>Shy </li></ul></ul><ul><li>Strategy </li></ul><ul><ul><li>Reward </li></ul></ul><ul><ul><li>Incentive </li></ul></ul>
  29. 29. Discussion <ul><li>Security (275a-b) </li></ul><ul><ul><li>State some methods for an organization to prevent the semantic security problems. </li></ul></ul><ul><li>Problem Solving (283a-b) </li></ul><ul><ul><li>State two statistic usages and its associated risks in a business decision making process. </li></ul></ul><ul><li>Ethics (289a-b) </li></ul><ul><ul><li>State some disadvantages of using decision tree as the admission rules. </li></ul></ul><ul><li>Reflections (295a-b) </li></ul><ul><ul><li>Is it a common practice of lower management to manipulate the data and generate the information to accommodate the needs of upper management in the real business world? How do you avoid this situation as the upper management? </li></ul></ul>
  30. 30. Case Study <ul><li>Case 9-1 Laguna Tools (300-301) every question </li></ul>
  31. 31. Points to Remember <ul><li>Business Intelligence System </li></ul><ul><li>Reporting system </li></ul><ul><li>Data Warehouse </li></ul><ul><li>Data Mart </li></ul><ul><li>Knowledge Management Systems </li></ul><ul><li>Discussion and Case Study </li></ul>