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Chapter 11: Business Intelligence and Knowledge Management

Chapter 11: Business Intelligence and Knowledge Management

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Chapter 11: Business Intelligence and Knowledge Management

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  1. Management Information Systems, Sixth Edition Chapter 11: Business Intelligence and Knowledge Management

  2. Objectives • Explain the concepts of data mining and online analytical processing • Explain the notion of business intelligence and its benefits to organizations • Identify needs for knowledge storage and management in organizations • Explain the challenges in knowledge management and its benefits to organizations Management Information Systems, Sixth Edition

  3. Objectives (continued) • Identify possible ethical and societal issues arising from the increasing globalization of information technology Management Information Systems, Sixth Edition

  4. Data Mining and Online Analysis • Data warehouse: a large database containing historical transactions and other data • Data warehouses are useless without software tools to process the data into meaningful information • Business intelligence (BI): information gleaned with information analysis tools • Also called business analytics Management Information Systems, Sixth Edition

  5. Data Mining • Data mining: the process of selecting, exploring, and modeling large amounts of data • Used to discover relationships or useful patterns that can support decision making • Data-mining tools may use complex statistical analysis applications • Data-mining queries are more complex than traditional queries • Combination of data-warehousing techniques and data-mining tools facilitates the prediction of future outcomes Management Information Systems, Sixth Edition

  6. Data Mining (continued) • Data mining has four main objectives: • Sequence or path analysis: finding patterns where one event leads to another • Classification: finding whether certain facts fall into predefined groups • Clustering: finding groups of related facts not previously known • Forecasting: discovering patterns that can lead to reasonable predictions Management Information Systems, Sixth Edition

  7. Data Mining (continued) • Data mining techniques are applied to various fields, including marketing, fraud detection, and targeted marketing to individuals • Predicting customer behavior: • Banking: help find profitable customers, detect patterns of fraud, and predict bankruptcies • Mobile phone services vendors: help determine factors that affect customer loyalty • Customer loyalty programs ensure a steady flow of customer data into data warehouses Management Information Systems, Sixth Edition

  8. Management Information Systems, Sixth Edition

  9. Data Mining (continued) • Many industries utilize loyalty programs • Examples include frequent-flier programs and consumer clubs • These programs amass huge amounts of data about customers • UPS has a Customer Intelligence Group • Analyzes customer behavior • Predicts customer defections so that a salesperson can intervene to resolve problems Management Information Systems, Sixth Edition

  10. Data Mining (continued) • Identifying profitable customer groups • Financial institutions dismiss high-risk customers • Companies attempt to define narrow groups of potentially profitable customers • Utilizing loyalty programs • Amass huge amounts of data about customers • Help companies perform yield management and price-discrimination • Example: Harrah’s charges higher per-night rates to low-volume gamblers Management Information Systems, Sixth Edition

  11. Data Mining (continued) • Inferring demographics • Predict what customers are likely to purchase in the future • Amazon.com • Determines a customer’s age range based on his or her purchase history • Attempts to determine customer’s gender • Advertises for appropriate age groups based on the inferred customer demographics • Anticipates holidays Management Information Systems, Sixth Edition

  12. Data Mining – Machine Learning • Machine Learning • is an automated process that extracts patterns from data. • Two basic types • Supervised ML - automatically learn a model of the relationship between a set of input (or descriptive features) and an output (or target features) based on a set of historical examples, or instances. • Unsupervised ML – is when you have an input but you don’t have a corresponding output. The result is neither right or wrong but the idea is to discover patterns. Management Information Systems, Sixth Edition

  13. Data Mining – Machine Learning • Supervised ML (an example) • Below is an a historical examples or instances of loans data of customers from a bank. The descriptive features of this dataset are Occupation, Age and Loan-Salary Ratio and its target feature is the Outcome field. Management Information Systems, Sixth Edition

  14. Data Mining – Machine Learning • Supervised ML (an example) • Based on the historical instances as shown on the previous table. The descriptive features (i.e. Age, Occupation and Loan-Salary Ratio) map into the target feature (i.e. Outcome), namely, whether a bank customer defaulted (did not pay the loan) or was able to repay the loan, the machine learn the following model: If (Loan-Salary-Ratio > 3) then Outcome = Default Else Outcome = Repay Management Information Systems, Sixth Edition

  15. Data Mining – Machine Learning • Flowchart for the model If (Loan-Salary-Ratio > 3) T Print Repay F Print Default Management Information Systems, Sixth Edition

  16. Data Mining – Machine Learning • Example C++ code: #include <iostream> #include <string> #include <iomanip> using namespace std; using std::setw; int main () { int age[] = {34,41,36,41,48,61,37,40,33,32}; double loan_salary_ratio[] = {2.96, 4.64, 3.22, 3.11, 3.80, 2.52, 1.50, 1.93, 5.25, 4.15}; string occupation[] = {"Industrial", "Professional", "Professional", "Professional", "Industrial","Industrial","Professional" ,"Professional" ,"Industrial","Industrial"}; cout << "ID" << setw(10) << "Occupation" << setw(14) << "Age" << setw(12) << "Loan Salary Ratio" << setw(20) << "Outcome" << endl; for ( int i = 0; i < 10; i++ ) { cout << i << setw(7) << occupation[i] << setw(20) << age[i] << setw(12) << loan_salary_ratio[i]; if (loan_salary_ratio[i]>3) cout << setw(25) << "Repay" << endl; else cout << setw(25) << "Default" << endl; } return 0; } Management Information Systems, Sixth Edition

  17. Data Mining – Machine Learning • Data Mining Activity Part I – Below also is another historical examples or instances of banks loans from different customers. Management Information Systems, Sixth Edition

  18. Data Mining – Machine Learning • Supervised ML (an example) • Using the descriptive features of Loan-Salary Ratio, Age and Occupation mapping to Target feature (i.e. Outcome field) which has either the values of Repay or Default, create a Model and a Flowchart later on. • So, imagine yourself as the software (or algorithm for that matter) for machine learning. What possible pattern from the previous table could you detect that you could turn into a Model? • Your goal is to produce a Model (see Slide 14) and a Flowchart of the model (see Slide 15). • This is worth 20 points for the Model and 20 points also for the Flowchart. • Open the file that I send you and write your name (after Student Name). Make your Model and Flowchart and once your done send it to nationalcis@gmail.com with e-mail title Data Mining Activity Part I. • Tip : If you look at the Model on Slide 14 it has one (actually including the else statement) pattern, namely, if a loan has loan-salary ration greater than 3 then the Loan is not paid (or on Default) otherwise the customer was able to Repay. Now, on this exercise there are three possible patterns (and again four if you include the else statement). So, your first task is to detect patterns from the table on Slide 17 and turn it into a Model. And after the Model is done make a flowchart out of it. Management Information Systems, Sixth Edition

  19. Data Mining – Machine Learning • Data Mining Activity Part II – Convert your flowchart (which is based on your Model) into a C++ program. See Slides 15 and 16 for example. • Open the MSWord document that I send you and write your name after Student Name label. • Open your Visual Studio and create a Project in C++ with a name Data Mining Your Last Name (e.g. Data Mining Smith). Erase all the code. And then open the sample code that I send you in Notepad and then copy paste its content into your Visual Studio. • Convert your Flowchart into C++ code (See Slides 15 and 16 as your example for that) and make sure that it runs and it has no bug. • Once you are completed, paste your code from Visual Studio on the Microsoft Word document that I send you and then save it. Then send it to nationalcis@gmail.com with title Data Mining Activity Part II. Management Information Systems, Sixth Edition

  20. Online Analytical Processing • Online analytical processing (OLAP): a type of application used to exploit data warehouses • Provides extremely fast response times • Allows a user to view multiple combinations of two dimensions by rotating virtual “cubes” of information • Drilling down: the process of starting with broad information and then retrieving more specific information as numbers or percentages • Can use relational or dimensional databases designed for OLAP applications Management Information Systems, Sixth Edition

  21. Management Information Systems, Sixth Edition

  22. Online Analytical Processing (continued) • OLAP application composes tables “on the fly” based on the desired relationships • Dimensional database: data is organized into tables showing information summaries • Also called multidimensional databases • OLAP applications are powerful tools for executives Management Information Systems, Sixth Edition

  23. Management Information Systems, Sixth Edition

  24. Online Analytical Processing (continued) • Ruby Tuesday restaurant chain case • One location was performing below average • OLAP analysis showed that customers were waiting longer than normal • Appropriate changes were made • OLAP applications are usually installed on a special server • OLAP applications are usually significantly faster than relational applications Management Information Systems, Sixth Edition

  25. Management Information Systems, Sixth Edition

  26. Online Analytical Processing (continued) • OLAP is increasingly used by corporations to gain efficiencies • Office Depot used OLAP on a data warehouse to determine cross-selling strategies • Ben & Jerry’s tracks ice cream flavor popularity • BI software is becoming easier to use • Intelligent interfaces accept queries in free form • BI software is integrated into Microsoft’s SQL Server database software Management Information Systems, Sixth Edition

  27. More Customer Intelligence • A major effort of business is collecting business intelligence about customers • Data-mining and OLAP software are often integrated into CRM systems • Web has become popular for transactions, making data collection easy • Targeted marketing is more effective than mass marketing • Clickstream software: tracks and stores data about every visit to a Web site Management Information Systems, Sixth Edition

  28. Management Information Systems, Sixth Edition

  29. More Customer Intelligence (continued) • Data from customer activity on a Web site may not provide a full picture • Third-party companies such as DoubleClick and Engage Software may be hired to study consumer activity • These companies compile billions of consumer clickstreams to create behavioral models • Can determine consumers’ interests by capturing where, what, when, and how often Web pages are visited, ads are clicked, and transactions are completed Management Information Systems, Sixth Edition

  30. More Customer Intelligence (continued) • Drugstore.com: a Web-based drugstore • Wanted to reach more customers • Hired Avenue A | Razorfish Inc. to do customer profiling • Avenue A compiles anonymous information about customers continuously, and also collected and analyzed data from Drugstore.com • Discovered basic themes in shopper behavior that will help Drugstore.com determine where and how to advertise to gain new customers Management Information Systems, Sixth Edition

  31. Dashboards • Dashboard: an interface between BI tools and the user • Resembles a car dashboard • Contains visual images to quickly represent specific business metrics of interest to management • Helps management monitor revenue and sales, monitor inventory levels, and pinpoint trends and changes over time Management Information Systems, Sixth Edition

  32. Summary • Business intelligence (BI) is any information about organization, its customers, or its suppliers that can help firms make decisions • Data mining is the process of selecting, exploring, and modeling large amounts of data to discover previously unknown relationships • Data mining is useful for predicting customer behavior and detecting fraud • Online analytical processing (OLAP) puts data into two-dimensional tables Management Information Systems, Sixth Edition

  33. Summary (continued) • OLAP either uses dimensional databases or calculates desired tables on the fly • Drilling down means moving from a broad view to a specific view of information • Dashboards interface with BI software tools to provide quick information such as business metrics • Knowledge management involves gathering, organizing, sharing, analyzing, and disseminating knowledge Management Information Systems, Sixth Edition

  34. Summary (continued) • The main challenge of knowledge management is identifying and classifying useful information from unstructured sources • Most unstructured knowledge is textual • Employee knowledge networks are software tools to help employees find other employees with specific expertise • Autocategorization is the automatic classification of information Management Information Systems, Sixth Edition