Using MIS 2e Chapter 9: Business Intelligence Systems David Kroenke This presentation has been modified from the original and should be downloaded from the Course Documents area in Blackboard

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? Security Guide: Semantic Security Chapter 9: Business Intelligence Systems

Q1 – Why do organizations need business intelligence? Computers gather and store enormous amounts of data, literally drowning us in data, yet starving us for information. The generation of all these data has much to do with Moore’s Law; i.e., the capacity of storage devices increases as their costs decrease. The result is that storage today is virtually unlimited and essentially free. Source: Used with permission of Peter Lyman and Hal R. Varian, University of California at Berkeley. Chapter 9: Business Intelligence Systems

Q2 – What business intelligence systems are available (intelligence tools)? Business intelligence tools search data to find meaningful information; they fall into two classifications – reporting tools and data-mining tools Reporting tools read data from a variety of sources, process that data, and produce formatted reports Use simple techniques; e.g., sorting, selecting and grouping to calculate totals and averages. Used primarily for assessment; e.g., What has happened in the past? What is the current situation? and how does the current situation compare to the past? Data-mining tools process data using statistical techniques, many of which are mathematically complex. Data mining involves searching for patterns and relationships among data. In most cases, data-mining tools are used to make predictions; e.g., what is the probability that a customer will default on a loan? Chapter 9: Business Intelligence Systems

Q2 – What business intelligence systems are available (tools versus systems)? The purpose of a business intelligence system is to provide the right information, to the right user, at the right time. A tool is a computer program An information system is a collection of hardware, software, data, procedures, and people A reporting tool can generate a report that shows a customer has canceled an important order. A reporting system, however, alerts that customer’s salesperson with this unwanted news, and does so in time for the salesperson to try to alter the customer’s decision. A data-mining tool can create an equation that computes the probability that a customer will default on a loan. A data-mining system uses that equation to enable banking personnel to assess new loan applications. Chapter 9: Business Intelligence Systems

Q3 – What are typical reporting applications (report characteristics)? Reports may be classified in different ways: Static reports are prepared once from the underlying data and do not change; ;e.g., a report of past year’s sales Dynamic reports: the reporting system reads the most current data and generates the report using that data; e.g., a report on today’s sales or current stock prices The report mode is either “push” or “pull”: A push report is sent to users according to a preset schedule; users receive the report without any activity on their part. A pull report must be requested by the user; the user goes to a Web portal or digital dashboard and clicks a link or button to cause the reporting system to produce and deliver the report. All reports can be delivered via different media including paper, e-mail alerts, Web sites and/or a digital dashboard. Chapter 9: Business Intelligence Systems

Q3 – What are typical reporting applications (basic operations) Data are recorded facts or figures. The data here are in no particular order with every sale appearing as an individual transaction; i.e., there is no grouping by customer or cumulative sales total. La Pierre, for example, appears multiple times within the list Chapter 9: Business Intelligence Systems

Q3 – What are typical reporting applications (basic operations) Information is knowledge derived from data or alternatively data presented in meaningful context. Data is converted to information as a result of four operations: Filtering (selecting) Sorting Grouping Calculating The sales data have been Sorted by Customer Name and Grouped by Orders and Purchase Amount The sales data can be subsequently filtered to show repeat customers Chapter 9: Business Intelligence Systems

Q3 – What are typical reporting applications (RFM analysis)? RFM analysis ranks customers according to purchasing patterns. It is a simple technique that considers: How recently (R) a customer has ordered How frequently (F) a customer orders, How much money (M) the customer spends per order. To produce an RFM score, the program first sorts customer purchase records by the date of their most recent (R) purchase. The program then divides the customers into five groups giving each group a score of 1 to 5. The group with the most recent orders is given an R score 1 (highest). The program then resorts the customers on the basis of frequency and creates five groups with scores of 1 to 5 And finally the program resorts customers on the basis of how much money was spent, once again creating five groups with scores of 1 to 5 Chapter 9: Business Intelligence Systems

Q3 – What are typical reporting applications (RFM analysis)? Ajax: Ordered recently and orders frequently, but does not order the most expensive goods Bloominghams: Potential problem; did not order in some time, but in the past ordered frequently for big money Caruthers: Bad customer; did not order for some time, did not order frequently and spent little money; sales staff should not “waste time”. Davidson: “OK customer” but not outstanding in any area; may be served by some type of automated system or used in training A reporting system can create different RFM reports and deliver those reports automatically; e.g., reports for specific regions can be pushed to the regional sales manager while reports for specific accounts can be pushed to the account executives. Chapter 9: Business Intelligence Systems

Q3 – What are typical reporting applications (OLAP processing) Online analytical processing (OLAP) provides the ability to sum, count, average, and perform other arithmetic operations on groups of data. OLAP reports are dynamic and are easily changed online. An OLAP report has measures and dimensions. A measure is the data item of interest. It is the item that is to be summed or averaged or otherwise processed in the OLAP report. A dimension is a characteristic of a measure. Purchase data, customer type, customer location, and sales region are all examples of dimension. OLAP reports enable the user to “drill down”; i.e. to divide the data into more detail. The OLAP cube is analogous to an Excel pivot table; OLAP output may be directed to Excel. Chapter 9: Business Intelligence Systems

Q3 – What are typical reporting applications (OLAP processing) The worksheet contains several hundred (thousand) transactions in chronological order for the year. A pivot table and/or the corresponding pivot chart analyzes the data quickly and with a minimum of effort. Chapter 9: Business Intelligence Systems

Q3 – What are typical reporting applications (OLAP processing) You can “pivot” the table, by switching the page, row, and/or column fields The quarterly field does not appear in the original data, and is calculated by Excel You can create additional worksheets with the underlying data for any row or column heading Chapter 9: Business Intelligence Systems

Q4 – What are typical 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 and machine-learning. Chapter 9: Business Intelligence Systems

Q4 – What are typical data-mining applications? Data mining techniques fall into two broad categories, unsupervised and supervised With unsupervised data mining, analysts do not create a model or hypothesis before running the analysis. Instead, they apply the data-mining technique to the data, observe the results, and create hypotheses after the analysis to explain the patterns found. One common unsupervised technique is cluster analysis. A common use for cluster analysis is to find groups of similar customers from customer order and demographic data. With supervised data mining, data miners develop a model before the analysis and apply statistical techniques to data to estimate parameters of the model. Regression analysis is a common supervised technique that measures impact of a set of independent variables on another dependent variable. Chapter 9: Business Intelligence Systems

Q4 – What are typical data-mining applications (market basket analysis)? Market-Basket Analysis is a data-mining tool for determining sales patterns. It helps businesses create cross-selling opportunities. Fig 9-12 Market-Basket Example Chapter 9: Business Intelligence Systems

Q4 – What are typical data-mining applications (decision trees)? Source: Used with permission of Insightful Corporation. Copyright © 1999-2005 Insightful Corporation. All Rights Reserved. A decision tree decision tree is a hierarchical arrangement of criteria that predict a classification or a value. Decision tree analyses are an unsupervised data-mining technique. The analyst sets up the computer program and provides the data to analyze, then the decision tree program produces the tree. In this example, organizations analyze data from past loans to produce a tree that can be converted to loan-decision rules. A financial institution could use such a tree to assess the default risk on a new loan. Reflects data from 3,485 loans, of which 28 percent defaulted PercentageOfLoanPastDue is best criterion; only 6% default when pct is less than 50% 89% of loans default when pct > than 50% but . . Default rate drops to 58% when credit score is greater than 572.6 and drops further to 42% when LTV (Loan to Collateral Value) is less than 94%. Thus: Accept loan if it is more than 50% paid Accept if loan is less than 50% paid but credit score is >572.6 and loan to value is less than 94% The financial institution will need to combine these data with additional analysis of each loan before making a final decision Chapter 9: Business Intelligence Systems

Q4a – What are other examples of data-mining (Benford’s Law)? Benford’s Law states that in a large variety of numeric sequences the probability that the first digit is 1, is not 1/9 but rather .30. The probabilities follow a logarithmic distribution and decline for each successive digit until the probability that the first digit is 9 is .046. This spreadsheet is available in the Spreadsheet Portfolio. One practical application of Benford’s law is the identification of fraudulent tax returns Chapter 9: Business Intelligence Systems

Q4a – What are other examples of data-mining? (Pareto Principal)? The so-called 80/20 rule (Pareto Principle) was first observed in 19th century Italy where 80% of the land was owned by (approximately) 20% of the people. The law of the “vital few and trivial many” pertains to a wide variety of applications. This spreadsheet is available in the Spreadsheet Portfolio. Chapter 9: Business Intelligence Systems

Q5 – What is the purpose of data warehouses and data marts? Operational data are often unsuited to more sophisticated analyses that require high-quality input for accurate and useful results. Thus many organizations extract operational data into facilities called data warehouses and data marts. The data warehouse cleans and processes operational or purchased data, and then stores the data on the “shelves” of the data warehouse. Metadata concerning the data, its source, format, assumptions, constraints, and other facts about the data is kept in a data-warehouse metadata database. A data mart is a data collection, smaller than the data warehouse, that addresses a particular component or functional area of the business. Users in the data mart obtain data that pertain to a particular business function from the warehouse. Chapter 9: Business Intelligence Systems

Q5 – What is the purpose of data warehouses and data marts (data warehouse)? The data warehouse DBMS extracts and provides data to business intelligence tools such as data mining programs Chapter 9: Business Intelligence Systems

Q5 – What is the purpose of data warehouses and data marts (data mart) A data mart is smaller than a data warehouse and addresses a particular component or functional area of an organization Chapter 9: Business Intelligence Systems

Q6 – What are typical knowledge-management applications? Knowledge Management (KM) is the sharing of knowledge that already exists, be it in libraries, documents, in the heads of employees, or elsewhere It is the process of creating value from intellectual capital and sharing that knowledge with managers, employees, suppliers, customers, and others who need that capital. In other words, someone, some where has the answer; KM seeks to get the right information to the right person at the right time so that he/she can do the job more effectively. KM is different from data mining which relies on statistical techniques to acquire information from hidden patterns KM is supported by the five components of an information system; the emphasis is on people, their knowledge, and effective means for sharing that knowledge with others. KM preserves organizational memory by storing the lessons learned and best practices of key employees. Chapter 9: Business Intelligence Systems

Q6 – What are typical knowledge-management applications? Content management systems are information systems that track organizational documents, graphics, Web pages, and related materials; they are a subset of KMS. Such systems differ from operational document systems in that they do not directly support business operations. Typical users of content management systems are companies that sell complicated products and want to share their knowledge of those products with employees and customers. Content management functions are very complicated. Most content databases are huge; some have thousands of individual documents, pages, and graphics. Documents may refer to one another or multiple documents may refer to the same product or procedure. When one of them changes, others must change as well. Document contents are perishable. Documents become obsolete and need to be altered, removed, or replaced. Multinational companies have to ensure language translations. Chapter 9: Business Intelligence Systems

Q6 – What are typical knowledge-management applications? Source: Used with permission of Tom Rizzo of Microsoft Corporation. Chapter 9: Business Intelligence Systems

Q6 – What are typical knowledge-management applications? Almost all users of content management systems pull (i.e., request) the contents. Users cannot pull content if they do not know it exists. The content must be arranged and indexed, and a facility for searching the content devised. Documents that reside behind a corporate firewall, however, are not publicly accessible and will not be reachable by Google or other search engines. Organizations must index their own proprietary documents and provide their own search capability for them. Nothing is more frustrating for a manager to contemplate than the situation in which one employee struggles with a problem that another employee knows how to solve easily. KM systems are concerned with the sharing not only of content, but with sharing of knowledge among humans. Collaboration systems include video conferencing and net presentations Human knowledge-sharing systems use portals, bulletin boards, and email to facilitate knowledge interchange. Chapter 9: Business Intelligence Systems

Q7 – How are business intelligence applications delivered? A data source is processed by a BI tool to produce an application result; a BI server delivers those results in a variety of formats Chapter 9: Business Intelligence Systems

Security Guide–Semantic Security Security is a very difficult problem, and it gets worse every year. Physical security is difficult enough: How do we know that the person (or program) that signs on as Megan Cho is really Megan Cho? Semantic security concerns the unintended release of information through a combination of reports or documents that are independently not protected. Megan works in the HR department and therefore has access to personal and private data of other employees, but not to salary. Megan is asked to determine if salary offers are inconsistent over time and/or if they vary significantly from one department to another. Megan is authorized to receive a report that shows “Salary Offer and Date” and a second report showing each “Average Salary in each Department” Chapter 9: Business Intelligence Systems

Security Guide–Semantic Security (Continued) Megan also has access to the employee directory She obtains a list of employees in each department She reads the company newsletter for June welcoming new employees which includes their department and title (receptionist, test engineer, and director of marketing programs). She examines the Salary Offer report for June and notices three offers for $35,000, $53,000, and $110,000 and infers the salaries for each new employee. Megan returns to the Average Department Salary report and the Employee directory. She finds three people in the Marketing Programs department and further that the average salary is $105,000. She concludes the other two people in marketing programs average $102,500. If she can get the hire week for either employee she can compute the individual salaries And so on . . . Chapter 9: Business Intelligence Systems

Enormous amounts of data are generated each year. Summary Enormous amounts of data are generated each year. Business intelligence (BI) tools search these increasing amounts of data for useful information. Reporting tools tend to be used for assessment and use simple calculations such as sums and averages. Data-mining tools, tend to be used for prediction and process data using sophisticated statistical and mathematical techniques. Benford’s Law and the Pareto Principle are two widely applicable data patterns. Reporting systems create meaningful information from disparate data sources and deliver that information to the proper user on a timely basis. RFM and OLAP are two examples of report applications. Chapter 9: Business Intelligence Systems

Content management is extremely complex. Summary (Continued) Decision trees are used to construct “If…Then…” rules for predicting classifications. Data warehouses and data marts are facilities that clean and store data for data mining and other analyses. Knowledge management is the process of creating value from intellectual capital and sharing that knowledge with employees, managers, suppliers, customers, and others who need that capital. Content management is extremely complex. Human knowledge-sharing systems use portals, bulletin boards, and email to facilitate knowledge interchange. Collaboration systems include net conferencing, video conferencing, and expert systems. Chapter 9: Business Intelligence Systems

Review: Select the appropriate term for each item OLAP Cube – Data mart – Pull report – Push Report – RFM Analysis –Terabyte – Semantic Security– Data Mining – Digital dashboard – Decision Tree A way of analyzing and ranking customers according to their purchasing pattern RFM analysis One thousand gigabytes Terabyte The search for relationships among data Data mining Electronic display customized for a user Digital dashboard Analogous to an Excel pivot table OLAP cube Report sent according to a preset schedule Push report Report requested by the user Pull report Hierarchical arrangement of criteria that predict a value or classification Decision Tree A data collection smaller than a data warehouse Data mart Concerned with the unintended release of protected information via a combination of reports Semantic Security Chapter 9: Business Intelligence Systems