Using Management Information Systems David Kroenke Business Intelligence

Learning Objectives Understand the need for business intelligence systems. Know the characteristics of reporting systems. Know the purpose and role of data warehouses and data marts. Understand fundamental data-mining techniques. Know the purpose, features, and functions of knowledge management systems. 2

The Nature of Intelligence Some of the characteristics of intelligent behavior include the ability to do the following: Learn from experience including the ability to learn by trial and error Apply knowledge acquired from experience to another situation Handle complex situations Solve problems when important information is missing is the essence of decision making when dealing with uncertainty Determining what is important is a mark of a good decision maker The ability to reason and think Reacting quickly and correctly to a new situation Understand interpret visual images including processing and manipulating symbols Being creative and imaginative Using heuristics, or rules of thumb, or even guesses in making decisions 3

What is AI? Artificial Intelligence systems include people, procedures, hardware, software, data and knowledge needed to develop computer systems and machines that demonstrate characteristics of intelligence [Ralph Stair]. 4

Components of AI Expert Systems are computer programs that act or behave like a human expert in a field or area. Robotics involves developing mechanical or computer devices controlled by software to perform tasks that require a high degree of precision or are tedious or hazardous for humans Vision Systems include hardware and software that permit computers to capture, store and manipulate visual images and pictures Natural Language Processing allows the computer to understand react to statements and commands made in a "natural" language, such as English Learning Systems include hardware and software that allow the computer to change how it functions or reacts to situations based on feedback it receives Neural Networks are computer systems that act like or simulate the functioning of the human brain 5

Expert Systems l l l Expert systems are created by interviewing experts in a given business domain and codifying the rules stated by those experts. Many expert systems were created in the late 1980 s and 1990 s, and some of them have been successful. Expert systems suffer from three major disadvantages. l They are difficult and expensive to develop. l They are difficult to maintain. l They were unable to live up to the high expectations set by their name. 6

The Need for Business Intelligence Systems According to a study done at the University of California at Berkeley, a total of 403 petabytes of new data were created in 2002. 403 petabytes is roughly the amount of all printed material ever written. l l The printed collection of the Library of Congress is. 01 petabytes. 400 petabytes equals 40, 000 copies of the print collection of the Library of Congress. 7

Business Intelligence Systems The purpose of a business intelligence (BI) system is to provide the right information, to the right user, at the right time. BI systems help users accomplish their goals and objectives by producing insights that lead to actions. 8

Business Intelligence Tools for searching business data in an attempt to find patterns is called business intelligence (BI) tools. The processing of data is simple: Data are sorted and grouped and simple totals and averages are calculated. Reporting tools are used to address questions like: l l l What has happened in the past? What is the current situation? How does the current situation compare to the past? Data-mining tools process data using statistical techniques, many of which are sophisticated and mathematically complex. Data mining involves searching for patterns and relationships among data. In most cases, data-mining tools are used to make predictions. l For example, we can use one form of analysis to compute the probability that a customer will default on a loan. Data-mining tools use sophisticated techniques. 9

Data Mining Data mining is the application of statistical techniques to find patterns and relationships among data and to classify and predict. Data mining represents a convergence of disciplines. Data-mining techniques emerged from statistics and mathematics and from artificial intelligence and machine-learning fields in computer science. 10

Unsupervised Data Mining 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 and observe the results. Analysts create hypotheses after the analysis to explain the patterns found. 11

Supervised Data Mining With supervised data mining, data miners develop a model prior to the analysis and apply statistical techniques to data to estimate parameters of the model. One such analysis, which measures the impact of a set of variables on another variable, is called a regression analysis. Neural networks are another popular supervised data-mining technique used to predict values and make classifications such as “good prospect” or “poor prospect” customers. 12

Data Warehouses and Data Marts Basic reports and simple OLAP analyses can be made directly from operational data. Many organizations choose to extract operational data into facilities called data warehouses and data marts, both of which are facilities that prepare, store, and manage data specifically for data mining and other analyses. Programs read operational data and extract, clean, and prepare that data for BI processing. The prepared data are stored in a data-warehouse database using data-warehouse DBMS, which can be different from the organization’s operational DBMS. 13

Data Warehouses Versus Data Marts A data mart is a data collection, smaller than the data warehouse, that addresses a particular component or functional area of the business. The data warehouse is like the distributor in the supply chain and the data mart is like the retail store in the supply chain. Users in the data mart obtain data that pertain to a particular business function from the data warehouse. It is expensive to create, staff, and operate data warehouses and data marts. 14

Problems with Operational Data Most operational and purchased data have problems that inhibit their usefulness for business intelligence. 15

Decision Trees A decision tree is a hierarchical arrangement of criteria that predict a classification or a value. Decision tree analyses are an unsupervised datamining technique. The analyst sets up the computer program and provides the data to analyze, and the decision tree program produces the tree. 16

A Decision Tree for Loan Evaluation A common business application of decision trees is to classify loans by likelihood of default. Organizations analyze data from past loans to produce a decision tree that can be converted to loan-decision rules. l A financial institution could use such a tree to assess the default risk on a new loan. 17

Reporting Systems The purpose of a reporting system is to create meaningful information from disparate data sources and to deliver that information to the proper user on a timely basis. Reporting systems generate information from data as a result of four operations: l l Filtering data Sorting data Grouping data Making simple calculations on the data 18

Components of Reporting Systems A reporting system maintains a database of reporting metadata. The metadata describes the reports, users, groups, roles, events, and other entities involved in the reporting activity. The reporting system uses the metadata to prepare and deliver reports to the proper users on a timely basis. 19

Report Type In terms of a report type, reports can be static or dynamic. Static reports are prepared once from the underlying data, and they do not change. l Example, a report of past year’s sales Dynamic reports: the reporting system reads the most current data and generates the report using that fresh data. Examples are: a report on sales today and a report on current stock prices Query reports are prepared in response to data entered by users. Online analytical processing (OLAP) reports allow the user to dynamically change the report grouping structures. l 20

Report Media Reports are delivered via many different report media or channels. Some reports are printed on paper, and others are created in a format like PDF whereby they can be printed or viewed electronically. Other reports are delivered to computer screens. Companies sometimes place reports on internal corporate Web sites for employees to access. Another report medium is a digital dashboard, which is an electronic display customized for a particular user. l l l Vendors like Yahoo! and MSN provide common examples. Users of these services can define content they want- say, a local weather forecast, a list of stock prices, or a list of news sources. The vendor constructs the display customized for each user. 21

Digital Dashboard Example 22

RFM Analysis RFM analysis is a way of analyzing and ranking customers according to their purchasing patterns. It is a simple technique that considers how recently (R) a customer has ordered, how frequently (F) a customer orders, and 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. In a common form of this analysis, the program then divides the customers into five groups and gives customers in each group a score of 1 to 5. l The top 20% of the customers having the most recent orders are given an R score 1 (highest). The program then re-sorts the customers on the basis of how frequently they order. l The top 20% of the customers who order most frequently are given a F score of 1 (highest). Finally the program sorts the customers again according to the amount spent on their orders. l The 20% who have ordered the most expensive items are given an M score of 1 (highest). 23

Online Analytical Processing Online analytical processing (OLAP) provides the ability to sum, count, average, and perform other simple arithmetic operations on groups of data. The remarkable characteristics of OLAP reports is that they are dynamic. The viewer of the report can change the report’s format, hence, the term online. An OLAP report has measures and dimensions. A measure is the data item of interest. l 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. With an OLAP report, it is possible to drill down into the data. l l This term means to further divide the data into more detail. 24

Market-Basket Analysis A market-basket analysis is a data-mining technique for determining sales patterns. A market-basket analysis shows the products that customers tend to buy together. In market-basket terminology, support is the probability that two items will be purchased together. You can expect market-basket analysis to become a standard CRM analysis during your career. 25

Knowledge Management Knowledge management systems concern the sharing of knowledge that is already known to exist, either in libraries of documents, in the heads of employees, or in other known sources. Knowledge management (KM) is the process of creating value from intellectual capital and sharing that knowledge with employees, managers, suppliers, customers, and others who need that capital. Knowledge management is a process that is supported by the five components of an information system. l Its emphasis is on people, their knowledge, and effective means for sharing that knowledge with others. The benefits of KM concern the application of knowledge to enable employees and others to leverage organizational knowledge to work smarter. KM preserves organizational memory by capturing and storing the lessons learned and best practices of key employees. 26

Content Management Systems Content management systems are information systems that track organizational documents, Web pages, graphics, and related materials. KM content management systems are concerned with the creation, management, and delivery of documents that exist for the purpose of imparting knowledge. 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. The basic functions of content management systems are the same as for report management systems: author, manage, and deliver. The only requirement that content managers place on document authoring is that the document has been created in a standardized format. 27

Content Delivery Almost all users of content management systems pull the contents. Users cannot pull content if they do not know it exists. l 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. Web browsers and other programs can readily format content expressed in HTML, PDF, or another standard format. XML documents often contain their own formatting rules that browsers can interpret. l l The content management system will have to determine an appropriate format for content expressed in other ways. 28

KM Systems to Facilitate the Sharing of Human Knowledge 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 also with the sharing of knowledge among humans. How can one person share her knowledge with another? l How can one person learn of another person’s great idea? Three forms of technology are used for knowledge- sharing among humans: l l Portals, discussion groups, and email Collaborations systems Expert systems 29

Ethics Guide–The Ethics of Classification is a useful human skill. Sorting and classifying are necessary, important, and essential activities. l But those activities can also be dangerous Serious ethical issues arise when we classify people. l l l What makes someone a good or bad “prospect”? If we’re talking about classifying customers in order to prioritize our sales calls, then the ethical issue may not be too serious. What about classifying applicants for college? 30