Introduction to Information Systems

1. Introduction to Information Systems HTM 304 - Management Information Systems College of Business Administration California State University @ San Marcos • Authors: Turban, Rainer and Potter • Publisher: John Wiley & Sons, Inc. Chapter 4

2. Chapter 4 Data and Knowledge Management Chapter 4

3. Chapter Outline • 4.1 Managing Data • 4.2 The Database Approach • 4.3 Database Management Systems • 4.4 Data Warehousing • 4.5 Data Visualization • 4.6 Knowledge Management Chapter 4

4. Learning Objectives • Recognize the importance of data, issues involved in managing data and their lifecycle. • Describe the sources of data and explain how data are collected. • Explain the advantages of the database approach. • Explain the operation of data warehousing and its role in decision support. Chapter 4

5. Learning Objectives (Continued) • Understand the capabilities and benefits of data mining. • Describe data visualization. • Explain geographic information systems and virtual reality as decision support tools. • Define knowledge and describe the different types of knowledge. Chapter 4

6. 4.1 Managing Data • Difficulties of Managing Data. • Amount of data increases exponentially. • Data are scattered and collected by many individuals using various methods and devices. • Data come from many sources including internal sources, personal sources and external sources. • Data security, quality and integrity are critical. Chapter 4

7. Managing Data (Continued) • Clickstream data. Data that visitors and customers produce when they visit a Website. • An ever-increasing amount of data needs to be considered in making organizational decisions. Chapter 4

8. Data Life Cycle Chapter 4

9. Data Hierarchy • Bit (a binary digit): a circuit that is either on or off. • Byte: group of 8 bits, represents a single character. • Field: name, number, or characters that describe an aspect of a business object or activity. Chapter 4

10. Data Hierarchy (Continued) • Record: collection of related data fields. • File (or table): collection of related records. • Database: a collection of integrated and related files. Chapter 4

11. 4.2 Database Approach • Database management system (DBMS) provides all users with access to all the data. • DBMSs minimizes the following problems: • Data redundancy: the same data stored in many places. • Data isolation: applications cannot access data associated with other applications. • Data inconsistency: various copies of the data do not agree. Chapter 4

12. Database Approach (Continued) • DBMSs maximize the following issues: • Data security. • Data integrity: data meets certain constraints, no alphabetic characters in zip code field. • Data independence: applications and data are independent of one another, all applications are able to access the same data. Chapter 4

13. Designing the Database • Data model. Diagram that represents the entities in the database and their relationships. • Entity is a person, place, thing or event. • Attribute is a characteristic or quality of a particular entity. • Primary key is a field that uniquely identifies that record. • Secondary keys are fields that have identifying information but may not identify with complete accuracy. Chapter 4

14. Entity-Relationship Modeling • Database designers plan the database design in a process called entity-relationship (ER) modeling. • ER diagrams consists of entities, attributes and relationships. • Entity classes are a group of entities of a given type, i.e. STUDENT. • Instance is the representation of a particular entity, i.e. STUDENT(John Smith, 123-45-6789, …). • Identifiers are attributes unique to that entity instance, i.e. StudentIDNumber. Chapter 4

15. 4.3 Database Management Systems • Database management system (DBMS) is a set of programs that provide users with tools to add, delete, access and analyze data stored in one location. • Online transaction processing (OLTP) is when transactions are processed as soon as they occur. • Relational database model is based on the concept of two-dimensional tables. • Popular examples of relational databases are Microsoft Access and Oracle. Chapter 4

16. Query Languages • Structured query language (SQL) is the most popular query language used to request information. • Query by example (QBE) is a grid or template that a user fills out to construct a sample or description of the data wanted. Chapter 4

17. Relational Database Management Systems • Normalization is a method for analyzing and reducing a relational database to its most streamlined form for: • Mimimum redunancy; • Maximum data integrity; • Best processing performance. • Normalized data is when attributes in the table depend only on the primary key. Chapter 4

18. Virtual Databases • Software applications that provide a way of managing many different data sources as though they were all one large database. • Benefits of virtual databases include: • Lower development costs; • Faster development time; • Less maintenance; • Single point of entry into a company’s data. Chapter 4

19. 4.4 Data Warehousing • Data warehouse is a repository of historical data organized by subject to support decision makers in the organization and include: • Online analytical processing which involves the analysis of accumulated data by end users; • Multidimensional data structure which allows data to be represented in a three-dimensional matrix (or data cube). Chapter 4

20. Benefits of Data Warehousing • End users can access data quickly and easily via Web browsers because they are located in one place. • End users can conduct extensive analysis with data in ways that may not have been possible before. • End users have a consolidated view of organizational data. Chapter 4

21. Data Marts & Data Mining • Data mart is a small data warehouse, designed for the end-user needs in a strategic business unit (SBU) or a department. • Data mining involves searching for valuable business information in a large database, data warehouse, or data mart. • Used to predict trends and behaviors. • Identify previously unknown patterns. Chapter 4

22. Data Mining Applications • Retailing and sales. Predict sales, prevent theft and fraud, determine correct inventory levels and distribution schedules. • Banking. Forecast levels of bad loans, fraudulent credit card use, predict credit card spending by new customers, etc. • Manufacturing and production. Predict machinery failures, find key factors to help optimize manufacturing capacity. • Insurance. Forecast claim amounts, medical coverage costs, predict which customers will buy new insurance policies. Chapter 4

23. Data Mining Applications (Continued) • Policework. Track crime patterns, locations, criminal behavior; identify attributes to assist in solving criminal cases. • Health care. Correlate demographics of patients with critical illnesses, develop better insight to identify and treat symptoms and their causes. • Marketing. Classify customer demographics to predict how customers will respond to mailing or buy a particular product. Chapter 4

24. 4.5 Data Visualization Technologies • Geographic Information Systems (GIS) is a computer-based system for capturing, integrating, manipulating and displaying data using digitized maps. • Find locations for new restaurants. • Emerging GIS applications integrated with global positioning systems (GPSs). • Virtual Reality is interactive, computer-generated, three-dimensional graphics delivered to the user through a head-mounted display. Chapter 4

25. 4.6 Knowledge Management • Knowledge management (KM) is a process that helps organizations manipulate important knowledge that is part of the organization’s memory, usually in an unstructured format. • Knowledge is information that is contextual, relevant and actionable; information in action. • Intellectual capital (or intellectual assets) is another term often used for knowledge. Chapter 4

26. Knowledge Management (Continued) • Explicit knowledge deals with more objective, rational and technical knowledge. • Tacit knowledge is the cumulative store of subjective or experiential learning. • Knowledge management systems (KMSs) use modern information technologies – Internet, intranets, extranets, data warehouses - to systemize, enhance and expedite intrafirm and interfirm knowledge management. • Best practices are the most effective and efficient ways of doing things, readily available to a wide range of employees. Chapter 4

27. Knowledge Management System Cycle • Create knowledge. Determine new ways. • Capture knowledge. Identify as valuable. • Refine knowledge. Make it actionable. • Store knowledge. Store in a reasonable format. • Manage knowledge. Verify it is relevant, accurate. • Disseminate knowledge. Made available. Chapter 4