1. Databases and Information Management
CHAPTER SIX
Databases and Information Management
Oleh : Kundang K Juman

2. LEARNING OBJECTIVES
After reading this chapter, you will be able to answer the following questions:
What are the problems of managing data resources in a traditional file environment, and how are they solved by a database management system?
What are the major capabilities of a database management system (DBMS), and why is a relational DBMS so powerful?
What are some important principles of database design?
continued …

3. LEARNING OBJECTIVES (continued)
What are the principal tools and technologies for accessing information from databases to improve business performance and decision making?
Why are information policy, data administration, and data quality assurance essential for managing a firm’s data resources?

4. Toronto Opens up its Data
Problem: The City of Toronto decided to open its data to the public
Solutions: Allowed public access to much of the data that had previously been kept private
Used appropriate technology to ensure proper privacy and security of data
Demonstrates importance of database management in creating open access to data
Illustrates need to ensure data safeguards are in place to ensure proper privacy
As the text says “An effective information system provides users with accurate, timely, and relevant information.” Ask students to define and explain why these three characteristics (accurate, timely, relevant) are important.

5. Organizing Data in a Traditional File Environment
File organization terms and concepts
Bit: Smallest unit of data; binary digit (0,1)
Byte: Group of bits that represents a single character
Field: Group of words or a complete number
Record: Group of related fields
File: Group of records of same type
Continued …

6. Organizing Data in a Traditional File Environment
File Organization Concepts (continued)
Database: Group of related files
Entity: Person, place, thing, event about which information is maintained
Attribute: Description of a particular entity
Key field: Identifier field used to retrieve, update, sort a record

7. Organizing Data in a Traditional File Environment

8. Organizing Data in a Traditional File Environment
Problems with the traditional file environment
Data redundancy and inconsistency
Program-data dependence
Lack of flexibility
Poor security
Lack of data sharing and availability

9. Organizing Data in a Traditional File Environment

10. Organizing Data in a Traditional File Environment
Problems with the Traditional File Environment
Data Redundancy and Inconsistency:
Data redundancy: The presence of duplicate data in multiple data files so that the same data are stored in more than one place or location
Data inconsistency: The same attribute may have different values.
Continued …

11. Organizing Data in a Traditional File Environment
Problems with the Traditional File Environment (continued)
Program-Data Dependence:
The coupling of data stored in files and the specific programs required to update and maintain those files such that changes in programs require changes to the data
Continued …

12. Organizing Data in a Traditional File Environment
Problems with the Traditional File Environment (continued)
Lack of Flexibility
A traditional file system can deliver routine scheduled reports after extensive programming efforts, but it cannot deliver ad-hoc reports or respond to unanticipated information requirements in a timely fashion
Continued …

13. Organizing Data in a Traditional File Environment
Problems with the Traditional File Environment (continued)
Poor security
Management may have no knowledge of who is accessing or making changes to the organization’s data
Lack of data sharing and availability:
Information cannot flow freely across different functional areas or different parts of the organization.

14. The Database Approach to Data Management
Database management systems
How a DBMS solves the problems of the traditional file environment
Relational DBMS
Operations of a relational DBMS
Hierarchical and network DBMS
Object-oriented DBMS

15. The Database Approach to Data Management

16. The Database Approach to Data Management
Relational DBMS
Represents data as two-dimensional tables called relations
Relates data across tables based on common data element
Examples: Access, DB2, Oracle, MS SQL Server

17. The Database Approach to Data Management

18. The Database Approach to Data Management
Operations of a Relational DBMS
Select: Creates subset of rows that meet specific criteria
Join: Combines relational tables to provide users with information
Project: Enables users to create new tables containing only relevant information

19. The Database Approach to Data Management

20. The Database Approach to Data Management
Object-oriented DBMS
Stores data and procedures as objects that can be retrieved and shared automatically
Provides capabilities of both object-oriented and relational DBMS
Hybrid OODBMS:
combine benefits of relational and object-oriented DBMS

21. The Database Approach to Data Management
Capabilities of Database Management Systems
Data Definition Language
Data Dictionary
Querying and Reporting

22. The Database Approach to Data Management

23. The Database Approach to Data Management

24. The Database Approach to Data Management

25. The Database Approach to Data Management
Designing Databases
Conceptual design: Abstract model of database from a business perspective
Physical design: Detailed description of business information needs
Entity-relationship diagram: Methodology for documenting databases illustrating relationships between database entities
Normalization: Process of creating small stable data structures from complex groups of data

26. The Database Approach to Data Management

27. The Database Approach to Data Management

28. The Database Approach to Data Management

29. The Database Approach to Data Management
Distributed database:
A database that is stored in more than one physical location
Reduce the vulnerability of a single, massive central site
Increase service and responsiveness to local users
Can often run on smaller, less expensive computers
Depend on high-quality telecommunications lines

30. Using Databases to Improve Business Performance and Decision Making
Data warehouse
Stores current and historical data from many core operational transaction systems
Consolidates and standardizes information for use across enterprise, but data cannot be altered
Data warehouse system will provide query, analysis, and reporting tools
Data marts
Subset of data warehouse
Summarized or highly focused portion of firm’s data for use by specific population of users
Typically focuses on single subject or line of business

31. Using Databases to Improve Business Performance and Decision Making

32. Using Databases to Improve Business Performance and Decision Making
Tools for Business Intelligence
Tools for consolidating, analyzing, and providing access to vast amounts of data to help users make better business decisions
Example of Best Western building customer relationships with CRM
Principle tools include:
Software for database query and reporting
Online analytical processing (OLAP)
Data mining

33. Using Databases to Improve Business Performance and Decision Making
Online analytical processing (OLAP)
Supports multidimensional data analysis
Viewing data using multiple dimensions
Each aspect of information (product, pricing, cost, region, time period) is different dimension
E.g., how many washers sold in East in June compared with other regions?
OLAP enables rapid, online answers to ad hoc queries
This slide discusses online analytical processing, one of the three principle tools for gathering business intelligence.
Ask students to come up with additional examples of what a multidimensional query might be.

34. Using Databases to Improve Business Performance and Decision Making

35. Using Databases to Improve Business Performance and Decision Making
Data Mining
Tools for analyzing large pools of data
Find hidden patterns and infer rules to predict trends
Associations
Sequences
Classifications
Clusters
Forecasts

36. Using Databases to Improve Business Performance and Decision Making
Text Mining
Extracts key elements from large unstructured data sets (e.g., stored s)

37. Using Databases to Improve Business Performance and Decision Making
Web Mining
Discovery and analysis of useful patterns and information from WWW
Techniques
Web content mining
Knowledge extracted from content of Web pages
Web structure mining
E.g., links to and from Web page
Web usage mining
User interaction data recorded by Web server

38. What can Businesses Learn from Text Mining?
Read the Window on Technology and answer the following questions:
What challenges does the increase in unstructured data present for businesses?
How does text mining improve decision making? What are the challenges involved in text mining?
What kinds of companies are most likely to benefit from text mining software? Explain your answer.
How might text mining lead to the erosion of personal information privacy? (See Chapter 4.) Explain.

39. Using Databases to Improve Business Performance and Decision Making
Databases and the Web
Many companies use Web to make some internal databases available to customers or partners
Typical configuration includes:
Web server
Application server/middleware/CGI scripts
Database server (hosting DBM)
Advantages of using Web for database access:
Ease of use of browser software
Web interface requires few or no changes to database
Inexpensive to add Web interface to system

40. Using Databases to Improve Business Performance and Decision Making

41. Managing Data Resources
Establishing an information policy
Specifies the organization’s rules for sharing, disseminating, acquiring, standardizing, classifying, and inventorying information
Data administration is responsible for specific policies and procedures through which data is managed
Data governance
Database administration

42. Managing Data Resources
Ensuring Data Quality
Data Quality Audit
Structured survey of the accuracy and completeness of data in an information system
Data cleansing
consists of activities for detecting and correcting data in an information system

43. Credit Bureau Errors – Big People Problems
Read the Window on Organizations, and then discuss the following questions:
Assess the business impact of credit bureaus’ data quality problems for the credit bureaus, for lenders, and for individuals.
Are any ethical issues raised by credit bureaus’ data quality problems? Explain your answer.
Analyze the management, organization, and technology factors responsible for credit bureaus’ data quality problems.
What can be done to solve these problems?

44. Databases and Information Management
CHAPTER SIX
Databases and Information Management