1. McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 5 Data Resource Management

2. 5-2 Learning Objectives  Explain the value of implementing data resource management processes and technologies in an organization.  Outline the advantages of a database management approach to managing the data resources of a business, compared with a file processing approach.  Explain how database management software helps business professionals and supports the operations and management of a business.

3. 5-3 Learning Objectives  Provide examples to illustrate each of the following concepts:  Major types of databases  Data warehouses and data mining  Logical data elements  Fundamental database structures  Database development

4. 5-4 Section 1 Technical Foundations of Database Management

5. 5-5 I. Database Management System  Data resources must be structured and organized in some logical manner so they can be accessed, processed, retrieved, and managed easily

6. 5-6 II. Fundamental Data Concepts  Character – the most basic logical data element that can be observed, a single alpha or numeric or other symbol, represented by one byte  Field – a grouping of related characters, as a last name or a salary, represents an attribute of some entity General Purpose Application Programs – perform common information processing jobs for end users

7. 5-7 II. Fundamental Data Concepts  Record – a grouping of attributes that describe an entity  File – a group of related data records  Database – a collection of logically related data elements

8. 5-8 II. Fundamental Data Concepts

9. 5-9 III. Database Structures (Models)  Hierarchical Structure – treelike structure of one- to-many parent-child relationships (each child can have only one parent)  Network Structure – similar to hierarchical but allows many-to-many relationships (a child record can have more than one parent)  Relational Structure – the most widely used database model today; data is represented as a series of two-dimensional tables called Relations; each column is a named attribute of the entity, each row is an unnamed instance of that entity

10. 5-10 III. Database Structures (Models)  Relational Operations  Select – create a subset that meets a criterion  Join – temporarily combine two or more tables for comparison  Project – create a subset of the columns in the temporary tables  Multidimensional Structure – variation of the Relational model using multidimensional structures to organize and express relationships

11. 5-11 III. Database Structures (Models)

12. 5-12 III. Database Structures (Models)

13. 5-13 III. Database Structures (Models)

14. 5-14 III. Database Structures (Models)  Object-Oriented Structure – combining the data of interest and the processes that act on that data into a structure called an object  Encapsulation – allows complex data types  Inheritance – allows replication of some/all of the characteristics of a parent object in the creation of a child object  Evaluation of Database Structures – databases have grown more complex as needs have changed and grown in complexity

15. 5-15 III. Database Structures (Models)

16. 5-16 Database Pioneer Rethinks the Best Way to Organize Data  How do databases currently focus on data to be stored?  What is suggested as a better away to focus on data warehouses?  What gains do these changes promise?

17. 5-17 IV. Database Development  Database Administrator (DBA) – controls development and administration of the database  Data Definition Language (DDL) – used to specify the contents, relationships, and structure of the database  Data Dictionary – directory containing the metadata

18. 5-18 IV. Database Development  Metadata – data about the data  Data Planning and Database Design  Data Modeling (Entity-Relationship Diagrams) – logical models of the data itself; this must be done before choosing the database model  Schema – the physical/internal view of a system  Subschema – the logical/external view of a system

19. 5-19 IV. Database Development

20. 5-20 IV. Database Development Entity Relationship Diagram

21. 5-21 AAA Missouri: Data Quality Is an Important First Step  What problem did AAA Missouri have?  How does Melissa solve this problem?  What other benefits does this software provide?  What is the ultimate goal of using this software? What business benefit will that provide?

22. 5-22 Hadoop: Ready for the Large-scale Databases of the Future  What is the strength of Hadoop?  What does Hadoop do differently from pervious databases?  Why do we need something different today for handling data in databases?  What does this tell you about the future od handling data?

23. 5-23 Section 2 Managing Data Resources

24. 5-24 I. Data Resource Management  Data are an organizational resource that must be managed as any other resource

25. 5-25 I. Data Resource Management Types of Databases Used by Organizations and End-Users

26. 5-26 II. Types of Databases  Operational Databases – store detailed data to support business processes and operations  Distributed Databases – many organizations distribute their databases over multiple locations  Replication – complex process of updating distributed data  Duplication – simplified method of updating distributed data

27. 5-27 II. Types of Databases  External Databases – outside the firm, free or fee-based  Hypermedia Databases – hyperlinked pages of multimedia

28. 5-28 Coty: Using Real-Time Analytics to Track Demand  What percentage of retails products are usually out of stock in the U.S.?  What percentage of promotional items are usually out of stock in the U.S.?  What effect does this have on business?  How does Coty deal with these issues?

29. 5-29 III. Data Warehouses and Data Mining  Data Warehouse – stores data extracted from other databases  Data Mart – subset of a data warehouse focusing on a single topic, customer, product, etc.  Data Mining – analyzing a data warehouse to reveal hidden patterns and trends

30. 5-30 III. Data Warehouses and Data Mining Components of a Data Warehouse System

31. 5-31 III. Data Warehouses and Data Mining A Data Warehouse and its Data Mart Subsets

32. 5-32 III. Data Warehouses and Data Mining Data Mining Extracts Business Knowledge from a Data Warehouse

33. 5-33 Better Analytics Means Better Care  According to this case, what is the state of healthcare and BI?  In what ways did the system improve patients’ health?  How does SETMA view the cost and benefits of the system?

34. 5-34 IV. Traditional File Processing  Data was stored in independent files without regard to other needs for that data  Problems of File Processing – databases seek to solve these problems  1. Data Redundancy – the same data is kept in more than one location; databases seek to Control (NOT reduce!) Redundancy; this led to Data Inconsistency – same data in multiple locations but the Values were Different

35. 5-35 IV. Traditional File Processing  Problems of File Processing – databases seek to solve these problems  2. Lack of data Integration – data not easily available for ad hoc requests  3. Data Dependence – data and programs were “tightly coupled”, changing one meant having to change the other  4. Lack of Data Integrity (Standardization) – data was defined differently by different end users or applications

36. 5-36 Online Dating: The Technology Behind Finding Love  Are all dating sites the same? For users, what makes the difference between different dating sites?  What is the biggest challenge for eHarmony?  When is the demand for eHarmony’s services greatest? Why might this be? What does this mean from a business perspective?

37. 5-37 V. Database Management Approach  Consolidate the data from separate files into databases accessible by multiple application programs  Database Management System (DBMS) – a collection of programs to create, maintain, and use (retrieve) data in a database  Database Maintenance – organizational databases need to be updated continually  Application Development – facilitated by the Data Manipulation Language (DML) provided by the DBMS

38. 5-38 V. Database Management Approach  Database Interrogation – query (“ask”) the database for information  Query Language – allows ad hoc requests of the database  SQL Queries (Structured Query Language) – standard query language found in many databases  Boolean Logic – 3 logical operators: AND, OR, and NOT  Graphical and Natural Queries – easier methods of structuring SQL statements