1. Chapter 5 Storing and Organizing Information Information Systems for Business: An Experiential Approach Second Edition France Bélanger, Craig Van Slyke, Robert E. Crossler Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-1 FreeStockPhotos.biz

2. Practice: Focusing Story 1. Identify the information elements that are part of a Facebook profile. 2. What information do you think Facebook uses to determine what friend suggestions to make? Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-2

3. Practice: Learning Activity 5.1 (p. 78) Suppose you are ordering something from Amazon.  What pieces of data does Amazon need to carry out your order? Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-3

4. Databases and DMBS  Database: organized collection of data.  Relational database: organizes data in connected two-dimensional tables; dominant type of database for business.  Database management system (DBMS): provides means for creating, maintaining, and using databases. Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-4

5. Multi-tiered Architecture Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-5 Figure 5.1 – Interaction between Applications and Databases

6. Spreadsheets vs. Databases Spreadsheets Issues  Good for analyzing and displaying information visually Database Issues  Unnecessary duplication  Inconsistent data  Difficulty in data retrieval and search  Poor data integrity  Errors Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-6  Requires more planning and designing  Good for storing and organizing information

7. Practice: Learning Activity 5.2 (p. 80) In LA 5.1, you identified a list of data Amazon would need to process an order.  Take those elements and group similar items together and name each group of elements. Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-7

8. Relational Database Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-8 Table 5.1 – Instructors Table o organizes data in connected two-dimensional tables; dominant type of database for business.

9. Relational Database Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-9 Figure 5.2 – A Small Database

10. Relational Databases - Terminology  Record: set of fields that all pertain to the same thing  Field: some characteristic of the thing  Primary key (PK): unique identifier Each table in a database has a primary key.  Composite primary key: PK made up of more than one field.  Foreign keys: fields that reference a primary key in a related table. This cross-referencing of tables is a relationship. Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-10

11. Normal Forms Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-11  Rules that govern relational database to ensure data consistency by eliminating unnecessary redundancy.  A particular row in a table can be related to at most one row in a related table. For example, in most businesses a specific order can only be related to one customer. A customer can have multiple orders (1:many)

12. Redundancy Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-12 What is Instructor #1’s last name?

13. Relationships Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-13  Relational databases store one-to-many relationships (1:many)  Many-to-many relationships also exist In these relationships, a specific row can be related to multiple rows in a related table. This is true in both directions (many:many).  Many-to-many relationships require creating a new table that links the two related tables. These are called linking or intersection tables.

14. Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-14 Figure 5.3 – Many-to-many Linking Table Example Many-to-many Relationships

15. One-to-One Relationships  Although they are more unusual, one-to-one relationships also exist.  A specific row in a table can be related to at most one row in a related table (1:1)  This is true in both directions of the relationship Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-15

16. Practice: Additional Learning Activity 5.4 (p. 93) Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-16

17. Database Diagrams Using actual data to show the structure of a database only works for very small databases. For larger databases, we use database schema diagrams.  Entity-relationship diagrams (ERD)  Database schemas Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-17

18. Database Diagrams Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-18 Figure 5.4 – Database Diagram

19. Practice: Learning Activity 5.3 (p. 86) Your campus library likely has a variety of business-related online databases. Identify and briefly describe one database for each of the related purposes.  Economic statistics  Industry information  Demographic statistics  Business-related news articles. Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-19

20. Online Databases  These databases serve a different purpose than databases we discussed earlier  Very useful for performing research  Contain pointers to sources of information  Most of these are available through libraries Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-20

21. Online Databases Examples  Career Research United States Bureau of Labor Statistics (http://www.bls.gov/data/)http://www.bls.gov/data/ www.monster.com, www.careerlink.com, www.careerbuilder.com www.monster.comwww.careerlink.com www.careerbuilder.com  M arket Research United States Census Bureau (http://www.census.gov).http://www.census.gov  Economic Forecasting Federal Reserve Economic Data (FRED) (http://research.stlouisfed.org/fred2/)http://research.stlouisfed.org/fred2/ Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-21

22. Big Data  Vast amount of data created and stored that ahs grown beyond the capabilities of traditional data processing tools and applications.  Often in terabytes or petabytes  Can be designed with traditional relational databases, or via alternative methods. Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-22

23. Big Data Challenges  How should the data be stored? Data is unstructured, unlike relational data Network-attached storage (NAS)  Uses a series of file servers that can easily expand to grow capacity and high-speed connections between them Direct-attached storage (DAS)  Keeps data more centralized for faster access time for processing; limits scalability of data size Hybrid approaches: store data with NAS; access temporary data with DAS Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-23

24. Big Data Challenges  How do data from various sources integrate with one another? Big data uses E/T/L process (extract, transform, load) Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-24

25. Big Data Challenges  How are data retrieved and disseminated? If structured data: SQL If unstructured: NoSQL (“Not only SQL”)  Greater scalability and efficiency in storing and retrieving data.  Dynamic creation of a table per query  Allows ad hoc queries Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-25

26. Reflection 1. What relationships do you see between what you learned in this chapter and what you learned in previous chapters? 2. Suppose you need to keep track of some data. How would you decide whether to use a spreadsheet or database management system to store the data? 3. What challenges do you think arise as organizations collect the amount of data necessary to conduct the analyses provided by Big Data? Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-26

27. Bélanger, Van Slyke, Crossler © Prospect Press 2016 5-27 Copyright 2016 Prospect Press All rights reserved. This material may not be copied or distributed without permission from Prospect Press.