Chapter 3 and Module C

1. Chapter 3 and Module C DATABASES AND DATA WAREHOUSES Building Business Intelligence

2. MORE CHERRIES PLEASE • Ben & Jerry’s • 190,000 pints of ice cream and frozen yogurt • 50,000 grocery stores • In the U.S. and 12 other countries • Meticulously tracks every piece of information on every pint

3. MORE CHERRIES PLEASE • Noticed a problem with Cherry Garcia Ice Cream • Complaints of not enough cherries • Ben & Jerry’s could find no production problems • Eventually found that the wrong photo was on the ice cream container • Ben & Jerry’s analyzed all the information to create business intelligence

4. INTRODUCTION • Organizations need business intelligence • Business intelligence (BI) – knowledge about your customers, competitors, business partners, competitive environment, and internal operations to make effective, important, and strategic business decisions

5. INTRODUCTION • IT tools help process information to create business intelligence according to… • OLTP • OLAP

6. INTRODUCTION • Online transaction processing (OLTP) – the gathering and processing transaction information, and updating existing information to reflect the transaction • Databases support OLTP • Operational database – databases that support OLTP • Batch Processing – processing all of the transactions at once; can be used to update a database

7. INTRODUCTION • Online analytical processing (OLAP) – the manipulation of information to support decision making • Databases can support some OLAP • Data warehouses only support OLAP, not OLTP • Data warehouses are special forms of databases that support decision making

8. OLTP, OLAP, and Business Intelligence

9. THE RELATIONAL DATABASE MODEL • There are many types of databases • The relational database model is the most popular • Relational database

10. Database Characteristics Collections of information Created with logical structures Include logical ties within the information Include built-in integrity constraints

11. Database – Collection of Information

12. Database – Logical Structure • Character • Field • Record • File (Table) • Database • Data Warehouse

13. Database – Physical Structure • Bits • Bytes • Words

14. Databases – Created with Logical Structures • Databases have many tables • In databases, the row number is irrelevant; not true in spreadsheet software • In databases, column names are very important. Column names are created in the data dictionary

15. Database – Created with Logical Structures Before you can enter information into a database, you must define the data dictionary for all the tables and their fields. For example, when you create the Truck table, you must specify that it will have three pieces of information and that Date of Purchase is a field in Date format. Data dictionary – contains the logical structure for the information in a database

16. Databases – With Logical Ties Within the Information • Logical ties must exist between the tables or files in a database • Logical ties are created with primary and foreign keys • Primary key • Composite primary key • Foreign key

17. Database – Logical Ties within the Information Customer Number is the primary key for Customer and appears in Order as a foreign key

18. Databases – With Built-In Integrity Constraints • Integrity constraints – rules that help ensure the quality of the information • Examples • Primary keys must be unique • Foreign keys must be present • Sales price cannot be negative • Phone number must have area code

19. Steps in Developing a Database • Step 1: Define Entity Classes (tables) and Primary Keys • Step 2: Defining Relationships Among Entity Classes • ERD (entity relationship diagram) • Normalization • Step 3: Defining Information For Each Relation • Step 4: Use A Data Definition Language To Create Your Database

20. DATABASE MANAGEMENT SYSTEM TOOLS

21. 5 Components of a DBMS • DBMS engine • Data definition subsystem • Data manipulation subsystem • Views • Report generators • QBE tools • SQL • Application generation subsystem • Data administration subsystem

22. View Binoculars

23. Report Generator

24. Query-by-Example Tool

25. Structured Query Language SQL – standardized fourth-generation query language found in most DBMSs Sentence-structure equivalent to QBE Mostly used by IT professionals Non-procedural language, which makes it different from other programming languages

26. DATA WAREHOUSES AND DATA MINING • Data warehouses support OLAP and decision making • Data warehouses do not support OLTP • Data warehouse • Data mart • Data-mining

27. DATA WAREHOUSES AND DATA MINING

28. Data Marts

29. Data-Mining Tools

30. Data Warehouse Considerations Do you really need one, or does your database environment support all your functions? Do all employees need a big data warehouse or a smaller data mart? How up-to-date must the information be? What data-mining tools do you need?

31. INFORMATION OWNERSHIP • Information is a resource you must manage and organize to help the organization meet its goals and objectives • You need to consider • Strategic management support • Sharing information with responsibility • Information cleanliness

32. Strategic Management Support • Data administration – function that plans for, oversees the development of, and monitors the information resource • Database administration – function responsible for the more technical and operational aspects of managing organizational information

33. Sharing Information Everyone can share – while not consuming – information But someone must “own” it by accepting responsibility for its quality and accuracy

34. Information Cleanliness Related to ownership and responsibility for quality and accuracy No duplicate information No redundant records with slightly different data, such as the spelling of a customer name GIGO – if you have garbage information you get garbage information for decision making