Chapter 7: Databases and Data Warehouses

1. Chapter 7: Databases and Data Warehouses Oz (5th edition)

2. Ideas From the First Part of Chapter 7 • Problems with the traditional file approach (pre database) • Data redundancy • Data integrity • Data security • Program data dependence • Programmers are required to access data • Advantages of the database approach • Reductions in data redundancy • Application-data independence • Better control; better security • Flexibility

3. More Ideas • Object-oriented database model • Successor to the relational model • Integration of data and programs • Handles wider variety of field types • Entity-relationship (ER) diagrams • Graphical method of displaying relationships between tables • An ER diagram is an example of a schema (conceptual model of the database) • Tool for IS professionals

4. CREATING A DATABASE ENVIRONMENT An Entity-Relationship Diagram

5. Physical versus Logical Views • In managing information, physical deals with the structure of information as it resides on various storage media. • Logical deals with how knowledge workers view their information needs, and includes such terms as: • CHARACTER - our smallest unit of information. • FIELD - group of related characters. • RECORD - group of related fields. • FILE - group of related records. • DATABASE - group of logically associated files. • DATA WAREHOUSE - information from many databases.

6. Other Logical Structures in a Database • DATA DICTIONARY - contains the logical structure of information in a database. • Definitions of all fields, records, and tables • Relationships between tables • Who is responsible for maintaining data in the database • Descriptions of who is authorized to access different parts of the database • Data dictionary contains meta data (data about the data)

7. Sample Data Dictionary Report

8. Components of a DBMS • Data definition subsystem (language; DDL) • Defines the structure of the database tables (design view in Access) • Creates and maintains the data dictionary • Defines the relationships between tables • Add, delete, or modify field properties

9. More Components of a DBMS • Data manipulation subsystem (language; DML) • Add, delete, and modify data in the database • Contains the query languages (QBE or SQL) for the database. SQL is both a DML and DDL • Contains report generation capability • Data administration subsystem • Manage the overall database environment by providing facilities for: • Backup and recovery • Security management

10. Data Warehouse • Definition- a database with tools that stores current and historical data that is designed to support business analysis activities and decision-making tasks of managers; typically a relational database model is used • Benefits • improved access • improved information • isolation from operational systems • tools permit advanced data analysis • Users • Data marts

11. Building a Data Warehouse (ETL) • Extraction phase – create files on the computer that will store the data warehouse and move transaction data to this machine; data may come from many sources or parts of the organization • Transformation phase – cleanse and standardize the data. Why is this necessary? • Load phase – transfer the data from the transformation phase into the data warehouse • The ETL process becomes automated to make regular transfers of transaction data into the data warehouse

12. Operational Data Data is on many systems Current operational data Inconsistent data definitions Functionally organized data Data are constantly changing Support OLTP Warehouse Data Integrated in one enterprise-wide system Recent and historical data Consistent data definitions Data are organized around business entities Data are stabilized Support OLAP Comparison of Data in a Data Warehouse and Operational Data

13. Data-Mining and Data-Mining Tools • Data-mining is the process of selecting, exploring, and modeling large amounts of data to discover previously unknown relationships that support decision making. • Traditional data mining tools answer questions about variables that we think are related • Query languages (QBE or SQL) • Report generators • Multidimensional analysis tools (OLAP or pivot tables) • Standard statistical procedures (regression, ANOVA) • Knowledge discovery tools are data-mining tools for finding relationships that are not discernable to the human eye (see next slide);

14. Figure 8.22 Potential applications of data-mining Data-Mining as Knowledge Discovery:Selected Examples

15. Multidimensionality • Multidimensional data analysis (or OLAP) enables users to view data using various dimensions, measures and time frames (i. e., OLAP) • dimensions: products, business units, country, industry (e.g., categories) • measures: money, unit sales, head count, variances • time: daily, weekly, monthly, quarterly, yearly) • This type of analysis also provides the ability to view data in different ways (tables, charts, 3-D, geographically) • OLAP tools provide for this • Pivot tables in Excel or Access

16. Examples of OLAP Tools • Go to www.fedscope.opm.gov • Under data cubes on entry page click on employment • Demonstrate drill down and adding charts • Data for this example comes from the Central Personnel Data File (CPDF) of the federal government • The OLAP tool used to build this site is from a company named Cognos (PowerPlay) • OLAP tools based on Excel • http://wLCubed.com • http://www.cubularity.com

17. Multidimensionality

18. Database Architecture: The Physical and Logical Layout of the Hardware, Data, and Applications • Centralized databases with remote access • Distributed Databases • With replication a full copy of the entire database is stored at all sites • With fragmentation the database is partitioned • Parts of database are stored where they are most often accessed

19. Web Databases • The ease of use of Web browsers enables firms to link their databases to the Web • Ease of use enables users to • Access and retrieve information from a database • Enter information into the database • The user requires no special training in a DBMS to perform the above activities; prior to the browser and the Web this would not have been feasible • What does this mean?

20. Federal Trade Commission’s Fair Information Practice Principles* (1973) • Notice/awareness – disclosure of practices before collecting data • Choice/consent – opt in/opt out for consumers • Access/participation – consumers can review and contest data for accuracy and completeness • Security – data collectors must take steps to secure data for accuracy and unauthorized use • Enforcement – there must be a mechanism in place to enforce FIP principles • *Laws enforce these principles for data collected by federal agencies; not so in the private sector

21. Spreadsheets Versus DBMS • Linkage between elements • spreadsheet - between cells in same table • DBMS - between elements in different tables • Orientation • spreadsheet is toward calculations • DBMS is tilted toward organization and linkage of data elements in different tables • Capabilities • DBMS has extensive querying and reporting power • spreadsheet is limited • Memory requirements • entire spreadsheet table must be in memory • not true for the database table