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McGraw-Hill/Irwin ©2008 The McGraw-Hill Companies, All Rights Reserved DATABASES AND DATA WAREHOUSES Opening Case Searching for Revenue - Google DATABASES.

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Presentation on theme: "McGraw-Hill/Irwin ©2008 The McGraw-Hill Companies, All Rights Reserved DATABASES AND DATA WAREHOUSES Opening Case Searching for Revenue - Google DATABASES."— Presentation transcript:

1 McGraw-Hill/Irwin ©2008 The McGraw-Hill Companies, All Rights Reserved DATABASES AND DATA WAREHOUSES Opening Case Searching for Revenue - Google DATABASES AND DATA WAREHOUSES Opening Case Searching for Revenue - Google CHAPTER 6

2 6-2 Chapter Six Overview SECTION 6.1 – DATABASE FUNDAMENTALS –Understanding Information –Database Fundamentals –Database Advantages –Relational Database Fundamentals –Database Management Systems –Integrating Data Among Multiple Databases SECTION 6.2 – DATA WARAEHOUSE FUNDAMENTALS –Accessing Organizational Information –History of Data Warehousing –Data Warehouse Fundamentals –Business Intelligence –Data Mining

3 McGraw-Hill/Irwin ©2008 The McGraw-Hill Companies, All Rights Reserved SECTION 6.1 DATABASE FUNDAMENTALS

4 6-4 UNDERSTANDING INFORMATION Information is everywhere in an organization Employees must be able to obtain and analyze the many different levels, formats, and granularities of organizational information to make decisions Successfully collecting, compiling, sorting, and analyzing information can provide tremendous insight into how an organization is performing

5 6-5 UNDERSTANDING INFORMATION Information granularity – refers to the extent of detail within the information (fine and detailed or coarse and abstract) –Levels –Formats –Granularities

6 6-6 Information Quality Business decisions are only as good as the quality of the information used to make the decisions Characteristics of high quality information include: –Accuracy –Completeness –Consistency –Uniqueness –Timeliness

7 6-7 Information Quality Low quality information example

8 6-8 Understanding the Costs of Poor Information The four primary sources of low quality information include: 1.Online customers intentionally enter inaccurate information to protect their privacy 2.Information from different systems have different entry standards and formats 3.Call center operators enter abbreviated or erroneous information by accident or to save time 4.Third party and external information contains inconsistencies, inaccuracies, and errors

9 6-9 Understanding the Costs of Poor Information Potential business effects resulting from low quality information include: –Inability to accurately track customers –Difficulty identifying valuable customers –Inability to identify selling opportunities –Marketing to nonexistent customers –Difficulty tracking revenue due to inaccurate invoices –Inability to build strong customer relationships

10 6-10 Understanding the Benefits of Good Information High quality information can significantly improve the chances of making a good decision Good decisions can directly impact an organization's bottom line

11 6-11 DATABASE FUNDAMENTALS Information is everywhere in an organization Information is stored in databases –Database – maintains information about various types of objects (inventory), events (transactions), people (employees), and places (warehouses)

12 6-12 DATABASE FUNDAMENTALS Database models include: –Hierarchical database model – information is organized into a tree-like structure (using parent/child relationships) in such a way that it cannot have too many relationships –Network database model – a flexible way of representing objects and their relationships –Relational database model – stores information in the form of logically related two-dimensional tables

13 6-13 DATABASE ADVANTAGES Database advantages from a business perspective include –Increased flexibility –Increased scalability and performance –Reduced information redundancy –Increased information integrity (quality) –Increased information security

14 6-14 Increased Flexibility A well-designed database should: –Handle changes quickly and easily –Provide users with different views –Have only one physical view Physical view – deals with the physical storage of information on a storage device –Have multiple logical views Logical view – focuses on how users logically access information

15 6-15 Increased Scalability and Performance A database must scale to meet increased demand, while maintaining acceptable performance levels –Scalability – refers to how well a system can adapt to increased demands –Performance – measures how quickly a system performs a certain process or transaction

16 6-16 Reduced Redundancy Databases reduce information redundancy –Redundancy – the duplication of information or storing the same information in multiple places Inconsistency is one of the primary problems with redundant information

17 6-17 Increased Integrity (Quality) Information integrity – measures the quality of information Integrity constraint – rules that help ensure the quality of information –Relational integrity constraint – rule that enforces basic and fundamental information-based constraints –Business-critical integrity constraint – rule that enforce business rules vital to an organization’s success and often require more insight and knowledge than relational integrity constraints

18 6-18 Increased Security Information is an organizational asset and must be protected Databases offer several security features including: –Password – provides authentication of the user –Access level – determines who has access to the different types of information –Access control – determines types of user access, such as read-only access

19 6-19 Entity – a person, place, thing, transaction, or event about which information is stored –The rows in each table contain the entities –In Figure 6.5 CUSTOMER includes Dave’s Sub Shop and Pizza Palace entities Entity class (table) – a collection of similar entities –In Figure 6.5 CUSTOMER, ORDER, ORDER LINE, DISTRIBUTOR, and PRODUCT entity classes RELATIONAL DATABASE FUNDAMENTALS

20 6-20 Attributes (fields, columns) – characteristics or properties of an entity class –The columns in each table contain the attributes –In Figure 6.5 attributes for CUSTOMER include: Customer ID Customer Name Contact Name Phone RELATIONAL DATABASE FUNDAMENTALS

21 6-21 Primary keys and foreign keys identify the various entity classes (tables) in the database –Primary key – a field (or group of fields) that uniquely identifies a given entity in a table –Foreign key – a primary key of one table that appears an attribute in another table and acts to provide a logical relationship among the two tables RELATIONAL DATABASE FUNDAMENTALS

22 6-22 Potential relational database for Coca- Cola

23 6-23 DATABASE MANAGEMENT SYSTEMS Database management systems (DBMS) – software through which users and application programs interact with a database

24 6-24 DATABASE MANAGEMENT SYSTEMS Four components of a DBMS

25 6-25 Data Definition Component Data definition component – creates and maintains the data dictionary and the structure of the database The data definition component includes the data dictionary –Data dictionary – a file that stores definitions of information types, identifies the primary and foreign keys, and maintains the relationships among the tables

26 6-26 Data Definition Component Data dictionary essentially defines the logical properties of the information that the database contains

27 6-27 Data Manipulation Component Data manipulation component – allows users to create, read, update, and delete information in a database A DBMS contains several data manipulation tools: –View – allows users to see, change, sort, and query the database content –Report generator – users can define report formats –Query-by-example (QBE) – users can graphically design the answers to specific questions –Structured query language (SQL) – query language

28 6-28 Data Manipulation Component Sample report using Microsoft Access Report Generator

29 6-29 Data Manipulation Component Sample report using Access Query-By-Example (QBE) tool

30 6-30 Data Manipulation Component Results from the query in Figure 6.10

31 6-31 Data Manipulation Component SQL version of the QBE Query in Figure 6.10

32 6-32 Application Generation and Data Administration Components Application generation component – includes tools for creating visually appealing and easy-to- use applications Data administration component – provides tools for managing the overall database environment by providing faculties for backup, recovery, security, and performance IT specialists primarily use these components

33 6-33 INTEGRATING DATA AMONG MULTIPLE DATABASES Integration – allows separate systems to communicate directly with each other –Forward integration – takes information entered into a given system and sends it automatically to all downstream systems and processes –Backward integration – takes information entered into a given system and sends it automatically to all upstream systems and processes

34 6-34 INTEGRATING DATA AMONG MULTIPLE DATABASES Forward and backward integration

35 6-35 INTEGRATING DATA AMONG MULTIPLE DATABASES Building a central repository specifically for integrated information

36 McGraw-Hill/Irwin ©2008 The McGraw-Hill Companies, All Rights Reserved SECTION 6.2 DATA WAREHOUSE FUNDAMENTALS

37 6-37 HISTORY OF DATA WAREHOUSING Data warehouses extend the transformation of data into information In the 1990’s executives became less concerned with the day-to-day business operations and more concerned with overall business functions The data warehouse provided the ability to support decision making without disrupting the day-to-day operations

38 6-38 DATA WAREHOUSE FUNDAMENTALS Data warehouse – a logical collection of information – gathered from many different operational databases – that supports business analysis activities and decision-making tasks The primary purpose of a data warehouse is to aggregate information throughout an organization into a single repository for decision-making purposes

39 6-39 DATA WAREHOUSE FUNDAMENTALS Extraction, transformation, and loading (ETL) – a process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse Data mart – contains a subset of data warehouse information


41 6-41 Multidimensional Analysis Databases contain information in a series of two-dimensional tables In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows –Dimension – a particular attribute of information

42 6-42 Multidimensional Analysis Cube – common term for the representation of multidimensional information

43 6-43 Multidimensional Analysis Data mining – the process of analyzing data to extract information not offered by the raw data alone To perform data mining users need data-mining tools –Data-mining tool – uses a variety of techniques to find patterns and relationships in large volumes of information and infers rules that predict future behavior and guide decision making

44 6-44 Information Cleansing or Scrubbing An organization must maintain high- quality data in the data warehouse Information cleansing or scrubbing – a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information

45 6-45 Information Cleansing or Scrubbing Contact information in an operational system

46 6-46 Information Cleansing or Scrubbing Standardizing Customer name from Operational Systems

47 6-47 Information Cleansing or Scrubbing

48 6-48 Information Cleansing or Scrubbing Accurate and complete information

49 6-49 BUSINESS INTELLIGENCE Business intelligence – information that people use to support their decision- making efforts Principle BI enablers include: –Technology –People –Culture

50 6-50 DATA MINING Data-mining software includes many forms of AI such as neural networks and expert systems

51 6-51 DATA MINING Common forms of data-mining analysis capabilities include: –Cluster analysis –Association detection –Statistical analysis

52 6-52 Cluster Analysis Cluster analysis – a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible CRM systems depend on cluster analysis to segment customer information and identify behavioral traits

53 6-53 Association Detection Association detection – reveals the degree to which variables are related and the nature and frequency of these relationships in the information –Market basket analysis – analyzes such items as Web sites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services

54 6-54 Statistical Analysis Statistical analysis – performs such functions as information correlations, distributions, calculations, and variance analysis –Forecast – predictions made on the basis of time-series information –Time-series information – time-stamped information collected at a particular frequency

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