Presentation is loading. Please wait.

Presentation is loading. Please wait.

McGraw-Hill-Ryerson ©2011 The McGraw-Hill Companies, All Rights Reserved CHAPTER 7 Opening Case: It Takes a Village to Write an Encyclopedia Opening Case:

Similar presentations


Presentation on theme: "McGraw-Hill-Ryerson ©2011 The McGraw-Hill Companies, All Rights Reserved CHAPTER 7 Opening Case: It Takes a Village to Write an Encyclopedia Opening Case:"— Presentation transcript:

1 McGraw-Hill-Ryerson ©2011 The McGraw-Hill Companies, All Rights Reserved CHAPTER 7 Opening Case: It Takes a Village to Write an Encyclopedia Opening Case: It Takes a Village to Write an Encyclopedia Databases and Data Warehouses

2 7-2 Copyright © 2011 McGraw-Hill Ryerson Limited Chapter Seven Overview SECTION 7.1 – DATABASES –Organizational Data and Information –Storing Transactional Information –Relational Database Fundamentals –Relational Database Advantages –Database Management Systems –Integrating Data Among Multiple Databases SECTION 7.2 – DATA WAREHOUSING –History of Data Warehousing –Data Warehouse Fundamentals –Business Intelligence –Operational, Tactical, and Strategic BI –Data Mining –Business Benefits of BI

3 7-3 Copyright © 2011 McGraw-Hill Ryerson Limited LEARNING OUTCOMES 1.Understand the defining value characteristics of both transactional data and analytical information, and the need for organizations to have data and information that are timely and of high quality. 2.Describe relational database fundamentals and advantages. 3.Understand how users interact with a database management system, the advantage of data-driven Web sites, and the primary methods of integrating data and information across multiple databases in organizations.

4 7-4 Copyright © 2011 McGraw-Hill Ryerson Limited LEARNING OUTCOMES 4.Describe data warehouse fundamentals and advantages. 5.Understand business intelligence, data mining, and the relationship between business intelligence and data warehousing.

5 McGraw-Hill-Ryerson ©2011 The McGraw-Hill Companies, All Rights Reserved SECTION 7.1 DATABASES

6 7-6 Copyright © 2011 McGraw-Hill Ryerson Limited ORGANIZATIONAL DATA AND INFORMATION Data are raw facts that describe the characteristics of an event. Information is data converted into a meaningful and useful context.

7 7-7 Copyright © 2011 McGraw-Hill Ryerson Limited ORGANIZATIONAL DATA AND INFORMATION Information granularity – refers to the extent of detail within the information (fine and detailed or coarse and abstract) –Levels –Formats –Granularities

8 7-8 Copyright © 2011 McGraw-Hill Ryerson Limited ORGANIZATIONAL DATA AND INFORMATION

9 7-9 Copyright © 2011 McGraw-Hill Ryerson Limited The Value of Transactional Data and Analytical Information Transactional data encompasses all of the data contained within a single business process or unit of work, and its primary purpose is to support the performing of daily operational tasks. Analytical information encompasses all organizational information, and its primary purpose is to support the performing of higher- level analysis tasks.

10 7-10 Copyright © 2011 McGraw-Hill Ryerson Limited The Value of Transactional Data and Analytical Information

11 7-11 Copyright © 2011 McGraw-Hill Ryerson Limited The Value of Timely Data and Information Real-time is immediate Real-time data Real-time information Real-time system

12 7-12 Copyright © 2011 McGraw-Hill Ryerson Limited The Value of Quality Data and Information

13 7-13 Copyright © 2011 McGraw-Hill Ryerson Limited The Value of Quality Data and Information Low-quality information example

14 7-14 Copyright © 2011 McGraw-Hill Ryerson Limited The Value of Quality Data and Information The four primary sources of low-quality information include: 1.Online customers intentionally enter inaccurate information to protect their privacy 2.Data or information from different systems have different entry standards and formats 3.Call centre operators enter abbreviated or erroneous information by accident or to save time 4.Third party and external information contains inconsistencies, inaccuracies, and errors

15 7-15 Copyright © 2011 McGraw-Hill Ryerson Limited 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

16 7-16 Copyright © 2011 McGraw-Hill Ryerson Limited 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

17 7-17 Copyright © 2011 McGraw-Hill Ryerson Limited RELATIONAL 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)

18 7-18 Copyright © 2011 McGraw-Hill Ryerson Limited RELATIONAL 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

19 7-19 Copyright © 2011 McGraw-Hill Ryerson Limited Entities, Entity Classes, and Attributes Entity – a person, place, thing, transaction, or event about which information is stored –The rows in each table contain the entities –In Figure 7.5 CUSTOMER includes Dave’s Sub Shop and Pizza Palace entities Entity class (table) – a collection of similar entities –In Figure 7.5 CUSTOMER, ORDER, ORDER LINE, DISTRIBUTOR, and PRODUCT entity classes

20 7-20 Copyright © 2011 McGraw-Hill Ryerson Limited Entities, Entity Classes, and Attributes Attributes (fields, columns) – characteristics or properties of an entity class –The columns in each table contain the attributes –In Figure 7.5 attributes for CUSTOMER include: Customer ID Customer Name Contact Name Phone

21 7-21 Copyright © 2011 McGraw-Hill Ryerson Limited Potential relational database for Coca- Cola Entities, Entity Classes, and Attributes

22 7-22 Copyright © 2011 McGraw-Hill Ryerson Limited 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 as an attribute in another table and acts to provide a logical relationship between the two tables Keys and Relationships

23 7-23 Copyright © 2011 McGraw-Hill Ryerson Limited RELATIONAL DATABASE ADVANTAGES Database advantages from a business perspective include –Increased flexibility –Increased scalability and performance –Reduced redundancy –Increased integrity (quality) –Increased security

24 7-24 Copyright © 2011 McGraw-Hill Ryerson Limited 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

25 7-25 Copyright © 2011 McGraw-Hill Ryerson Limited 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

26 7-26 Copyright © 2011 McGraw-Hill Ryerson Limited 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

27 7-27 Copyright © 2011 McGraw-Hill Ryerson Limited 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 enforces business rules vital to an organization’s success and often requires more insight and knowledge than relational integrity constraints

28 7-28 Copyright © 2011 McGraw-Hill Ryerson Limited 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

29 7-29 Copyright © 2011 McGraw-Hill Ryerson Limited DATABASE MANAGEMENT SYSTEMS Database management systems (DBMS) – software through which users and application programs interact with a database

30 7-30 Copyright © 2011 McGraw-Hill Ryerson Limited Data-Driven Web Sites Data-driven Web site – is an interactive Web site kept updated and relevant to the needs of its customers.

31 7-31 Copyright © 2011 McGraw-Hill Ryerson Limited Data-Driven Web Site Advantages

32 7-32 Copyright © 2011 McGraw-Hill Ryerson Limited Querying Data-Driven Web Sites

33 7-33 Copyright © 2011 McGraw-Hill Ryerson Limited 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 7-34 Copyright © 2011 McGraw-Hill Ryerson Limited INTEGRATING DATA AMONG MULTIPLE DATABASES Forward and backward integration

35 7-35 Copyright © 2011 McGraw-Hill Ryerson Limited INTEGRATING DATA AMONG MULTIPLE DATABASES Building a central repository specifically for integrated information

36 7-36 Copyright © 2011 McGraw-Hill Ryerson Limited OPENING CASE QUESTIONS It Takes a Village to Write an Encyclopedia 1.Determine if an entry in Wikipedia is an example of transactional information or analytical information. 2.What is the impact to Wikipedia if the information contained in its database is of low quality? 3.Review the five common characteristics of high-quality information and rank them in order of importance. 4.How is Wikipedia resolving the problem of poor information? 5.Identify the different types of entities that might be stored in Wikipedia’s database. 6.Why is database technology so important to Wikipedia’s business model?

37 McGraw-Hill-Ryerson ©2011 The McGraw-Hill Companies, All Rights Reserved SECTION 7.2 DATA WAREHOUSING

38 7-38 Copyright © 2011 McGraw-Hill Ryerson Limited 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

39 7-39 Copyright © 2011 McGraw-Hill Ryerson Limited 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

40 7-40 Copyright © 2011 McGraw-Hill Ryerson Limited 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 7-41 Copyright © 2011 McGraw-Hill Ryerson Limited DATA WAREHOUSE FUNDAMENTALS

42 7-42 Copyright © 2011 McGraw-Hill Ryerson Limited 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

43 7-43 Copyright © 2011 McGraw-Hill Ryerson Limited Multidimensional Analysis Cube – common term for the representation of multidimensional information

44 7-44 Copyright © 2011 McGraw-Hill Ryerson Limited 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 7-45 Copyright © 2011 McGraw-Hill Ryerson Limited Information Cleansing or Scrubbing Contact information in an operational system

46 7-46 Copyright © 2011 McGraw-Hill Ryerson Limited Information Cleansing or Scrubbing Standardizing Customer name from Operational Systems

47 7-47 Copyright © 2011 McGraw-Hill Ryerson Limited Information Cleansing or Scrubbing

48 7-48 Copyright © 2011 McGraw-Hill Ryerson Limited Information Cleansing or Scrubbing Accurate and complete information

49 7-49 Copyright © 2011 McGraw-Hill Ryerson Limited BUSINESS INTELLIGENCE Business intelligence – information that people use to support their decision- making efforts

50 7-50 Copyright © 2011 McGraw-Hill Ryerson Limited BUSINESS INTELLIGENCE BI information analysis

51 7-51 Copyright © 2011 McGraw-Hill Ryerson Limited BUSINESS INTELLIGENCE How BI can answer tough customer questions

52 7-52 Copyright © 2011 McGraw-Hill Ryerson Limited OPERATIONAL, TACTICAL, AND STRATEGIC BI

53 7-53 Copyright © 2011 McGraw-Hill Ryerson Limited OPERATIONAL, TACTICAL, AND STRATEGIC BI The three forms of BI must work towards a common goal

54 7-54 Copyright © 2011 McGraw-Hill Ryerson Limited BI’s Operational Value The latency between a “business event” and an “action taken”

55 7-55 Copyright © 2011 McGraw-Hill Ryerson Limited DATA MINING 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 behaviour and guide decision making

56 7-56 Copyright © 2011 McGraw-Hill Ryerson Limited DATA MINING Common forms of data-mining analysis capabilities include: –Cluster analysis –Association detection –Statistical analysis

57 7-57 Copyright © 2011 McGraw-Hill Ryerson Limited 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 behavioural traits

58 7-58 Copyright © 2011 McGraw-Hill Ryerson Limited Cluster Analysis

59 7-59 Copyright © 2011 McGraw-Hill Ryerson Limited 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 behaviour and predict future behaviour by identifying affinities among customers’ choices of products and services

60 7-60 Copyright © 2011 McGraw-Hill Ryerson Limited 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

61 7-61 Copyright © 2011 McGraw-Hill Ryerson Limited BUSINESS BENEFITS OF BI Categories of BI benefits: 1.Direct quantifiable benefits 2.Indirect quantifiable benefits 3.Unpredictable benefits 4.Intangible benefits

62 7-62 Copyright © 2011 McGraw-Hill Ryerson Limited OPENING CASE QUESTIONS It Takes a Village to Write an Encyclopedia 7.How could Wikipedia use a data warehouse to improve its business operations? 8.Why must Wikipedia cleanse or scrub the information in its data warehouse? 9.How could a company use information from Wikipedia to gain business intelligence? 10.Choose one of the three common forms of data-mining analysis and explain how Wikipedia could use it to gain BI. 11.How can Wikipedia use tactical, operational and strategic BI?

63 7-63 Copyright © 2011 McGraw-Hill Ryerson Limited CLOSING CASE ONE Scouting for Quality 1.Explain the importance of high-quality information for Scouts Canada. 2.Review the five common characteristics of high quality information and rank them in order of importance for Scouts Canada. 3.How could data warehouses and data marts be used to help Scouts Canada improve the efficiency and effectiveness of its operations?

64 7-64 Copyright © 2011 McGraw-Hill Ryerson Limited CLOSING CASE ONE Scouting for Quality 4.What kinds of data marts might Scouting Canada want to build to help it analyze its operational performance? 5.Do the managers at Scouting Canada actually have all of the information they require to make an accurate decision? Explain the statement “it is never possible to have all of the information required to make the best decision possible.”

65 7-65 Copyright © 2011 McGraw-Hill Ryerson Limited CLOSING CASE TWO Google 1.How did the Web site RateMyProfessor.com solve its problem of low-quality information? 2.Review the five common characteristics of high-quality information and rank them in order of importance to Google’s business. 3.What would be the ramifications of Google’s business if the search information it presented to its customers was of low quality? 4.Describe the different types of databases. Why should Google use a relational database? 5.Identify the different types of entities, entity classes, attributes, keys, and relationships that might be stored in Google’s AdWords relational database.

66 7-66 Copyright © 2011 McGraw-Hill Ryerson Limited CLOSING CASE TWO Google 6.How could Google use a data warehouse to improve its business operations? 7.Why would Google need to scrub and cleanse the information in its data warehouse? 8.Identify a data mart that Google’s marketing and sales department might use to track and analyze its AdWords revenue.

67 7-67 Copyright © 2011 McGraw-Hill Ryerson Limited CLOSING CASE THREE Harrah’s 1.Identify the effects poor information might have on Harrah’s service-oriented business strategy 2.How does Harrah’s use database technologies to implement its service-oriented strategy? 3.Harrah’s was one of the first casino companies to find value in offering rewards to customers who visit multiple Harrah’s locations. Describe the effects on the company if it did not build any integrations among the databases located at each of its casinos. How could Harrah’s use distributed databases or a data warehouses to synchronize customer information?

68 7-68 Copyright © 2011 McGraw-Hill Ryerson Limited CLOSING CASE THREE Harrah’s 4.Estimate the potential impact to Harrah’s business if there is a security breach in its customer information. 5.Identify three different types of data marts Harrah’s might want to build to help it analyze its operational performance.

69 7-69 Copyright © 2011 McGraw-Hill Ryerson Limited 6.What might occur if Harrah’s fails to clean or scrub its information before loading it into its data warehouse? 7.Describe cluster analysis, association detection, and statistical analysis and explain how Harrah’s could use each one to gain insights into its business. CLOSING CASE THREE Harrah’s


Download ppt "McGraw-Hill-Ryerson ©2011 The McGraw-Hill Companies, All Rights Reserved CHAPTER 7 Opening Case: It Takes a Village to Write an Encyclopedia Opening Case:"

Similar presentations


Ads by Google