McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 8-1 BUSINESS DRIVEN TECHNOLOGY Chapter Eight: Viewing and Protecting Organizational Information

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 8-2 LEARNING OUTCOMES 8.1 Describe the roles and purposes of data warehouses and data marts in an organization 8.2 Compare and contrast the multidimensional nature of data warehouses (and data marts) with the two-dimensional nature of databases 8.3 Summarize the importance of ensuring the cleanliness of information throughout an organization

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 8-3 LEARNING OUTCOMES 8.4 Define the relationship between backup and recovery 8.5 Illustrate the five characteristics of adaptable systems

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 8-4 CHAPTER EIGHT OVERVIEW This chapter takes a step beyond databases and discusses data warehouses and data mining It also discusses the importance of building systems that operate efficiently and effectively

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 8-5 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

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 8-6 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

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 8-7 DATA WAREHOUSE FUNDAMENTALS Start

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 8-8 DATA WAREHOUSE FUNDAMENTALS Data Warehouse Model

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 8-9 MULTIDIMENSIONAL ANALYSIS AND DATA MINING 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

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved MULTIDIMENSIONAL ANALYSIS AND DATA MINING Cube – common term for the representation of multidimensional information

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved MULTIDIMENSIONAL ANALYSIS AND 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 tools – use a variety of techniques to find patterns and relationships in large volumes of information and infer rules from them that predict future behavior and guide decision making – Include query tools, reporting tools, multidimensional analysis tools, statistical tools, and intelligent agents

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved MULTIDIMENSIONAL ANALYSIS AND DATA MINING Start

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved INFORMATION CLEANSING OR SCRUBBING An organization must maintain high-quality data in the data warehouse Information cleansing and scrubbing – a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved KEEPING BUSINESS OPERATIONS RUNNING SMOOTHLY Organizations must protect themselves from system failures and crashes Three primary steps an organization can take to protect its systems: 1.Develop an appropriate backup and recovery strategy 2.Create a disaster recovery plan 3.Build adaptable business systems

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved BACKUP AND RECOVERY STRATEGY Backup – an exact copy of a system’s information Recovery – the ability to get a system up and running in the event of a system crash or failure and includes restoring the information backup

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved DISASTER RECOVERY PLAN Disaster recovery plan – a detailed process for recovering information or an IT system in the event of a catastrophic disaster – Hot site – a separate and fully equipped facility where the company can move immediately after a disaster and resume business – Cold site – a separate facility that does not have any computer equipment, but is a place where employees can move after the disaster

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved DISASTER RECOVERY PLAN Disaster recovery cost curve charts: 1.The cost to the organization of the unavailability of information and technology 2.The cost to the organization of recovering from a disaster over time

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved BUILDING ADAPTABLE SYSTEMS Five characteristics of adaptable systems: 1. Flexibility – systems must meet all types of business changes 2. Scalability – refers to how well a system can adapt to increased demands 3. Reliability – ensures all systems are functioning correctly and providing accurate information 4. Availability – addresses when systems can be accessed by employees, customers, and partners 5. Performance – measures how quickly a system performs a certain process or transaction in terms of efficiency IT metrics of both speed and throughput

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved OPENING CASE STUDY QUESTIONS Searching for Revenue - Google 1.Determine how Google could use a data warehouse to improve its business operations 2.Explain why Google would need to scrub and cleanse the information in its data warehouse 3.Identify a data mart that Google’s marketing and sales department might use to track and analyze its AdWords revenue

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved OPENING CASE STUDY QUESTIONS Searching for Revenue - Google 4.Describe the fundamentals of a disaster recovery plan along with a recommendation for a plan for Google 5.Describe why availability and scalability are critical to Google’s business operations

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved CHAPTER EIGHT CASE Connecting Austria Almost 6 million of the 8 million people in Austria have mobile phones, of which T-Mobile Austria serves approximately 36% T-Mobile wants to build stronger relationships with its current customers and move away from its initial strategy of using a low-priced introductory rate plan to attract new customers

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved CHAPTER EIGHT CASE QUESTIONS 1.Explain how T-Mobile Austria is using information from its data warehouse to remain successful and competitive in a saturated market 2.Identify why information cleansing and scrubbing is critical to T-Mobile Austria’s data warehouse success 3.Assess the potential impacts on T-Mobile Austria’s business if it failed to create a disaster recovery plan 4.Review the five characteristics of adaptable system and rank them in order of importance to T-Mobile Austria’s business

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved BUSINESS DRIVEN TECHNOLOGY UNIT TWO CLOSING

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved Unit Closing Case One Harrah’s – Gambling Big on Technology 1.Identify the effects low-quality information might have on Harrah’s service-oriented business strategy 2.Summarize how Harrah’s uses 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.

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved Unit Closing Case One Harrah’s – Gambling Big on Technology 4.Estimate the potential impact to Harrah’s business if there is a security breach in its customer information 5.Explain the business ramifications if Harrah’s fails to implement a backup policy 6.Identify three different types of data marts Harrah’s might want to build to help it analyze its operational performance 7.Predict what might occur if Harrah’s fails to clean or scrub its information prior to loading it into its data warehouse

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved Unit Closing Case Two Evaluating Swiss Army’s Success 1.Why is accurate and reliable data considered a system benefit for Swiss Army? 2.How would unintegrated information affect Swiss Army’s ability to operate its business? 3.What types of data marts might Swiss Army choose to gain business intelligence?

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved Unit Closing Case Two Evaluating Swiss Army’s Success 4.Explain the disaster recovery cost curve and the optimal place for Swiss Army to operate on it 5.Assess the importance of backup and recovery strategies for Swiss Army