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Chapter 8 Accessing Organizational Information – Data Warehouse McGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, All Rights Reserved Learning Outcomes 8.1 Describe the roles and purposes of data warehouses and data marts in an organization 8.2 Compare the multidimensional nature of data warehouses (and data marts) with the two-dimensional nature of databases 8-2 Learning Outcomes 8.3 Identify the importance of ensuring the cleanliness of information throughout an organization 8.4 Explain the relationship between business intelligence and a data warehouse 8-3 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 8-4 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 8-5 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 8-6 Data Warehouse Fundamentals 8-7 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 8-8 Multidimensional Analysis and Data Mining • Cube – common term for the representation of multidimensional information 8-9 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 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 8-10 Information Cleansing or Scrubbing • An organization must maintain highquality data in the data warehouse • Information cleansing or scrubbing – a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information 8-11 Information Cleansing or Scrubbing • Contact information in an operational system 8-12 Information Cleansing or Scrubbing • Standardizing Customer name from Operational Systems 8-13 Information Cleansing or Scrubbing • Information cleansing activities 8-14 Information Cleansing or Scrubbing • Accurate and complete information 8-15 Business Intelligence • Business intelligence – information that people use to support their decisionmaking efforts • Principle BI enablers include: – Technology – People – Culture 8-16 OPENING CASE STUDY QUESTIONS It Takes A Village to Write an Encyclopedia 1. Determine how Wikipedia could use a data warehouse to improve its business operations 2. Explain why Wikipedia must cleanse or scrub the information in its data warehouse 3. Explain how a company could use information from Wikipedia to gain business intelligence 8-17 CHAPTER EIGHT CASE Mining the Data Warehouse • According to a Merrill Lynch survey in 2006, business intelligence software and data-mining tools were at the top of the technology spending list of CIOs • Ben & Jerry’s, California Pizza Kitchen, and Noodles & Company are using business intelligence and data mining in new and exciting ways 8-18 Chapter Eight Case Questions 1. Explain how Ben & Jerry’s is using business intelligence tools to remain successful and competitive in a saturated market 2. Identify why information cleansing and scrubbing is critical to California Pizza Kitchen’s business intelligence tool’s success 8-19 Chapter Eight Case Questions 3. Illustrate why 100 percent accurate and complete information is impossible for Noodles & Company to obtain 4. Describe how each of the companies above is using BI from their data warehouse to gain a competitive advantage 8-20 BUSINESS DRIVEN TECHNOLOGY UNIT TWO CLOSING McGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, All Rights Reserved UNIT CLOSING CASE ONE Harrah’s – Gambling Big on Technology 1. Identify the effects poor 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 8-22 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 effects if Harrah’s fails to use data-mining tools to gather business intelligence 6. Identify three different types of data marts Harrah’s might want to build to help it analyze its operational performance 8-23 UNIT CLOSING CASE ONE Harrah’s – Gambling Big on Technology 7. Predict what might occur if Harrah’s fails to clean or scrub its information before loading it into its data warehouse 8. How could Harrah’s use data mining to increase revenue? 8-24 UNIT CLOSING CASE TWO Searching for Revenue - Google 1. Determine if Google’s search results are examples of transactional information or analytical information 2. Describe the ramifications on Google’s business if the search information it presented to its customers was of low quality 3. Explain how the Web site RateMyProfessors.com solved its problem of low quality information 8-25 UNIT CLOSING CASE TWO Searching for Revenue - Google 4. Identify the different types of entity classes that might be stored in Google’s indexing database 5. Identify how Google might use a data warehouse to improve its business 6. Explain why Google would need to cleanse the information in its data warehouse 7. Identify a data mart that Google’s marketing and sales department might use to track and analyze its AdWords revenue 8-26