1. Accessing Organizational Information—Data Warehouse
CHAPTER 8
Accessing Organizational Information—Data Warehouse

2. 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

3. 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

4. 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

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

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

7. DATA WAREHOUSE FUNDAMENTALS

8. 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

9. Multidimensional Analysis and Data Mining
Cube – common term for the representation of multidimensional information

10. 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

11. 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

12. Information Cleansing or Scrubbing
Contact information in an operational system

13. Information Cleansing or Scrubbing
Standardizing Customer name from Operational Systems

14. Information Cleansing or Scrubbing
Information cleansing activities

15. Information Cleansing or Scrubbing
Accurate and complete information

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

17. OPENING CASE STUDY QUESTIONS It Takes A Village to Write an Encyclopedia
Determine how Wikipedia could use a data warehouse to improve its business operations
Explain why Wikipedia must cleanse or scrub the information in its data warehouse
Explain how a company could use information from Wikipedia to gain business intelligence

18. 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

19. CHAPTER EIGHT CASE QUESTIONS
Explain how Ben & Jerry’s is using business intelligence tools to remain successful and competitive in a saturated market
Identify why information cleansing and scrubbing is critical to California Pizza Kitchen’s business intelligence tool’s success

20. CHAPTER EIGHT CASE QUESTIONS
Illustrate why 100 percent accurate and complete information is impossible for Noodles & Company to obtain
Describe how each of the companies above is using BI from their data warehouse to gain a competitive advantage

21. BUSINESS DRIVEN TECHNOLOGY
UNIT TWO CLOSING

22. UNIT CLOSING CASE ONE Harrah’s – Gambling Big on Technology
Identify the effects poor information might have on Harrah’s service-oriented business strategy
Summarize how Harrah’s uses database technologies to implement its service-oriented strategy
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

23. UNIT CLOSING CASE ONE Harrah’s – Gambling Big on Technology
Estimate the potential impact to Harrah’s business if there is a security breach in its customer information
Explain the business effects if Harrah’s fails to use data-mining tools to gather business intelligence
Identify three different types of data marts Harrah’s might want to build to help it analyze its operational performance

24. UNIT CLOSING CASE ONE Harrah’s – Gambling Big on Technology
Predict what might occur if Harrah’s fails to clean or scrub its information before loading it into its data warehouse
How could Harrah’s use data mining to increase revenue?

25. UNIT CLOSING CASE TWO Searching for Revenue - Google
Determine if Google’s search results are examples of transactional information or analytical information
Describe the ramifications on Google’s business if the search information it presented to its customers was of low quality
Explain how the website RateMyProfessors.com solved its problem of poor information

26. UNIT CLOSING CASE TWO Searching for Revenue - Google
Identify how Google could use a data warehouse to improve its business
Explain why Google would need to scrub and cleanse the information in its data warehouse
Identify a data mart that Google’s marketing and sales department might use to track and analyze its AdWords revenue