1. Business Driven Technology Unit 2
Exploring Business Intelligence
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2. Unit Two Chapter Six – Valuing Organizational Information
Chapter Seven – Storing Organizational Information – Databases
Chapter Eight – Accessing Organizational Information – Data Warehouse

3. Accessing Organizational Information—Data Warehouse
Chapter 8
Accessing Organizational Information—Data Warehouse
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4. LEARNING OUTCOMES
Describe the roles and purposes of data warehouses and data marts in an organization
Identify the advantages of using business intelligence to support managerial decision making

5. 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

6. 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

7. 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. Data Warehouse Fundamentals

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

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

11. 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

12. 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

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

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

15. Information Cleansing or Scrubbing
Information cleansing activities

16. Information Cleansing or Scrubbing
Accurate and complete information

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

18. The Problem: Data Rich, Information Poor
Businesses face a data explosion with digital images, in-boxes, and broadband connections
The amount of data generated is doubling every year
Some believe it will soon double monthly

19. The Solution: Business Intelligence
Improving the quality of business decisions has a direct impact on costs and revenue
BI enables business users to receive data for analysis that is:
Reliable
Consistent
Understandable
Easily manipulated

20. The Solution: Business Intelligence
BI Can Answer Tough Questions

21. Visual Business Intelligence
Informing – Accessing large amounts of data from different management information systems
Infographics – Displays information graphically
Data visualization – Allows users to “see” or visualize data to transform information into a business perspective
Data visualization tools – Sophisticated analysis techniques such as pie charts, controls, instruments, maps, time-series graphs, and more

22. LEARNING OUTCOME REVIEW
Now that you have finished the chapter please review the learning outcomes in your text