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Business Driven Technology Unit 2
Exploring Business Intelligence Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.
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Unit Two Chapter Six – Valuing Organizational Information
Chapter Seven – Storing Organizational Information – Databases Chapter Eight – Accessing Organizational Information – Data Warehouse
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Accessing Organizational Information—Data Warehouse
Chapter 8 Accessing Organizational Information—Data Warehouse 8-3
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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
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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
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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
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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
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Data Warehouse Fundamentals
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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
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Multidimensional Analysis and Data Mining
Cube – common term for the representation of multidimensional information
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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
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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
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Information Cleansing or Scrubbing
Contact information in an operational system
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Information Cleansing or Scrubbing
Standardizing Customer name from Operational Systems
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Information Cleansing or Scrubbing
Information cleansing activities
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Information Cleansing or Scrubbing
Accurate and complete information
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Business Intelligence
Business intelligence – information that people use to support their decision-making efforts Principle BI enablers include: Technology People Culture
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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
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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
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The Solution: Business Intelligence
BI Can Answer Tough Questions
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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
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LEARNING OUTCOME REVIEW
Now that you have finished the chapter please review the learning outcomes in your text
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