Accessing Organizational Information—Data Warehouse

Slides:



Advertisements
Similar presentations
Mining the data warehouse
Advertisements

McGraw-Hill/Irwin ©2005 The McGraw-Hill Companies, All rights reserved ©2005 The McGraw-Hill Companies, All rights reserved McGraw-Hill/Irwin.
5.1 © 2007 by Prentice Hall 5 Chapter Foundations of Business Intelligence: Databases and Information Management.
By: Mr Hashem Alaidaros MIS 211 Lecture 4 Title: Data Base Management System.
Storing Organizational Information—Databases
DATABASES AND DATA WAREHOUSES Searching for Revenue - Google
McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 8-1 BUSINESS DRIVEN TECHNOLOGY Chapter Eight: Viewing and Protecting Organizational.
Opening Case: It Takes a Village to Write an Encyclopedia
Business Driven Information Systems 2e
Database – Part 3 Dr. V.T. Raja Oregon State University External References/Sources: Data Warehousing – Mr. Sakthi Angappamudali.
Introduction to Data Warehouse and Data Mining MIS 2502 Data Analytics
Database and Data Warehouse
Database – Part 2b Dr. V.T. Raja Oregon State University External References/Sources: Data Warehousing – Sakthi Angappamudali at Standard Insurance; BI.
McGraw-Hill/Irwin ©2008 The McGraw-Hill Companies, All Rights Reserved DATABASES AND DATA WAREHOUSES Opening Case Searching for Revenue - Google DATABASES.
Chapter 3 Databases and Data Warehouses: Building Business Intelligence Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.
McGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, All Rights Reserved Chapter 7 Storing Organizational Information - Databases.
Business Driven Technology Unit 2
BUSINESS DRIVEN TECHNOLOGY
Data Resource Management Data Concepts Database Management Types of Databases Chapter 5 McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies,
Business Intelligence
TOPIC 1: GAINING COMPETITIVE ADVANTAGE WITH IT (CONTINUE) SUPPLY CHAIN MANAGEMENT & BUSINESS INTELLIGENCE.
CHAPTER 08 Accessing Organizational Information – Data Warehouse
XP Information Information is everywhere in an organization Employees must be able to obtain and analyze the many different levels, formats, and granularities.
McGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, All Rights Reserved Chapter 8 Accessing Organizational Information – Data Warehouse.
CIS 429—Chapter 8 Accessing Organizational Information—Data Warehouse.
5.1 © 2007 by Prentice Hall 5 Chapter Foundations of Business Intelligence: Databases and Information Management.
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 1 Chapter 9 Competitive Advantage with Information Systems for Decision Making.
CHAPTER SIX DATA Business Intelligence
Chapter 6: Foundations of Business Intelligence - Databases and Information Management Dr. Andrew P. Ciganek, Ph.D.
@ ?!.
McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 3 Databases and Data Warehouses: Supporting the Analytics-Driven.
Management Information Systems MANAGING THE DIGITAL FIRM, 12 TH EDITION GLOBAL EDITION FOUNDATIONS OF BUSINESS INTELLIGENCE ENHANCING DECISION MAKING Lecture.
Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin CHAPTER SIX DATA: BUSINESS INTELLIGENCE.
McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 6-1 BUSINESS DRIVEN TECHNOLOGY UNIT 2: Managing Information for Business.
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Business Plug-In B18 Business Intelligence.
Lecturer: Gareth Jones. How does a relational database organise data? What are the principles of a database management system? What are the principal.
BUS1MIS Management Information Systems Semester 1, 2012 Week 6 Lecture 1.
BUSINESS DRIVEN TECHNOLOGY
Management Information Systems MANAGING THE DIGITAL FIRM, 12 TH EDITION FOUNDATIONS OF BUSINESS INTELLIGENCE: DATABASES AND INFORMATION MANAGEMENT Chapter.
Databases, Data Warehouses, and Data Mining
McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 1-1 BUSINESS DRIVEN TECHNOLOGY UNIT 1: Achieving Business Success Through.
Storing Organizational Information - Databases
5-1 McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved.
McGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, All Rights Reserved Chapter 7 Storing Organizational Information - Databases.
McGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, All Rights Reserved Chapter 7 Storing Organizational Information - Databases.
6.1 © 2010 by Prentice Hall 6 Chapter Foundations of Business Intelligence: Databases and Information Management.
Database and Data Warehouse
DATABASES AND DATA WAREHOUSES
Chapter 3 Databases and Data Warehouses: Building Business Intelligence Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.
Chapter 5 DATA WAREHOUSING Study Sections 5.2, 5.3, 5.5, Pages: & Snowflake schema.
McGraw-Hill/Irwin ©2009 The McGraw-Hill Companies, All Rights Reserved CHAPTER 6 DATABASES AND DATA WAREHOUSES CHAPTER 6 DATABASES AND DATA WAREHOUSES.
Foundations of Business Intelligence: Databases and Information Management.
McGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, All Rights Reserved Chapter 8 Accessing Organizational Information – Data Warehouse.
Data: Business Intelligence Chapter 6 McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.
Business Intelligence Transparencies 1. ©Pearson Education 2009 Objectives What business intelligence (BI) represents. The technologies associated with.
Exploring Business Intelligence
Chapter 6.  Problems of managing Data Resources in a Traditional File Environment  Effective IS provides user with Accurate, timely and relevant information.
McGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, All Rights Reserved Chapter 7 Storing Organizational Information - Databases.
ERP and Related Technologies
6.1 © 2010 by Prentice Hall 4 Chapter Databases and Information Management Databases and Information Management.
Data Warehouse – Your Key to Success. Data Warehouse A data warehouse is a  subject-oriented  Integrated  Time-variant  Non-volatile  Restructure.
Copyright  2007 McGraw-Hill Pty Ltd PPTs t/a Marketing Research 2e by Lukas, Hair, Bush and Ortinau Slides prepared by Judy Rex 19-1 Chapter Nineteen.
McGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, All Rights Reserved Chapter 7 Storing Organizational Information - Databases.
Accessing Organizational Information
CHAPTER SIX DATA Business Intelligence
From BI to Big DatA.
Data Warehouse.
Organizational Information – Data Warehouse
DATABASES AND DATA WAREHOUSES Searching for Revenue - Google
CHAPTER SIX OVERVIEW SECTION 6.1 – DATABASE FUNDAMENTALS
Presentation transcript:

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

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

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

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

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

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

DATA WAREHOUSE FUNDAMENTALS

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

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

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

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

Information Cleansing or Scrubbing Contact information in an operational system

Information Cleansing or Scrubbing Standardizing Customer name from Operational Systems

Information Cleansing or Scrubbing Information cleansing activities

Information Cleansing or Scrubbing Accurate and complete information

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

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

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

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

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

BUSINESS DRIVEN TECHNOLOGY UNIT TWO CLOSING

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

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

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?

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

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