Data Warehouse Yong Shi CSE DEPARTMENT. Strategic delivery of information The current Situation The never-ending quest to access any information, anywhere,

Slides:



Advertisements
Similar presentations
1 INCOSE HRA Advanced Risk Management Conference 2007 Courtney Lane INCOSE HRA Risk Management Conference November 9, 2007 Its More Than Just Numbers:
Advertisements

6.1 © 2007 by Prentice Hall 6 Chapter Foundations of Business Intelligence: Databases and Information Management.
MethodAssess System Assessment. Methoda Computers Ltd 2 List of Subjects 1. Introduction 2. Actions and deliverables 3. Lessons and decisions.
Faculty of Computer Science © 2006 CMPUT 605February 11, 2008 A Data Warehouse Architecture for Clinical Data Warehousing Tony R. Sahama and Peter R. Croll.
Basic guidelines for the creation of a DW Create corporate sponsors and plan thoroughly Determine a scalable architectural framework for the DW Identify.
Lecture 5 Themes in this session Building and managing the data warehouse Data extraction and transformation Technical issues.
Managing Data Resources
Multidimensional Database Structure
Chapter 9 DATA WAREHOUSING Transparencies © Pearson Education Limited 1995, 2005.
1 Samples The following slides are provided as samples and references for the Quarterly Reviews Additional slides will be added.
1 1 File Systems and Databases Chapter 1 The Worlds of Database Systems Prof. Sin-Min Lee Dept. of Computer Science.
DATA WAREHOUSING.
Pertemuan Matakuliah: A0214/Audit Sistem Informasi Tahun: 2007.
8 Systems Analysis and Design in a Changing World, Fifth Edition.
1 1 File Systems and Databases Chapter 1 Prof. Sin-Min Lee Dept. of Computer Science.
©1999, 2002, Joyce Bischoff, All rights reserved. Conducting Data Warehouse Assessments Joyce Bischoff Bischoff Consulting, Inc. Hockessin, Delaware
© 2003, Prentice-Hall Chapter Chapter 2: The Data Warehouse Modern Data Warehousing, Mining, and Visualization: Core Concepts by George M. Marakas.
Acquiring Information Systems and Applications
1 Data Strategy Overview Keith Wilson Session 15.
By N.Gopinath AP/CSE. Why a Data Warehouse Application – Business Perspectives  There are several reasons why organizations consider Data Warehousing.
Chapter 1 Database Systems. Good decisions require good information derived from raw facts Data is managed most efficiently when stored in a database.
Teaching Data Management - An Overview Anne Marie Smith La Salle University.
Organizing Information Technology Resources
INFO425: Systems Design INFORMATION X Finalizing Scope (functions/level of automation)  Finalizing scope in terms of functions and level of.
5.1 © 2007 by Prentice Hall 5 Chapter Foundations of Business Intelligence: Databases and Information Management.
Understanding Data Warehousing
Test Organization and Management
DBS201: DBA/DBMS Lecture 13.
Developing an IS/IT Strategy
Software Development *Life-Cycle Phases* Compiled by: Dharya Dharya Daisy Daisy
THE REGIONAL MUNICIPALITY OF YORK Information Technology Strategy & 5 Year Plan.
Improving Performance Through Integrated Analytics (iAnalytics) Lori Watson Principal Consultant IBM Business Consulting Services October 29, 2002.
Best Practices: Aligning Process, Culture and Tools Michael Jordan Senior Project Manager - Microsoft Consulting Services
Bennett Adelson. Microsoft Solution Center. Independence OH February 4, 2010 BENNETT ADELSON Microsoft® Solution Center.
Implementing and Integrating AI Systems. What Is Implementation? Implementation can be defined as getting a newly developed or significantly changed system.
Module 4: Systems Development Chapter 12: (IS) Project Management.
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall Chapter Chapter 10: The Data Warehouse Decision Support Systems in the 21 st.
© Mahindra Satyam 2009 Decision Analysis and Resolution QMS Training.
Data Warehouse Fundamentals Rabie A. Ramadan, PhD 2.
Data Cleansing Rule Based Strategy.
5 - 1 Copyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved.
Data Warehouse. Group 5 Kacie Johnson Summer Bird Washington Farver Jonathan Wright Mike Muchane.
CISB594 – Business Intelligence Data Warehousing Part I.
AL-MAAREFA COLLEGE FOR SCIENCE AND TECHNOLOGY INFO 232: DATABASE SYSTEMS CHAPTER 1 DATABASE SYSTEMS Instructor Ms. Arwa Binsaleh.
Foundations of Information Systems in Business. System ® System  A system is an interrelated set of business procedures used within one business unit.
Do It Strategically with Microsoft Business Intelligence! Bojan Ciric Strategic Consultant
Why BI….? Most companies collect a large amount of data from their business operations. To keep track of that information, a business and would need to.
Program Management Office ͏ Project Management
Database Systems: Design, Implementation, and Management Eighth Edition Chapter 1 Database Systems.
MBA/1092/10 MBA/1093/10 MBA/1095/10 MBA/1114/10 MBA/1115/10.
Development Project Management Jim Kowalkowski. Outline Planning and managing software development – Definitions – Organizing schedule and work (overall.
Chapter 8: Data Warehousing. Data Warehouse Defined A physical repository where relational data are specially organized to provide enterprise- wide, cleansed.
Business Intelligence and Decision Support Systems (9 th Ed., Prentice Hall) Chapter 8: Data Warehousing.
Risk Controls in IA Zachary Rensko COSC 481. Outline Definition Risk Control Strategies Risk Control Categories The Human Firewall Project OCTAVE.
Slide 1 Data Warehousing in CIM  2000 YourNameHere Data Warehousing in Computer Integrated Manufacturing Steve Daino IEM 5303.
Towards a dependable and sustainable National IT Infrastructure MANAGING IT INFRASTRUCTURE ASSETS: IMPLEMENTATION NEEDS.
Chapter 6 Foundations of Business Intelligence: Databases and Information Management.
INTRORDUCTION TO IT PORTFOLIO MANAGEMENT Pertemuan 1-2
Identify the Risk of Not Doing BA
Anjali Yakkundi, Analyst
Systems Analysis – ITEC 3155 Evaluating Alternatives for Requirements, Environment, and Implementation.
OLAP Systems versus Statistical Databases
Chapter 6 Foundations of Business Intelligence: Databases and Information Management.
Data Warehouse and OLAP
C.U.SHAH COLLEGE OF ENG. & TECH.
Chapter 6 Foundations of Business Intelligence: Databases and Information Management.
Data Warehousing Concepts
Agenda Purpose for Project Goals & Objectives Project Process & Status Common Themes Outcomes & Deliverables Next steps.
The Database Environment
Data Warehouse and OLAP
Presentation transcript:

Data Warehouse Yong Shi CSE DEPARTMENT

Strategic delivery of information The current Situation The never-ending quest to access any information, anywhere, anytime. The problem Data is scattered in many types of incompatible structures.

Analytical processing requirements Four levels of analytical processing: 1. Simple queries and reports 2. The ability to do “what if” processing 3. Step back and analyze what has previously occurred to bring about the current state of date 4. Analyze what has happened in the past and what needs to be done in the future for a specific change

Information data superstore(IDSS) Definition: The architecture needed to support the far- ranging requirements of the four levels of analysis. Also called super data warehouse Data warehouses is not an end of themselves but merely a step on the path to the information data super store

Why need for a separate environment The use of operational systems v.s data warehouse The data’s characteristics The type of access

A strategy for building a data warehouse Need indicators Action steps Three-stage data warehousing processing: model  build  deploy (understand) (establish) (implement)

Organizational and cultural issues Cultural imperatives Success criteria Satisfy users’ requirements Make a significant contribution to the success of the business The users accept and actively use it The benefits are not exceeded by the costs An adequate budget must be in place

Organizational and cultural issues Success criteria(continued) The implementation of the data warehouse must not cause other problems that overshadow the benefits A reasonable schedule must be established

Organizational and cultural issues End user(client) Strategic architecture User liaison End-user support Data analyst Security office Data administration

Organizational and cultural issues Database administration Choosing the initial data and department Establishing an infrastructure Training users Change in the power structure

End Users A crucial part of the project Gathering requirements and managing expectations Cost justification process Design reviews User perspective User training

A technical architecture for DW Source Data Data Acquisition Component Data Manager Component Warehouse Data Information Directory Component Warehouse Data External Data Data Access Component Design Component Management Component Data Delivery Component Middleware Component External Data

Data Quality Why is data quality important? Data is a critical issue It will limit the ability of the end users to make informed decision. It has a profound effect on the image of the enterprise. The poor one will make it difficult to make major changes in an organization.

Data Quality What is data quality? The data is accurate The data is stored according to data type The data has integrity The data is consistent The databases are well designed The data is accurate The data is stored according to data type The data has integrity The data is consistent The databases are well designed

Data Quality The data is not redundant The data follow business rules The data corresponds to established domains The data is timely The data is well understood

Data Quality The data satisfies the needs of the business The user is satisfied with the quality of the data and the information derived from that data There are no duplicate records Data anomalies

Data Quality Assessment of existing data quality Programs that abnormally terminate with data exceptions Clients who experience errors/anomalies Clients who do not know or are confused about what the data actually means Data that cannot be shared due to lack of integration

Data Quality What data should be improved? The energy should be spent on data where the quality improvement will bring an important benefit to the business. We can ignore unimportant data and obsolete data. Other criteria: improve those which can be fixed and kept clean.

Data Quality Purification process Determine the importance of data quality to the organization Identify the enterprise’s most important data and evaluate the quality. Determine users’ and owners’ perception of data quality. Prioritize which data to purify. Assemble and train a team to clean the data. Select tools to aid in the purification process, etc.

Data Quality Data quality case Lesson1: If those entering the data have a stake in the data being incorrect, the data will be incorrect. Lesson2: Reports may show desired results, but the reports may be highly inaccurate.

Directory/Catalog The challenge Providing short-term benefit without disabling broader long-term information handling solutions. Getting data into a warehouse is only half of the process.

Security in the data warehouse Basic security concepts Physical security Stand-alone or shared security Remote access