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Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)
Chapter 3:
Data Warehousing
Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)
Copyright © 2014 Pearson Education, Inc. 3-2
Learning Objectives
(Continued…)
Understand the basic definitions and concepts of data warehouses
Learn different types of data warehousing architectures; their comparative advantages and disadvantages
Describe the processes used in developing and managing data warehouses
Explain data warehousing operations
…
Copyright © 2014 Pearson Education, Inc. 3-3
Learning Objectives
Explain the role of data warehouses in decision support
Explain data integration and the extraction, transformation, and load (ETL) processes
Describe real-time (a.k.a. right-time and/or active) data warehousing
Understand data warehouse administration and security issues
Copyright © 2014 Pearson Education, Inc. 3-4
Opening Vignette…
“Isle of Capri Casinos Is Winning with Enterprise Data Warehouse”
Company background
Problem description
Proposed solution
Results
Answer & discuss the case questions.
Copyright © 2014 Pearson Education, Inc. 3-5
Questions for the Opening Vignette
1. Why is it important for Isle to have an EDW?
2. What were the business challenges or opportunities that Isle was facing?
3. What was the process Isle followed to realize EDW? Comment on the potential challenges Isle might have had going through the process of EDW development.
4. What were the benefits of implementing an EDW at Isle? Can you think of other potential benefits that were not listed in the case?
5. Why do you think large enterprises like Isle in the gaming industry can succeed without having a capable data warehouse/business intelligence infrastructure?
Copyright © 2014 Pearson Education, Inc. 3-6
Main Data Warehousing Topics
DW definition
Characteristics of DW
Data Marts
ODS, EDW, Metadata
DW Framework
DW Architecture & ETL Process
DW Development
DW Issues
Copyright © 2014 Pearson Education, Inc. 3-7
What is a Data Warehouse?
A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format
“The data warehouse is a collection of integrated, subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time”
Copyright © 2014 Pearson Education, Inc. 3-8
A Historical Perspective to Data Warehousing
1970s 1980s 1990s 2000s 2010s
ü Mainframe computersü Simple data entry ü Routine reportingü Primitive database structuresü Teradata incorporated
ü Mini/personal computers (PCs)ü Business applications for PCsü Distributer DBMSü Relational DBMSü Teradata ships commercial DBsü Business Data Warehouse coined
ü Centralized data storageü Data warehousing was born ü Inmon, Building the Data Warehouse ü Kimball, The Data Warehouse Toolkit ü EDW architecture design
ü Exponentially growing data Web dataü Consolidation of DW/BI industry ü Data warehouse appliances emergedü Business intelligence popularizedü Data mining and predictive modelingü Open source softwareü SaaS, PaaS, Cloud Computing
ü Big Data analyticsü Social media analyticsü Text and Web Analyticsü Hadoop, MapReduce, NoSQLü In-memory, in-database
Copyright © 2014 Pearson Education, Inc. 3-9
Characteristics of DWs
Subject oriented
Integrated
Time-variant (time series)
Nonvolatile
Summarized
Not normalized
Metadata
Web based, relational/multi-dimensional
Client/server, real-time/right-time/active...
Copyright © 2014 Pearson Education, Inc. 3-10
Data Mart
A departmental small-scale “DW” that stores only limited/relevant data
Dependent data mart
A subset that is created directly from a data warehouse
Independent data mart
A small data warehouse designed for a strategic business unit or a department
Copyright © 2014 Pearson Education, Inc. 3-11
Other DW Components
Operational data stores (ODS)
A type of database often used as an interim area for a data warehouse
Oper marts - an operational data mart.
Enterprise data warehouse (EDW)
A data warehouse for the enterprise.
Metadata: Data about data.
In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use
Copyright © 2014 Pearson Education, Inc. 3-12
Application Case 3.1
A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry
Questions for Discussion
1. What are the main challenges for TELCOs?
2. How can data warehousing and data analytics help TELCOs in overcoming their challenges?
3. Why do you think TELCOs are well suited to take full advantage of data analytics?
Copyright © 2014 Pearson Education, Inc. 3-13
A Generic DW Framework
Data
Sources
ERP
Legacy
POS
Other
OLTP/wEB
External
data
Select
Transform
Extract
Integrate
Load
ETL
Process
Enterprise
Data warehouse
Metadata
Replication
A P
I / M
idd
lew
are Data/text
mining
Custom built
applications
OLAP,
Dashboard,
Web
Routine
Business
Reporting
Applications
(Visualization)
Data mart
(Engineering)
Data mart
(Marketing)
Data mart
(Finance)
Data mart
(...)
Access
No data marts option
Copyright © 2014 Pearson Education, Inc. 3-14
Application Case 3.2
Data Warehousing Helps MultiCare Save More Lives
Questions for Discussion
1. What do you think is the role of data warehousing in healthcare systems?
2. How did MultiCare use data warehousing to improve health outcomes?
Copyright © 2014 Pearson Education, Inc. 3-15
DW Architecture
Three-tier architecture
1. Data acquisition software (back-end)
2. The data warehouse that contains the data & software
3. Client (front-end) software that allows users to access and analyze data from the warehouse
Two-tier architecture
First two tiers in three-tier architecture is combined into one
… sometimes there is only one tier?
Copyright © 2014 Pearson Education, Inc. 3-16
DW Architectures
Tier 2:
Application server
Tier 1:
Client workstation
Tier 3:
Database server
Tier 1:
Client workstation
Tier 2:
Application & database server
Copyright © 2014 Pearson Education, Inc. 3-17
Data Warehousing Architectures
Issues to consider when deciding which architecture to use: Which database management system (DBMS)
should be used?
Will parallel processing and/or partitioning be used?
Will data migration tools be used to load the data warehouse?
What tools will be used to support data retrieval and analysis?
Copyright © 2014 Pearson Education, Inc. 3-18
A Web-Based DW Architecture
Web
Server
Client
(Web browser)
Application
Server
Data
warehouse
Web pages
Internet/
Intranet/
Extranet
Alternative DW Architectures
Source
Systems
Staging
Area
Independent data marts
(atomic/summarized data)
End user
access and
applications
ETL
Source
Systems
Staging
Area
End user
access and
applications
ETL
Dimensionalized data marts
linked by conformed dimensions
(atomic/summarized data)
Source
Systems
Staging
Area
End user
access and
applications
ETL
Normalized relational
warehouse (atomic data)
Dependent data marts
(summarized/some atomic data)
(a) Independent Data Marts Architecture
(b) Data Mart Bus Architecture with Linked Dimensional Datamarts
(c) Hub and Spoke Architecture (Corporate Information Factory)
Alternative DW Architectures
Each architecture has advantages and disadvantages!
Which architecture is the best?
Source
Systems
Staging
Area
Normalized relational
warehouse (atomic/some
summarized data)
End user
access and
applications
End user
access and
applications
Logical/physical integration of
common data elementsExisting data warehouses
Data marts and legacy systems
ETL
Data mapping / metadata
(d) Centralized Data Warehouse Architecture
(e) Federated Architecture
Copyright © 2014 Pearson Education, Inc. 3-21
Ten factors that potentially affect the architecture selection decision
1. Information interdependence between organizational units
2. Upper management’s information needs
3. Urgency of need for a data warehouse
4. Nature of end-user tasks
5. Constraints on resources
6. Strategic view of the data warehouse prior to implementation
7. Compatibility with existing systems
8. Perceived ability of the in-house IT staff
9. Technical issues
10.Social/political factors
Copyright © 2014 Pearson Education, Inc. 3-22
Teradata Corp. DW Architecture
Copyright © 2014 Pearson Education, Inc. 3-23
Data Integration and the Extraction, Transformation, and Load Process
ETL = Extract Transform Load
Data integration
Integration that comprises three major processes: data access, data federation, and change capture.
Enterprise application integration (EAI)
A technology that provides a vehicle for pushing data from source systems into a data warehouse
Enterprise information integration (EII)
An evolving tool space that promises real-time data integration from a variety of sources, such as relational or multidimensional databases, Web services, etc.
Copyright © 2014 Pearson Education, Inc. 3-24
Data Integration and the Extraction, Transformation, and Load Process
Packaged
application
Legacy
system
Other internal
applications
Transient
data source
Extract Transform Cleanse Load
Data
warehouse
Data mart
Copyright © 2014 Pearson Education, Inc. 3-25
ETL (Extract, Transform, Load)
Issues affecting the purchase of an ETL tool
Data transformation tools are expensive
Data transformation tools may have a long learning curve
Important criteria in selecting an ETL tool
Ability to read from and write to an unlimited number of data sources/architectures
Automatic capturing and delivery of metadata
A history of conforming to open standards
An easy-to-use interface for the developer and the functional user
Copyright © 2014 Pearson Education, Inc. 3-26
Data Warehouse Development
Data warehouse development approaches
Inmon Model: EDW approach (top-down)
Kimball Model: Data mart approach (bottom-up)
Which model is best?
Table 3.3 provides a comparative analysis between EDW and Data Mart approach
One alternative is the hosted warehouse
Copyright © 2014 Pearson Education, Inc. 3-27
Application Case 3.5
Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing Questions for Discussion 1. How big and complex are the business
operations of Starwood Hotels & Resorts?
2. How did Starwood Hotels & Resorts use data warehousing for better profitability?
3. What were the challenges, the proposed solution, and the obtained results?
Copyright © 2014 Pearson Education, Inc. 3-28
Additional DW Considerations Hosted Data Warehouses
Benefits:
Requires minimal investment in infrastructure
Frees up capacity on in-house systems
Frees up cash flow
Makes powerful solutions affordable
Enables solutions that provide for growth
Offers better quality equipment and software
Provides faster connections
… more in the book
Copyright © 2014 Pearson Education, Inc. 3-29
Representation of Data in DW
Dimensional Modeling
A retrieval-based system that supports high-volume query access
Star schema
The most commonly used and the simplest style of dimensional modeling
Contain a fact table surrounded by and connected to several dimension tables
Snowflakes schema
An extension of star schema where the diagram resembles a snowflake in shape
Copyright © 2014 Pearson Education, Inc. 3-30
The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (four dimensions)
Multidimensional presentation
Dimensions: products, salespeople, market segments, business units, geographical locations, distribution channels, country, or industry
Measures: money, sales volume, head count, inventory profit, actual versus forecast
Time: daily, weekly, monthly, quarterly, or yearly
Multidimensionality
Copyright © 2014 Pearson Education, Inc. 3-31
Star versus Snowflake Schema
Fact Table
SALES
UnitsSold
...
Dimension
TIME
Quarter
...
Dimension
PEOPLE
Division
...
Dimension
PRODUCT
Brand
...
Dimension
GEOGRAPHY
Country
...
Fact Table
SALES
UnitsSold
...
Dimension
DATE
Date
...
Dimension
PEOPLE
Division
...
Dimension
PRODUCT
LineItem
...
Dimension
STORE
LocID
...
Dimension
BRAND
Brand
...
Dimension
CATEGORY
Category
...
Dimension
LOCATION
State
...
Dimension
MONTH
M_Name
...
Dimension
QUARTER
Q_Name
...
Star Schema Snowflake Schema
Copyright © 2014 Pearson Education, Inc. 3-32
Analysis of Data in DW
OLTP vs. OLAP…
OLTP (online transaction processing) Capturing and storing data from ERP, CRM, POS, … The main focus is on efficiency of routine tasks
OLAP (Online analytical processing) Converting data into information for decision support Data cubes, drill-down / rollup, slice & dice, … Requesting ad hoc reports Conducting statistical and other analyses Developing multimedia-based applications …more in the book
Copyright © 2014 Pearson Education, Inc. 3-33
OLAP vs. OLTP
Copyright © 2014 Pearson Education, Inc. 3-34
OLAP Operations
Slice - a subset of a multidimensional array
Dice - a slice on more than two dimensions
Drill Down/Up - navigating among levels of data ranging from the most summarized (up) to the most detailed (down)
Roll Up - computing all of the data relationships for one or more dimensions
Pivot - used to change the dimensional orientation of a report or an ad hoc query-page display
Copyright © 2014 Pearson Education, Inc. 3-35
OLAP
Product
Time
Ge
og
rap
hy
Sales volumes of
a specific Product
on variable Time
and Region
Sales volumes of
a specific Region
on variable Time
and Products
Sales volumes of
a specific Time on
variable Region
and Products
Cells are filled
with numbers
representing
sales volumes
A 3-dimensional
OLAP cube with
slicing
operations
Slicing Operations on a Simple Tree-Dimensional Data Cube
Copyright © 2014 Pearson Education, Inc. 3-36
Variations of OLAP
Multidimensional OLAP (MOLAP)
OLAP implemented via a specialized multidimensional database (or data store) that summarizes transactions into multidimensional views ahead of time
Relational OLAP (ROLAP)
The implementation of an OLAP database on top of an existing relational database
Database OLAP and Web OLAP (DOLAP and WOLAP); Desktop OLAP,…
Copyright © 2014 Pearson Education, Inc. 3-37
Technology Insights 3.2 Hands-On DW with MicroStrategy
A wealth of teaching and learning resources can be found at TUN portal
www.teradatauniversitynetwork.com
The available resource includes scripted demonstrations, assignments, white papers, etc…
Copyright © 2014 Pearson Education, Inc. 3-38
DW Implementation Issues
Identification of data sources and governance
Data quality planning, data model design
ETL tool selection
Establishment of service-level agreements
Data transport, data conversion
Reconciliation process
End-user support
Political issues
… more in the book
Copyright © 2014 Pearson Education, Inc. 3-39
Successful DW Implementation Things to Avoid
Starting with the wrong sponsorship chain
Setting expectations that you cannot meet
Engaging in politically naive behavior
Loading the data warehouse with information just because it is available
Believing that data warehousing database design is the same as transactional database design
Choosing a data warehouse manager who is technology oriented rather than user oriented
… more in the book
Copyright © 2014 Pearson Education, Inc. 3-40
Failure Factors in DW Projects
Lack of executive sponsorship
Unclear business objectives
Cultural issues being ignored
Change management
Unrealistic expectations
Inappropriate architecture
Low data quality / missing information
Loading data just because it is available
Copyright © 2014 Pearson Education, Inc. 3-41
Massive DW and Scalability
Scalability
The main issues pertaining to scalability:
The amount of data in the warehouse
How quickly the warehouse is expected to grow
The number of concurrent users
The complexity of user queries
Good scalability means that queries and other data-access functions will grow linearly with the size of the warehouse
Copyright © 2014 Pearson Education, Inc. 3-42
Real-Time/Active DW/BI
Enabling real-time data updates for real-time analysis and real-time decision making is growing rapidly
Push vs. Pull (of data)
Concerns about real-time BI Not all data should be updated continuously
Mismatch of reports generated minutes apart
May be cost prohibitive
May also be infeasible
Copyright © 2014 Pearson Education, Inc. 3-43
Enterprise Decision Evolution and Data Warehousing
Copyright © 2014 Pearson Education, Inc. 3-44
Real-Time/Active DW at Teradata
Copyright © 2014 Pearson Education, Inc. 3-45
Traditional versus Active DW
Copyright © 2014 Pearson Education, Inc. 3-46
DW Administration and Security
Data warehouse administrator (DWA)
DWA should…
have the knowledge of high-performance software, hardware and networking technologies
possess solid business knowledge and insight
be familiar with the decision-making processes so as to suitably design/maintain the data warehouse structure
possess excellent communications skills
Security and privacy is a pressing issue in DW
Safeguarding the most valuable assets
Government regulations (HIPAA, etc.)
Must be explicitly planned and executed
Copyright © 2014 Pearson Education, Inc. 3-47
The Future of DW
Sourcing…
Web, social media, and Big Data
Open source software
SaaS (software as a service)
Cloud computing
Infrastructure…
Columnar
Real-time DW
Data warehouse appliances
Data management practices/technologies
In-database & In-memory processing New DBMS
Advanced analytics
…
Copyright © 2014 Pearson Education, Inc. 3-48
Free of Charge DW Portal for Teaching & Learning
www.TeradataStudentNetwork.com
Password to signup: <check with your instructor>
Copyright © 2014 Pearson Education, Inc. 3-49
End of the Chapter
Questions, comments
Copyright © 2014 Pearson Education, Inc. 3-50
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any
means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the
United States of America.