Turban, Aronson, and Liang
Decision Support Systems and Intelligent Systems,
Seventh Edition
Chapter 5
Business Intelligence: Data
Warehousing, Data Acquisition, Data
Mining, Business Analytics, and
Visualization
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
5-1
Learning Objectives
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Describe the issues in management of data.
Understand the concepts and use of DBMS.
Learn about data warehousing and data marts.
Explain business intelligence/business analytics.
Examine how decision making can be improved
through data manipulation and analytics.
• Understand the interaction betwixt the Web and
database technologies.
• Explain how database technologies are used in
business analytics.
• Understand the impact of the Web on business
intelligence and analytics.
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Data, Information, Knowledge
• Data
– Items that are the most elementary descriptions
of things, events, activities, and transactions
– May be internal or external
• Information
– Organized data that has meaning and value
• Knowledge
– Processed data or information that conveys
understanding or learning applicable to a
problem or activity
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Data
• Raw data collected manually or by
instruments
• Quality is critical
– Quality determines usefulness
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Contextual data quality
Intrinsic data quality
Accessibility data quality
Representation data quality
– Often neglected or casually handled
– Problems exposed when data is summarized
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Turban, Aronson, and Liang
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Data
• Cleanse data
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When populating warehouse
Data quality action plan
Best practices for data quality
Measure results
• Data integrity issues
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Uniformity
Version
Completeness check
Conformity check
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Data
• Data Integration
• Access needed to multiple sources
– Often enterprise-wide
– Disparate and heterogeneous databases
– XML becoming language standard
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Describe the role of the Internet in MSS data
management and business intelligence.
• The role of the Internet in MSS data
management and business intelligence is
increasing. Currently database vendors are
providing Web hooks that allow their
databases to provide data directly in HTML
or XML format, and Web browsers are
used to access databases. Most business
intelligence tools permit access to data
warehouses via the Internet or company
intranet.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
5-8
•
List the major categories of data
sources for an MSS/BI.
Internal sources; usually the reporting
systems of the functional areas.
External sources (commercial
databases, government and industry
reports, etc.) and personal data.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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• Describe the benefits of commercial
databases.
Provide external data at a timely
manner and at a reasonable cost.
Because of economies of scale, such
services are comprehensive and
inexpensive.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Database Management Systems
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Supplements operating system
Manages data
Queries data and generates reports
Data security
Combines with modeling language for
construction of DSS
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Database Models
• Hierarchical
– Top down, like inverted tree
– Fields have only one “parent”, each “parent” can have multiple
“children”
– Fast
• Network
– Relationships created through linked lists, using pointers
– “Children” can have multiple “parents”
– Greater flexibility, substantial overhead
• Relational
– Flat, two-dimensional tables with multiple access queries
– Examines relations between multiple tables
– Flexible, quick, and extendable with data independence
• Object oriented
– Data analyzed at conceptual level
– Inheritance, abstraction, encapsulation
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Database Models, continued
• Multimedia Based
– Multiple data formats
• JPEG, GIF, bitmap, PNG, sound, video, virtual reality
– Requires specific hardware for full feature
availability
• Document Based
– Document storage and management
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• Define document management.
Document management involves managing
what was once paper documents in a firm. It is a
generally computerized system that provides
access to the most recent versions of important
documents (policies, methods, etc.), restricts
access to appropriate employees, allows updates
by key people, and performs archiving
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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• Define object-oriented database
management.
Based
on
object-oriented
programming: using symbols and
icons it can handle very complex data
structures, show hierarchies, and
complex relationships.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• What is SQL? Why is it important?
A SQL (Structured Query Language) is a
nonprocedural language for data
manipulation in a relational DBMS. It can
be used to query a database, to exercise
DBMS operations, and to perform
database administration functions. It is a
standard used by database vendors to
permit access to relational databases.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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•
What is the difference between a database and a data
warehouse?
Technically a data warehouse is a database, however,
a data warehouse is an integrated, time-variant,
nonvolatile, subject-oriented repository of detail and
summary data used for decision support and business
analytics within an organization. Databases are typically
the term used to describe operational data stores and are
transactional in their structure. As a result databases are
usually highly normalized, whereas data warehouses are
highly denormalized.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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Data warehouse
•
A data warehouse is a physically separate
database
from
a
company’s
operational
environments. Its purpose is to provide decision
support from its data repository that makes
operational data accessible in a form that is readily
acceptable for decision support and other user’s
applications. Data warehousing is the process of
taking internal data, cleansing it, and storing it in a
data warehouse where it can be accessed by
various decision makers in the decision-making
process. External information is also brought into
the data warehouse.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Data Warehouse
• Subject oriented
• Scrubbed so that data from heterogeneous sources are
standardized
• Nonvolatile
– Read only
• Summarized
• Not normalized; may be redundant
• Data from both internal and external sources is present
• Metadata included
– Data about data
• Business metadata
• Semantic metadata
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Architecture
• May have one or more tiers
– Determined by warehouse, data
acquisition (back end), and client (front
end)
• One tier, where all run on same platform, is
rare
• Two tier usually combines DSS engine
(client) with warehouse
– More economical
• Three tier separates these functional parts
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Turban, Aronson, and Liang
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Migrating Data
• Business rules
– Stored in metadata repository
– Applied to data warehouse centrally
• Data extracted from all relevant sources
– Loaded through data-transformation tools or
programs
– Separate operation and decision support
environments
• Correct problems in quality before data
stored
– Cleanse and organize in consistent manner
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Turban, Aronson, and Liang
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Data Warehouse Design
• Dimensional modeling
– Retrieval based
– Implemented by star schema
• Central fact table
• Dimension tables
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star schema
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A Star Schema is a technique used to define the
structure of a data warehouse. It consists of two
components, dimension tables (which define the
criteria by which data will be retrieved ;e.g.,
location, product, time and fact tables (the data
that is of interest to the organization). Facts can
be highly summarized or detail data
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
5-26
• Describe the role that a data warehouse can play
in MSS. List its benefits.
•
The data contained in a data warehouse has
been cleansed and thus has little redundancy and
a higher level of integrity. This gives a higher level
of confidence in the decisions made based on the
data contained in the warehouse. Benefits include
a common storage format, quick access to data for
strategic use, and accurate data.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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Data Marts
A data mart is a small data warehouse
designed for the strategic business unit
(SBU) or a department. Data marts can
either be dependent or independent. They
are important because they can be a cost
effective way to determine the benefits of a
data warehouse to an organization.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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Data Marts
• Dependent
– Created from warehouse
– Replicated
• Functional subset of warehouse
• Independent
– Scaled down, less expensive version of data
warehouse
– Designed for a department or SBU
– Organization may have multiple data marts
• Difficult to integrate
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Turban, Aronson, and Liang
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Business Intelligence and Analytics
• Business intelligence
– Acquisition of data and information for
use in decision-making activities
• Business analytics
– Models and solution methods
• Data mining
– Applying models and methods to data to
identify patterns and trends
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OLAP
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OLAP is the “online analytical
processing” of data. It allows a user to
tap into raw data and perform detailed
and complex analysis directly on the
client machine, without resorting to
back-end processing
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OLAP
• Activities performed by end users in online
systems
– Specific, open-ended query generation
• SQL
– Statistical analysis
– Building DSS applications
• Modeling and visualization capabilities
• Special class of tools
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DSS/BI front ends
Data access front ends
Database front ends
Visual information access systems
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Data Mining
• Organizes and employs information and
knowledge from databases
• Statistical, mathematical, artificial
intelligence, and machine-learning
techniques
• Automatic and fast
• Tools look for patterns
– Simple models
– Intermediate models
– Complex Models
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• Differentiate data mining, text mining, and Web
mining.
Text mining involves analyzing vast amounts of
textual data to determine patterns or correlations
within the text. Data mining is a broader subject
encompassing all types of information contained
within an organization. Web mining extends data
mining to include Web resources in the
determination of correlations or patterns with
organizational data.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Data Visualization
• Technologies supporting visualization
and interpretation
– Digital imaging, GIS, GUI, tables,
multidimensions, graphs, VR, 3D,
animation
– Identify relationships and trends
• Data manipulation allows real time
look at performance data
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Multidimensionality
• Data organized according to business
standards, not analysts
• Conceptual
• Factors Dimensions Measures Time
• Significant overhead and storage
• Expensive
• Complex
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Analytic systems
• Real-time queries and analysis
• Real-time decision-making
• Real-time data warehouses updated
daily or more frequently
– Updates may be made while queries are
active
– Not all data updated continuously
• Deployment of business analytic
applications
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GIS
• Computerized system for managing
and manipulating data with digitized
maps
– Geographically oriented
– Geographic spreadsheet for models
– Software allows web access to maps
– Used for modeling and simulations
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Turban, Aronson, and Liang
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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
5-41
•
It is said that a relational database is the
best for DSS (as compared to
hierarchical and network structures).
Explain why.
Because of its tabular structure, it is easy
to build tables that DSS users like. It is
friendly software that allows multiple
access queries. It is also relatively easy
to convert from one relational system to
another.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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Describe the major dimensions of
data quality.
Intrinsic DQ: Accuracy, objectivity,
and reputation
Accessibility DQ: Accessibility and
access security
Representation DQ: ease of
understanding, consistent
representation.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
5-43