Transcript Chapter 8 - Barbara Hecker

Chapter 8
Enhancing Business Intelligence
Using Information Systems
8-1
“Most executives, many
scientists, and almost all
business school graduates
believe that if you analyze data,
this will give you new ideas.
Unfortunately, this belief is
totally wrong. The mind can only
see what it is prepared to see.”
Edward de Bono,
Creative Thinking Guru
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Learning Objectives
8-2
1. Describe the concept of business intelligence and
how it is used at the operational, managerial, and
executive levels of an organization.
2. Explain the three components of business
intelligence: information and knowledge discovery,
business analytics, and information visualization.
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Learning Objectives
8-3
1. Describe the concept of business intelligence and
how it is used at the operational, managerial, and
executive levels of an organization.
2. Explain the three components of business
intelligence: information and knowledge discovery,
business analytics, and information visualization.
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Business Intelligence (BI)
8-4
 Business Intelligence (BI) is the use of information
systems to gather and analyze information from
internal and external sources in order to make better
business decisions.
 BI is used to integrate data from disconnected:



Reports
Databases
Spreadsheets
 Integrated data helps to monitor and fin-tune business
processes
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BI: Responding to Threats and Opportunities
8-5
 BI can help with reacting
to various threats and
opportunities, including:
 Unstable markets
 Global threats
 Fierce competition
 Short product life cycles
 Wider choices for
consumers
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BI: Continuous Planning
8-6
 Organizations need to continuously monitor and analyze
business processes
 Results lead to ongoing adjustments
 Involves decision makers from all levels
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Decision-Making Levels of an Organization
8-7
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Operational Level
8-8
 Day-to-day business processes
 Interactions with customers
 Decisions:
 Structured
 Recurring
 Can often be automated using IS
 BI used to:
 Optimize processes
 Understand causes of performance problems
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Operational Level (cont’d)
8-9
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Managerial Level
8-10
 Functional managers
 Monitor and control operational-level activities
Focus: effectively utilizing and deploying resources
 Goal: achieving strategic objectives

 Managers’ decisions
 Semistructured
 Moderately complex
 Time horizon of few days to few months
 BI can help with:
 Performance analytics
 Forecasts
 Providing key performance indicators on dashboards
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Managerial Level (cont’d)
8-11
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Executive Level
8-12
 The president, CEO, vice presidents, board of
directors
 Decisions



Unstructured
Long-term strategic issues
Complex and nonroutine problems with long-term
ramifications
 BI is used to:
 Obtain aggregate summaries of trends and projections
 Provide KPIs across the organization
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Executive Level (cont’d)
8-13
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Providing Inputs into BI Applications
8-14
 Decisions made by different departments need to be
based on the same underlying data
 “Single version of the truth”


BI systems access multiple databases or data warehouses
Data aggregated from operational systems

E.g., Transaction processing systems
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Transaction Processing Systems (TPS)
8-15
 Operational level
 Purpose:
 Processing of business events and transactions
 Increase efficiency



Automation
Lower costs
Increased speed and accuracy
 Examples:
 Payroll processing
 Sales and order processing
 Inventory management
 Product purchasing, receiving, and shipping
 Accounts payable and receivable
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Architecture of a TPS
8-16
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Architecture of a TPS: Inputs
8-17
 Source documents
 Different data entry methods
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Architecture of a TPS: Processing
8-18
 Online processing
 Immediate results
 Batch processing
 Transactions collected and later processed together
 Used when immediate notification not necessary
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Architecture of a TPS: Outputs
8-19
 Counts, summary reports
 Inputs to other systems
 Feedback to systems operator
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Summary of TPS Characteristics
8-20
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Learning Objectives
8-21
1. Describe the concept of business intelligence and
how it is used at the operational, managerial, and
executive levels of an organization.
2. Explain the three components of business
intelligence: information and knowledge discovery,
business analytics, and information visualization.
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Business Intelligence Components
8-22
 Three types of tools
 Information and knowledge discovery
 Business analytics
 Information visualization
 Information and Knowledge Discovery
 Search for hidden relationships
 Hypotheses are tested against existing data

E.g., customers with a household income over $150,000 are twice
as likely to respond to our marketing campaign as customers with
an income of $60,000 or less
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Business Intelligence Components
8-23
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Online Analytical Processing (OLAP)
8-24
 Complex, multidimensional analyses of data beyond
simple queries
 OLAP data concepts:

Measures (or facts)—values or numbers the user wants to analyze


Categorized data
Dimensions
Provide a way to summarize data
 Dimensions are organized as hierarchies, allowing to drill down or
roll up

 Example: Sales (measure) could be analyzed by
product or time (dimensions)
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OLAP Cubes
8-25
 OLAP cube: data structure allowing for analysis of
multiple dimensions

Data can be analyzed by more than three dimensions
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Data Slicing and Dicing
8-26
 Slicing and dicing of data allows for analyzing subsets of
the dimensions

E.g., sales by product type and region only for the second
quarter of 2009, or desktop sales in the western region
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Data Mining
8-27
 Used for discovering “hidden” predictive relationships in
the data



Patterns, trends, or rules
E.g, identification of profitable customer segments or fraud
detection
Any predictive models should be tested against “fresh” data
 Data-mining algorithms are run against large data
warehouses

Data reduction helps to reduce the complexity 0f data and
speed up analysis
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Association Discovery
8-28
 Technique used to find associations or correlations
among sets of items

Support and confidence indicate if findings are meaningful
 Sequence Discovery

Association discovery over time
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Clustering and Classification
8-29
 Clustering


Grouping of related records based on similar values for
attributes
Groups are not known beforehand

E.g., clustering frequent fliers based on segments flown
 Classification


Groups (classes) are known beforehand
Records are segmented into the different groups

Often using decision trees
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Text Mining
8-30
 Extracting information
from textual documents
 Can be applied to a
variety of documents:


Web sites
Transcripts
Phone calls
 Interviews
 Student college
applications

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Web Mining
8-31
 Web usage mining
 Determine patterns in customers’ usage data
Clickstream data—recording of the users’ paths through a Web site
 Analyze “stickiness”—ability to attract and keep visitors

 Web content mining
 Extract textual information from Web documents using
Web crawlers
 Analyze using text mining systems
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Presenting Results
8-32
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Business Analytics
8-33
 BI applications to support human and automated
decision making
Information systems to support human decision making
 Intelligent systems
 Tools for enhancing organizational collaboration

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Management Information Systems
8-34
 Managerial level
 Purpose:
 Produce reports
 Support of midlevel managers’ decisions
 Examples:
 Sales forecasting
 Financial management and forecasting
 Manufacturing, planning and scheduling
 Inventory management and planning
 Advertising and product pricing
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Architecture of an MIS
8-35
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Summary of MIS Characteristics
8-36
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Executive Information Systems
8-37
 Aka Executive support system
 Executive level
 Purpose:
 Aid in executive decision making
 Provide information in highly aggregated form
 Examples:
 Executive-level decision making
 Long-range and strategic planning
 Monitoring of internal and external events and resources
 Crisis management
 Staffing and labor relations
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Hard vs. Soft Data
8-38
 EIS can provide both hard and soft data
 Hard data
Facts and numbers
 Generated by TPS & MIS


Soft data
Nonanalytical information
 E.g., latest news stories
 Web-based news portals
 Customizable
 Delivery to different media

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Yahoo Finance Provides Soft Data
8-39
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Architecture of an EIS
8-40
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Summary of EIS Characteristics
8-41
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Decision Support Systems (DSS)
8-42
 Decision-making support for recurring problems
 Used mostly by managerial level employees (can be
used at any level)
 Interactive decision aid
 What-if analyses


Analyze results for hypothetical changes
Example: Microsoft Excel
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Architecture of a DSS
8-43
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Common DSS Models
8-44
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Summary of DSS Characteristics
8-45
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Functional Area Information Systems
8-46
 Cross-organizational-level IS
 Support specific functional area
 Focus on specific set of activities
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Business Processes Supported by Functional
Area Information Systems
8-47
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Organizational Functions and Representative
Information Systems
8-48
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Collaboration Systems
8-49
 Increased need for flexible teams
 Virtual teams—dynamic task forces
 Forming and disbanding as needed
 Fluctuating team size
 Easy, flexible access to other team members
 Need for new collaboration technologies
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Groupware
8-50
 Used to enable more effective team work
 Distinguished along two dimensions
Time (synchronous vs. asynchronous)
 Place (face to face vs. distributed)

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Benefits of Groupware
8-51
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Asynchronous Groupware
8-52
 Various tools are
commonplace in
organizations

E-mail, newsgroups, and
mailing lists, work flow
automation systems,
intranets, group calendars,
and collaborative writing
tools.
 Lotus Notes (released in
1989) still considered
industry leader
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Synchronous Groupware
8-53
 Shared whiteboards, online chat, videoconferencing
 Electronic meeting systems
 Help groups have better meetings
 Uses of EMS
 Strategic planning sessions
 Marketing focus groups
 Brainstorming sessions
 Business process management
 Quality improvement
 Web-based implementations
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Example: Electronic Meeting System
8-54
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Videoconferencing
8-55
 Used to replace traditional
meetings
 Dedicated videoconferencing

Highly realistic but very
expensive
 Desktop videoconferencing:
Low-cost alternative


Web cam
Enablers


Increase in processing power
Internet connection speed
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Intelligent Systems
8-56
 Artificial intelligence
 Simulation of human intelligence
 Reasoning and learning, as well as gaining sensing
capabilities, such as seeing, hearing, walking, talking,
and feeling
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Example: Artificial Intelligence
8-57
Source: http://world.honda.com/ASIMO.
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Intelligent Systems
8-58
 Intelligent system
 Sensors, software and computers
 Emulate and enhance human capabilities
 Three types
 Expert systems
 Neural networks
 Intelligent agents
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Expert Systems
8-59
 Use reasoning methods
 Manipulate knowledge rather than information
 System asks series of questions
 Inferencing/pattern matching
 Matching user responses with predefined rules
 If-then format
 Fuzzy logic
 Represent rules using approximations
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Example: Expert System
8-60
 WebMD.com’s expert system to make a medical
recommendation
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Architecture of an Expert System
8-61
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Summary of ES Characteristics
8-62
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Neural Networks
8-63
 Approximation of human brain functioning
 Training to establish common patterns
 Based on past information
 New data compared to patterns
 Example: loan processing
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Example: Neural Network System
8-64
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Intelligent Agent Systems
8-65



Program working in the background
Bot (software robot)
Provides service when a specific event occurs
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Types of Intelligent Agent Systems
8-66
 User agents
 Performs a task for the user
 Buyer agents (shopping bots)
 Search for the best price
 Monitoring and sensing agents
 Keep track of information and notifies users when it changes

Data-mining agents

Continuously browse data warehouses to detect changes
 Web crawlers (aka Web spiders)
 Continuously browses the Web
 Destructive agents
 Designed to farm e-mail addresses or deposit spyware
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Knowledge Management Systems
8-67
 Generating value from knowledge assets
 Collection of technology-based systems
 Knowledge assets
 Skills, routines, practices, principles, formulas, methods,
heuristics, and intuitions
 Used to improve efficiency, effectiveness, and
profitability
 Documents storing both facts and procedures

Examples:
 Databases, manuals, diagrams, books, and so on
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Knowledge Asset Categories
8-68
 Explicit knowledge
assets

Can be documented
 Tacit knowledge assets


Located in one’s mind
Often reflect an
organization’s best
practices
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Benefits and Challenges of Knowledge-Based
Systems
8-69
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Utilizing Knowledge Management Systems
 Challenge:
 People using the system are spread across organization
 Problems people face may have been solved by someone else
within the same organization
 Goal:
 Facilitate exchange of needed knowledge between separate
“islands”
 Social network analysis
 Knowledge portals
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Web-Based Knowledge Portals
8-71
Knowledge repository
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Information Visualization
8-72
 Display of complex data
relationships using a
graphical methods
Enables managers to
quickly grasp results of
analyses
 Visual analytics
 Dashboards
 Geographic information
systems

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Visual Analytics
8-73
 Interpreting complex output from BI systems is
challenging
 Visual analytics combines various analysis
techniques and interactive visualization

Combination of
Human intelligence and reasoning capabilities
 Technology’s retrieval and analysis capabilities


Helps to make sense of “noisy” data or unexpected patterns
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Digital Dashboards
8-74
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Geographic Information System (GIS)
8-75
 Use of geographically
referenced information




Finding optimal location for
a new store
Identification of areas too
wet to fertilize (see figure)
Locating target customers
Infrastructure design
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Geographic Information System Uses
8-76
 Customer dot mapping
 Trade area analysis
 Thematic mapping
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End of Chapter Content
8-77
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Opening Case—Managing in the Digital World: Providing Business
Intelligence to eBay.com
8-78
 Founded in 1995
 84 million users
 Sales of $30 billion
(2007)
 Problems
Shill bidding
 Sellers who don’t send
 etc.

 How to determine
patterns of fraudulent
behavior?
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Bad Intelligence—Anonymous Hackers Punish the
Wrong Person
8-79
 In 2008, The Church of Scientology was attacked
by the hacker group “Anonymous”
 The attack was due to retribution for the church
removing Tom Cruise interviews on the Web
 A hacker war with pro-Scientology hackers started

An innocent couple in California was accidentally
targeted as pro-Scientology hackers
Phone number posted online
 Social security number posted online
 Threatening phone calls

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The Demise of Broadcast TV
8-80
 56% of 18-34 aren’t watching TV on TV
 Online recording and DVR are the media of choice
 The broadcast industry needs to respond
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Too Much Intelligence?
RFID and Privacy
8-81
 RFID tags
 Latest in technological tracking devices
 Information imprinted on a tag
 Tag generates signature signal
 Special RFID reader interprets signal
 E.g., used by pharmaceutical industry to prevent
counterfeits
 Privacy concerns


Someone with an RFID reader could see where you bought a product
and what you paid for it
Devices needed to erase tracking information
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Instant Messaging at Work
8-82
 Using public IM has disadvantages for organizations:
 Security cannot be assumed
 Data resides on the provider’s server
 Access to the network cannot be blocked
 Alternatives:

Internal IM network
Secure message transfer
 Ability to handle thousands of employee accounts
 Platform compatibility
 Access from outside of the WAN
 Proper access rights


IM hosting service
Data resides on provider’s server
 Privacy concerns

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Very Smart Phones
8-83
 Turning the Mobile Phone into a personal assistant

Magitti
 Developed by Palo Alto Research Center (PARC)
 Combines time of day, location, past behaviors, and text messages
to suggest activities

MIT Media Lab has similar projects to predict behavior from GPS
locators, call logs, etc.

iPhone has various sensors built in

All it needs is applications being developed to utilize hardware capabilities
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Adobe’s John Warnock
and Chuck Geschke
8-84
 Warnock and Geschke worked
together at Xerox’ Palo Alto
Research Center (PARC)
 Developed PostScript

Technology that simplifies printing
documents directly from computers
 Warnock and Geschke left Xerox in
1982 to found Adobe Systems, Inc.
 Geschke was kidnapped in 1992
 Adobe is one of the biggest software
companies in the world
 Products include:

Acrobat, ColdFusion, Dreamweaver,
Flash, Photoshop, and many others
IS Today (Valacich & Schneider)
4/12/2017
Copyright © 2010 Pearson Education, Inc. Published as Prentice Hall
Healthcare
8-85
 Healthcare increasingly reliant on information technology

Many doctors carry PDAs/laptops to access patient records
or drug information
 Electronic patient records moving towards the Web

Google health and Microsoft HealthVault
 Other applications

Diagnosis and monitoring



EEG, EKG, computer tomography
Digital x-rays
Tele-medicine

Remote diagnosis and surgery
IS Today (Valacich & Schneider)
4/12/2017
Copyright © 2010 Pearson Education, Inc. Published as Prentice Hall