This chapter discusses decision support systems (DSS) and how they differ from traditional management information systems (MIS). DSS provide interactive support to managers during semistructured decision making through tools like analytical models, databases, and computer modeling. MIS produce predefined reports to support more structured decisions. The chapter outlines several types of DSS including executive information systems, enterprise portals, online analytical processing (OLAP), geographic information systems, and data visualization systems. It also discusses how various analytical techniques can be used in DSS to support decision making.
This presentation covers one of the process of Strategic Management; Strategic Implementation. There are 2 sub divisions; Functional Implementation and Structural Implementation. This section deals with Structural Implementation in detail.
Management Information System (MIS) is a planned system of collecting, storing, and disseminating data in the form of information needed to carry out the functions of management. A Management Information System is an information system that evaluates, analyzes, and processes an organization's data to produce meaningful and useful information based on which the management can take right decisions to ensure future growth of the organization.
Decision Support System - Management Information SystemNijaz N
Refers to class of system which supports in the process of decision making and does not always give a decision itself.
Decision Support Systems supply computerized support for the decision making process.
This presentation covers one of the process of Strategic Management; Strategic Implementation. There are 2 sub divisions; Functional Implementation and Structural Implementation. This section deals with Structural Implementation in detail.
Management Information System (MIS) is a planned system of collecting, storing, and disseminating data in the form of information needed to carry out the functions of management. A Management Information System is an information system that evaluates, analyzes, and processes an organization's data to produce meaningful and useful information based on which the management can take right decisions to ensure future growth of the organization.
Decision Support System - Management Information SystemNijaz N
Refers to class of system which supports in the process of decision making and does not always give a decision itself.
Decision Support Systems supply computerized support for the decision making process.
The series of presentations contains the information about "Management Information System" subject of SEIT for University of Pune.
Subject Teacher: Tushar B Kute (Sandip Institute of Technology and Research Centre, Nashik)
http://www.tusharkute.com
Enhancing Decision Making - Management Information SystemFaHaD .H. NooR
Problem: Chain retailers need to determine what products will sell at what prices at different locations
Solutions: Business analytics software to analyze patterns in sales data, create pricing profiles and buyer profiles for different regions, locales, even times of day
Senior managers:
Make many unstructured decisions
E.g. Should we enter a new market?
Middle managers:
Make more structured decisions but these may include unstructured components
E.g. Why is order fulfillment report showing decline in Lahore?
Operational managers, rank and file employees
Make more structured decisions
E.g. Does customer meet criteria for credit?
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
2. 9- 2
Learning Objectives
1. Identify the changes taking place in the form
and use of decision support in business.
2. Identify the role and reporting alternatives of
management information systems.
3. Describe how online analytical processing can
meet key information needs of managers.
4. Explain the decision support system concept
and how it differs from traditional management
information systems.
3. 9- 3
Learning Objectives
5. Explain how the following information systems
can support the information needs of
executives, managers, and business
professionals:
a. Executive information systems
b. Enterprise information portals
c. Knowledge management systems
4. 9- 4
Learning Objectives
5. Identify how neural networks, fuzzy logic,
genetic algorithms, virtual reality, and
intelligent agents can be used in business.
6. Give examples of several ways expert systems
can be used in business decision-making
situations.
5. 9- 5
Case 1: Oracle Corporation and Others:
Dashboards for Executives and Business
Professionals: The Power and the Challenge
• The dashboard has become the CEO’s killer app.
• Dashboards provide key business information to
executives, managers, and business professionals.
• At GE executives use dashboard to follow the
production of everything from light bulbs to
dishwashers, making sure production lines are
running smoothly.
• Dashboards have some challenges. These tools can
raise pressure on employees, create divisions in the
office, and lead workers to hoard information.
• Dashboards can hurt the morale of employees.
6. 9- 6
Case Study Questions
1. What is the attraction of dashboards to CEOs and
other executives? What real business value do
they provide to executives?
2. The case emphasizes that managers of small
businesses and many business professionals now
rely on dashboards. What business benefits do
dashboards provide to this business audience?
3. What are several reasons for criticism of the use of
dashboards by executives? Do you agree with any
of this criticism? Why or why not?
7. 9- 7
Real World Internet Activity
1. Use the Internet to research makers of
dashboards for large and small business. For
example, try NetSuite, Hyperion Solutions, and
Salesforce.com for relatively inexpensive
versions and Microsoft, Oracle, and SAP for
more costly corporate dashboards. Evaluate
the dashboard examples and demos you
experience. Pick your favorites and explain
your reasons for doing so to the class.
8. 9- 8
Real World Group Activity
2. How would you like to work for an executive
whose dashboard provides the level of
information about company and employee
performance described in this case? Would you
want that level of information when you enter
the executive ranks?
– Discuss this issue, and formulate suggestions
on any changes or safeguards you would
propose for the business use of dashboards.
10. 9- 10
Levels of Management Decision
Making
• Strategic management
– Executives develop organizational goals, strategies,
policies, and objectives
– As part of a strategic planning process
• Tactical management
– Managers and business professionals in self-directed
teams
– Develop short- and medium-range plans, schedules
and budgets
– Specify the policies, procedures and business
objectives for their subunits
11. 9- 11
Levels of Management Decision
Making
• Operational management
– Managers or members of self-directed teams
– Develop short-range plans such as weekly production
schedules
12. 9- 12
Information Quality
• Information products whose characteristics,
attributes, or qualities make the information more
value
• Information has 3 dimensions:
– Time
– Content
– Form
14. 9- 14
Decision Structure
• Structured – situations where the procedures to
follow when a decision is needed can be
specified in advance
• Unstructured – decision situations where it is not
possible to specify in advance most of the
decision procedures to follow
• Semistructured - decision procedures that can
be prespecified, but not enough to lead to a
definite recommended decision
15. 9- 15
Information Systems to support
decisions
Management
Information Systems
Decision Support
Systems
Decision
support
provided
Provide information about
the performance of the
organization
Provide information and
techniques to analyze
specific problems
Information
form and
frequency
Periodic, exception,
demand, and push reports
and responses
Interactive inquiries and
responses
Information
format
Prespecified, fixed format Ad hoc, flexible, and
adaptable format
Information
processing
methodology
Information produced by
extraction and manipulation
of business data
Information produced by
analytical modeling of
business data
16. 9- 16
Decision Support Trends
• Personalized proactive decision analytics
• Web-Based applications
• Decisions at lower levels of management and by
teams and individuals
• Business intelligence applications
18. 9- 18
Decision Support Systems
• DSS
• Provide interactive information support to
managers and business professionals during the
decision-making process
• Use:
– Analytical models
– Specialized databases
– A decision maker’s own insights and judgments
– Interactive computer-based modeling
• To support semistructured business decisions
20. 9- 20
DSS Model base
• Model base
– A software component that consists of models used in
computational and analytical routines that
mathematically express relations among variables
• Examples:
– Linear programming models,
– Multiple regression forecasting models
– Capital budgeting present value models
21. 9- 21
Management Information
Systems
• MIS
• Produces information products that support
many of the day-to-day decision-making needs
of managers and business professionals
• Prespecified reports, displays and responses
• Support more structured decisions
22. 9- 22
MIS Reporting Alternatives
• Periodic Scheduled Reports
– Prespecified format on a regular basis
• Exception Reports
– Reports about exceptional conditions
– May be produced regularly or when exception occurs
• Demand Reports and Responses
– Information available when demanded
• Push Reporting
– Information pushed to manager
23. 9- 23
Online Analytical Processing
• OLAP
– Enables mangers and analysts to examine and
manipulate large amounts of detailed and consolidated
data from many perspectives
– Done interactively in real time with rapid response
24. 9- 24
OLAP Analytical Operations
• Consolidation
– Aggregation of data
• Drill-down
– Display detail data that comprise consolidated data
• Slicing and Dicing
– Ability to look at the database from different viewpoints
26. 9- 26
Geographic Information Systems
• GIS
– DSS that uses geographic databases to construct and
display maps and other graphics displays
– That support decisions affecting the geographic
distribution of people and other resources
– Often used with Global Position Systems (GPS)
devices
27. 9- 27
Data Visualization Systems
• DVS
– DSS that represents complex data using interactive
three-dimensional graphical forms such as charts,
graphs, and maps
– DVS tools help users to interactively sort, subdivide,
combine, and organize data while it is in its graphical
form.
28. 9- 28
Using DSS
• What-if Analysis
– End user makes changes to variables, or relationships
among variables, and observes the resulting changes in
the values of other variables
• Sensitivity Analysis
– Value of only one variable is changed repeatedly and
the resulting changes in other variables are observed
29. 9- 29
Using DSS
• Goal-Seeking
– Set a target value for a variable and then repeatedly
change other variables until the target value is achieved
– How can analysis
• Optimization
– Goal is to find the optimum value for one or more target
variables given certain constraints
– One or more other variables are changed repeatedly
until the best values for the target variables are
discovered
30. 9- 30
Data Mining
• Main purpose is to provide decision support to
managers and business professionals through
knowledge discovery
• Analyzes vast store of historical business data
• Tries to discover patterns, trends, and
correlations hidden in the data that can help a
company improve its business performance
• Use regression, decision tree, neural network,
cluster analysis, or market basket analysis
31. 9- 31
Market Basket Analysis
• One of most common data mining for marketing
• The purpose is to determine what products
customers purchase together with other products
32. 9- 32
Executive Information Systems
• EIS
– Combine many features of MIS and DSS
– Provide top executives with immediate and easy access
to information
– About the factors that are critical to accomplishing an
organization’s strategic objectives (Critical success
factors)
– So popular, expanded to managers, analysts and other
knowledge workers
33. 9- 33
Features of an EIS
• Information presented in forms tailored to the
preferences of the executives using the system
– Customizable graphical user interfaces
– Exception reporting
– Trend analysis
– Drill down capability
34. 9- 34
Enterprise Interface Portals
• EIP
– Web-based interface
– Integration of MIS, DSS, EIS, and other technologies
– Gives all intranet users and selected extranet users
access
– To a variety of internal and external business
applications and services
• Typically tailored to the user giving them a
personalized digital dashboard
36. 9- 36
Knowledge Management
Systems
• The use of information technology to help
gather, organize, and share business knowledge
within an organization
• Enterprise Knowledge Portals
– EIPs that are the entry to corporate intranets that serve
as knowledge management systems
38. 9- 38
Case 2: Harrah’s Entertainment,
LendingTree, DeepGreen Financial, and
Cisco Systems:
• The promise of AI of automating decision making has been
very slow to materialize.
• The new generation AI applications are easier to create and
manage, do not require anyone to identify the problems or to
initiate the analysis, decision-making capabilities are
embedded into the normal flow of work, and are triggered
without human intervention.
• They sense online data or conditions, apply codified
knowledge or logic and make decisions with minimal human
intervention.
• But they rely on experts and managers to create and maintain
rules and monitor the results.
• Also, managers in charge of automated decision systems
must develop processes for managing exceptions.
39. 9- 39
Case Study Questions
1. Why did some previous attempts to use artificial
intelligence technologies fail? What key
differences of the new AI-based applications
versus the old cause the authors to declare that
automated decision making is finally coming of
age?
2. What types of decisions are best suited for
automated decision making? Provide several
examples of successful applications from the
companies in this case to illustrate your answer.
40. 9- 40
Case Study Questions
3. What role do humans play in automated
decision making applications? What are some
of the challenges faced by managers where
automated decision-making systems are being
used? What solutions are needed to meet
such challenges?
41. 9- 41
Real World Internet Activity
1. Use the Internet to find examples of companies
that are using automated decision making or
other business applications of artificial
intelligence. You might begin by looking for
such information on the companies mentioned
in this case and their main competitors, and
then widen your search to encompass other
companies. What business benefits or
challenges do you discover?
42. 9- 42
Real World Group Activity
2. Artificial intelligence applications in business
such as automated decision making pose
potential business risks, as evidenced by the
Cisco Systems experience, and have the
potential for other risks to business and human
security and safety, for example.
– Discuss such risks and propose controls and
safeguards to lessen the possibility of such
occurrences.
43. 9- 43
Artificial Intelligence (AI)
• A field of science and technology based on
disciplines such as computer science, biology,
psychology, linguistics, mathematics, and
engineering
• Goal is to develop computers that can simulate
the ability to think, as well as see, hear, walk,
talk, and feel
44. 9- 44
Attributes of Intelligent Behavior
• Think and reason
• Use reason to solve problems
• Learn or understand from experience
• Acquire and apply knowledge
• Exhibit creativity and imagination
• Deal with complex or perplexing situations
• Respond quickly and successfully to new
situations
• Recognize the relative importance of elements in
a situation
• Handle ambiguous, incomplete, or erroneous
information
46. 9- 46
Cognitive Science
• Based in biology, neurology, psychology, etc.
• Focuses on researching how the human brain
works and how humans think and learn
47. 9- 47
Robotics
• Based in AI, engineering and physiology
• Robot machines with computer intelligence and
computer controlled, humanlike physical
capabilities
48. 9- 48
Natural Interfaces
• Based in linguistics, psychology, computer
science, etc.
• Includes natural language and speech
recognition
• Development of multisensory devices that use a
variety of body movements to operate computers
• Virtual reality
– Using multisensory human-computer interfaces that
enable human users to experience computer-simulated
objects, spaces and “worlds” as if they actually exist
49. 9- 49
Expert Systems
• ES
• A knowledge-based information system (KBIS)
that uses its knowledge about a specific,
complex application to act as an expert
consultant to end users
• KBIS is a system that adds a knowledge base to
the other components on an IS
50. 9- 50
Expert System Components
• Knowledge Base
– Facts about specific subject area
– Heuristics that express the reasoning procedures of an
expert (rules of thumb)
• Software Resources
– Inference engine processes the knowledge and makes
inferences to make recommend course of action
– User interface programs to communicate with end user
– Explanation programs to explain the reasoning process
to end user
52. 9- 52
Methods of Knowledge
Representation
• Case-Based – knowledge organized in form of
cases
– Cases: examples of past performance, occurrences
and experiences
• Frame-Based – knowledge organized in a
hierarchy or network of frames
– Frames: entities consisting of a complex package of
data values
53. 9- 53
Methods of Knowledge
Representation
• Object-Based – knowledge organized in network
of objects
– Objects: data elements and the methods or processes
that act on those data
• Rule-Based – knowledge represented in rules
and statements of fact
– Rules: statements that typically take the form of a
premise and a conclusion
– Such as, If (condition) then (conclusion)
54. 9- 54
Expert System Benefits
• Faster and more consistent than an expert
• Can have the knowledge of several experts
• Does not get tired or distracted by overwork or
stress
• Helps preserve and reproduce the knowledge of
experts
55. 9- 55
Expert System Limitations
• Limited focus
• Inability to learn
• Maintenance problems
• Developmental costs
• Can only solve specific types of problems in a
limited domain of knowledge
56. 9- 56
Suitability Criteria for Expert
Systems
• Domain: subject area relatively small and limited to well-
defined area
• Expertise: solutions require the efforts of an expert
• Complexity: solution of the problem is a complex task that
requires logical inference processing (not possible in
conventional information processing)
• Structure: solution process must be able to cope with ill-
structured, uncertain, missing and conflicting data
• Availability: an expert exists who is articulate and
cooperative
57. 9- 57
Development Tool
• Expert System Shell
– Software package consisting of an expert system
without its knowledge base
– Has inference engine and user interface programs
58. 9- 58
Knowledge Engineer
• A professional who works with experts to capture
the knowledge they possess
• Builds the knowledge base using an iterative,
prototyping process
59. 9- 59
Neural Networks
• Computing systems modeled after the brain’s
mesh-like network of interconnected processing
elements, called neurons
• Interconnected processors operate in parallel
and interact with each other
• Allows network to learn from data it processes
60. 9- 60
Fuzzy Logic
• Method of reasoning that resembles human
reasoning
• Allows for approximate values and inferences
and incomplete or ambiguous data instead of
relying only on crisp data
• Uses terms such as “very high” rather than
precise measures
61. 9- 61
Genetic Algorithms
• Software that uses
– Darwinian (survival of the fittest), randomizing, and
other mathematical functions
– To simulate an evolutionary process that can yield
increasingly better solutions to a problem
62. 9- 62
Virtual Reality (VR)
• Computer-simulated reality
• Relies on multisensory input/output devices such
as
– a tracking headset with video goggles and stereo
earphones,
– a data glove or jumpsuit with fiber-optic sensors that
track your body movements, and
– a walker that monitors the movement of your feet
63. 9- 63
Intelligent Agents
• A software surrogate for an end user or a
process that fulfills a stated need or activity
• Uses its built-in and learned knowledge base
• To make decisions and accomplish tasks in a
way that fulfills the intentions of a user
• Also called software robots or bots
64. 9- 64
User Interface Agents
• Interface Tutors – observe user computer
operations, correct user mistakes, and provide
hints and advice on efficient software use
• Presentation – show information in a variety of
forms and media based on user preferences
• Network Navigation – discover paths to
information and provide ways to view information
based on user preferences
• Role-Playing – play what-if games and other
roles to help users understand information and
make better decisions
65. 9- 65
Information Management Agents
• Search Agents – help users find files and
databases, search for desired information, and
suggest and find new types of information
products, media, and resources
• Information Brokers – provide commercial
services to discover and develop information
resources that fit the business or personal needs
of a user
• Information Filters – receive, find, filter, discard,
save, forward, and notify users about products
received or desired
66. 9- 66
Case 3: IBM, Linden Labs, and Others: The
Business Case for Virtual Worlds in a 3D
Internet
• Second Life is a 3-D virtual world entirely built and owned by
its Residents.
• Since opening to the public in 2003, it has grown explosively
and today it is inhabited by more than eight million residents
from around the globe.
• It is catching the attention of many companies because of
it’s ability to use as a platform for a whole new Net with huge
opportunities to sell products and services.
• It is also possible to exchange Second Life’s currency,
called Linden dollars, for the real currency for a fee.
• Residents could thus build, own, or sell their digital
creations.
• Second Life has become a real economy.
67. 9- 67
Case Study Questions
1. What are the most important business benefits
and limitations of 3D virtual worlds like Second
Life to real-world companies such as those
mentioned in this case?
2. Why do you think IBM is taking a leadership
role in promoting and using 3D metaverses like
Second Life? What business benefits might it
expect to gain from its involvement in
developing a 3D Internet? Explain your
reasoning.
68. 9- 68
Case Study Questions
3. Are 3D virtual worlds like Second Life
“solutions in search of a problem” at this stage
of their development, in that do not satisfy any
vital business need? Why or why not?
69. 9- 69
Real World Internet Activity
1. Search the Internet to determine how Second
Life, Linden Labs, IBM, and other companies
mentioned in this case are doing in terms of
the growth and business success of their
development or use of 3D virtual worlds. Have
new competitors successfully entered the 3D
Internet market? If so, how do they differ in the
products and services they offer?
70. 9- 70
Real World Group Activity
2. Visit the Second Life Web site and evaluate
the experience in terms of level of difficulty,
response times, operation of basic functions,
realism, and so forth. Are 3D virtual worlds like
Second Life ready for widespread use as an
important form of social networking? How
could they improve what they offer to make it
more appealing and successful? Debate these
issues.
Editor's Notes
Need information systems to support decisions of all types. Decision support systems support the unstructured with unscheduled ad hoc reports Management information systems support the more structured with prespecified reports
Example is a Web-enabled marketing DSS. Note the hardware, software, model, data and network resources involved.
Unlike an MIS, DSS contains a model base as well as a database