S UPPORTING D ECISION M AKING Chapter (12 8 E, International) Information Systems Management In...
-
Upload
gwendolyn-owen -
Category
Documents
-
view
224 -
download
2
Transcript of S UPPORTING D ECISION M AKING Chapter (12 8 E, International) Information Systems Management In...
SUPPORTING DECISION MAKING
Chapter (12 8E, International)
Information Systems Management In Practice 8EMcNurlin & Sprague
PowerPoints prepared by Michael MatthewVisiting Lecturer, GACC, Macquarie University – Sydney Australia
PART IV: SYSTEMS FOR SUPPORTING KNOWLEDGE-BASED WORK This part consists of three chapters that discuss
supporting three kinds of work – decision making, collaboration, and knowledge work
As shown in the book’s framework figure, we distinguish between procedure-based and knowledge-based information-handling activities
The two previous chapters, in Part III, dealt mainly with building systems for procedure-based work
This part focuses on supporting knowledge-based activities: the systems that support people in performing information-handling activities to solve problems, work together, and share expertise
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-2
PART IV: SYSTEMS FOR SUPPORTING KNOWLEDGE-BASED WORK CONT.
Chapter 12 discusses supporting decision making by first presenting five underlying technologies and some examples of their use: Decision Support Systems (DSS) Data Mining Executive Information Systems (EIS), and Expert Systems (ES) Agent-based Modelling
The chapter then discusses the fascinating subject of the real-time enterprise, which has a goal of gaining competitive edge by learning of an event as soon as possible and then responding to that event quickly, if necessary
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-4
CHAPTER 12 This lecture / chapter discusses technologies for
supporting decision making: Decision Support Systems (DSS) Data Mining Executive Information Systems (EIS), and Expert Systems Agent-based Modelling
It then discusses IT issues related to creating the real-time enterprise
Case examples include: a problem-solving scenario, Ore-Ida Foods, a major services company, Harrah’s Entertainment, Xerox Corporation, General Electric, American Express, Delta Air Lines, a real-time interaction on a website, and Western Digital
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-5
INTRODUCTION
Most computer systems support decision making because all software programs involve automating decision steps that people would take
Decision making is a process that involves a variety of activities, most of which handle information
A wide variety of computer-based tools and approaches can be used to confront the problem at hand and work through its solution
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-6
A PROBLEM-SOLVING SCENARIOCASE EXAMPLE – SUPPORTING DECISION MAKING
Using an executive information system, (EIS) to compare budget to actual sales
Discover a sale shortfall in one region
Searches for the cause of the shortfall
But couldn’t find an answer
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-7
A PROBLEM-SOLVING SCENARIOCASE EXAMPLE – SUPPORTING DECISION MAKING CONT.
Investigate – several possible causes Economic Conditions – through the EIS & the Web
accesses: Wire services Bank economic newsletters Current business and economic publications
Competitive Analysis – through the same sources investigates whether competitors: Have introduced a new product Have launched an effective ad campaign
Written Sales Report – browses the reports “Concept based” text retrieval system makes this easier
A Data Mining Analysis Looking for any previously unknown relationships
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-8
A PROBLEM-SOLVING SCENARIOCASE EXAMPLE – SUPPORTING DECISION MAKING CONT.
Then accesses a marketing DSS – includes a set of models to analyze sales patterns by: Product Sales representative Major customer
Result – no clear problems revealed.Action – hold a meeting, in an electronic meeting
room supported by group DSS (GDSS) software
This scenario illustrates: The wide variety of activities involved in problem
solving, and The wide variety of technologies that can be used to
assist decision makers and problem solvers
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-9
TECHNOLOGIES THAT SUPPORT DECISION MAKING
The purpose of tractors, engines, machines etc. = to enhance humans’ physical capabilities
The purpose of computers has been to enhance our mental capabilities
Hence, a major use of IT is to relieve humans of some decision making or help us make more informed decisions
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-10
TECHNOLOGIES THAT SUPPORT DECISION MAKING DECISION SUPPORT SYSTEMS
Systems that support, not replace, managers in their decision-making activities
Decision modeling, decision theory, and decision analysis, attempt to make models from which the ‘best decision’ can be derived, by computation
DSS are defined as: Computer-based systemsThat help decision makersConfront ill-structured problemsThrough direct interactionWith data and analysis models
Wide range of technologies can be used to assist decision makers and problem solvers
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-11
DECISION SUPPORT SYSTEMS THE ARCHITECTURE FOR DSSS
Figure 11-1 shows the relationship between the three components of the DDM model
Software system in the middle of the figure consists of:The database management system (DBMS)The model base management system (MBMS)The dialog generation and management
system (DGMS)
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-12
DECISION SUPPORT SYSTEMS THE ARCHITECTURE FOR DSSS CONT.
The Dialog Component The DSS contains a dialog component to link the user to the
system Was ‘mouse’ (Mac) now = browser interface
The Data Component Data sources – as the importance of DSS has grown, it has
become increasingly critical for the DSS to use all the important data sources within and outside the organization
Data warehousing Data mining
Much of the work on the data component of DSS has taken the form of activities in this area
The Model Component Models provide the analysis capabilities for a DSS
Using a mathematical representation of the problem, algorithmic processes are employed to generate information to support decision making
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-14
DECISION SUPPORT SYSTEMS TYPES OF DSS The size and complexity of DSS range from large
complex systems that have many of the attributes of major applications down to simple ad hoc analyses that might be called end user computing tasks
Institutional DSSs tend to be fairly well defined They are based on pre=defined data sources
Heavily internal with perhaps some external data Use well established models in a prescheduled way
Quick-hit DSSs are developed quickly to help a manager make either a one-time decision or a recurring one
Can be every bit as useful for a small or large company Most today = Excel spreadsheets (and not ‘called’ DSS)
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-15
ORE-IDA FOODSCASE EXAMPLE – INSTITUTIONAL DSS
Frozen food division of H.J. Heinz Marketing DSS must support 3 main tasks in the
decision making process:1. Data retrieval – helps managers find answers to the
question, “what has happened?”2. Market analysis – addresses the question, “Why did it
happen?”3. Modeling – helps managers get answers to, “What will
happen if…?” Modeling for projection purposes, offers the
greatest potential value of marketing management For successful use – line managers must take over
the ownership of the models and be responsible for keeping them up-to-date
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-16
A MAJOR SERVICES COMPANYCASE EXAMPLE – “QUICK HIT” DSS – SHORT ANALYSIS PROGRAMS
Considering – new employee benefit program: an employee stock ownership plan (ESOP).
Wanted a study made to determine the possible impact of the ESOP on the company and to answer such questions as: How many shares of company stock will be needed in
10,20 and 30yrs to support the ESOP? What level of growth will be needed to meet these stock
requirements? The information systems manager wrote a
program to perform the calculations & printed the results
Results = showed the impact of the ESOP over a 30yr period Surprising results
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-17
TECHNOLOGIES THAT SUPPORT DECISION MAKING DATA MINING
A promising use of data warehouses is to let the computer uncover unknown correlations by searching for interesting patterns, anomalies, or clusters of data that people are unaware exist
Called data mining, its purpose is to give people new insights into data
Also covered in Chapter 7
Most frequent type of data mined = customer data
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-18
HARRAH’S ENTERTAINMENTCASE EXAMPLE – DATA MINING (CUSTOMER)
To better know its customers, Harrah’s encourages them to sign up for its frequent-gambler card, Total Rewards
Harrah’s mined its Total Rewards database to uncover patterns and clusters of customers
It has created 90 demographic clusters, each of which is sent different direct mail offers – encouraging them to visit other Harrah’s casinos Profit and loss for each customer calculating the likely
‘return’ for every ‘investment’ it makes in that customer
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-19
HARRAH’S ENTERTAINMENTCASE EXAMPLE – DATA MINING (CUSTOMER) CONT.
Much of its $3.7B in revenues (and 80% of its profits) comes from its slot machines and electronic gaming-machine playersFound = locals who played often
It was not the ‘high rollers’ who were the most profitable
Within the first two years of operation of Total Rewards, revenue from customers who visited more than one Harrah’s casino increased by $100 million
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-20
TECHNOLOGIES THAT SUPPORT DECISION MAKING EXECUTIVE INFORMATION SYSTEMS (EIS)
As the name implies EISs are for use by executives
They have been used for the following purposes:
1. Gauge company performance: sales, production, earnings, budgets, and forecasts
2. Scan the environmental: for news on government regulations, competition, financial and economics developments, and scientific subjects
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-21
TECHNOLOGIES THAT SUPPORT DECISION MAKING EXECUTIVE INFORMATION SYSTEMS (EIS) CONT.
EIS can be viewed as a DSS that:1. Provides access to summary performance
data2. Uses graphics to display and visualize the
data in an easy-to-use fashion, and 3. Has a minimum of analysis for modeling
beyond the capability to “drill down” in summary data to examine components
In many companies, the EIS is called a dashboard and may look like a dashboard of a car
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-22
XEROX CORPORATIONCASE EXAMPLE – EXECUTIVE INFORMATION SYSTEM
The EIS at Xerox began small and evolved to the point where even skeptical users became avid supporters
Its objective was to improve communications and planning, such as giving executives pre-meeting documents
It was also used in strategic planning and resulted in better plans, especially across divisions
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-23
EXECUTIVE INFORMATION SYSTEMS (EIS) PITFALLS IN EIS DEVELOPMENT
1. Lack of executive support: executives must provide the funding, but are the principal users and supply the needed continuity
2. Undefined system objectives: the technology, the convenience, and the power of EIS are impressive, but the underlying objectives and business values of an EIS must be carefully thought through
3. Poorly defined information requirements: EIS typically need non - traditional information sources - judgments, opinion, external text-based documents - in addition to traditional financial and operating data
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-24
EXECUTIVE INFORMATION SYSTEMS (EIS) PITFALLS IN EIS DEVELOPMENT CONT.
4. Inadequate support staff: support staff must: Have technical competence Understand the business, and Have the ability to relate to the varied responsibilities and work
patterns of executives
5. Poorly planned evolution: highly competent system professionals using the wrong development process will fail with EIS
EIS are not developed, delivered, and then maintained They should evolve over a period of time under the leadership
of a team that includes: The executive sponsor The operating sponsor Executive users The EIS support staff manager, and The IS technical staff
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-25
EXECUTIVE INFORMATION SYSTEMS (EIS)WHY INSTALL AN EIS?
Attack a critical business need: EIS can be viewed as an aid to dealing with important needs that involve the future health of the organization
A strong personal desire by the executive: The executive sponsoring the project may Want to get information faster than he/she is now getting
it, or Have a quicker access to a broader range of information, or Have the ability to select and display only desired
information and to probe for supporting detail, or To see information in graphical form
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-26
EXECUTIVE INFORMATION SYSTEMS (EIS) A WEAK REASON TO INSTALL AN EIS
“The thing to do”: An EIS is seen as something that modern management must have, in order to be current in management practices
The rationale given is that the EIS will increase executive performance and reduce time that is wasted looking for information and by such things as telephone tag
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-27
EXECUTIVE INFORMATION SYSTEMS (EIS) WHAT SHOULD THE EIS DO?
A Status Access System: Filter, extract, and compress a broad range of up-to-date internal and external information
It should call attention to variances from plan. It should also monitor and highlight the
critical success factors of the individual executive user
EIS is a structured reporting system for executive management, providing the executive with the data and information of choice and desired form
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-28
GENERAL ELECTRICCASE EXAMPLE – EXECUTIVE INFORMATION SYSTEM
Most senior GE executives have a real-time view of their portion of GE via an executive dashboard Each dashboard compares expected goals (sales,
response times, etc) with actual, alerting the executive when gaps of a certain magnitude appear
GE’s goal is to gain better visibility into all its operations in real time and give employees a way to monitor corporate operations quickly and easily
The system is based on complex enterprise software that interlinks existing systems
GE’s actions are also moving its partners and business ecosystem closer to real-time operation
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-29
TECHNOLOGIES THAT SUPPORT DECISION MAKING EXPERT SYSTEMS
A real-world use of artificial intelligence (AI) AI is a group of technologies that attempts to mimic our
senses and emulate certain aspects of human behavior such as reasoning and communication
Promising for 40 years +. Now = finally living up to promise
An expert system is an automated type of analysis or problem-solving model that deals with a problem the way an “expert” does
Note: Expert Systems are not new LISP Prolog
Languages in the ’70s
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-30
TECHNOLOGIES THAT SUPPORT DECISION MAKING EXPERT SYSTEMS CONT.
The process involves consulting a base of knowledge or expertise to reason out an answer based on the characteristics of the problem
Like DSSs, they have: A user interface An inference engine, and Stored expertise (in the form of a knowledge
base) The inference engine is that portion of the
software that contains the reasoning methods used to search the knowledge base and solve the problem
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-31
EXPERT SYSTEMSKNOWLEDGE REPRESENTATION
Knowledge can be represented in a number of ways:1. One is as cases; case-based reasoning expert
systems using this approach draw inferences by comparing a current problem (or case) to hundreds or thousands of similar past cases
2. A second form is neural networks, which store knowledge as nodes in a network and are more intelligent than the other forms of knowledge representation because they can learn
3. Third, knowledge can be stored as rules (the most common form of knowledge representation), which are obtained from experts drawing on their own expertise, experience, common sense, ways of doing business, regulations, and laws
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-32
AMERICAN EXPRESSCASE EXAMPLE – EXPERT SYSTEM One of the first commercially successful ESs and
a fundamental part of the company’s everyday credit card operation
Authorizer’s Assistant is an expert system that approves credit at the point of sale
It has over 2,600 rules and supports all AmEx card products around the world
Authorizes credit by looking at: Whether cardholders are creditworthy Whether they have been paying their bills Whether a purchase is within their normal spending
patterns It also assesses whether the request for credit
could be a potential fraud
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-34
AMERICAN EXPRESSCASE EXAMPLE – EXPERT SYSTEM CONT.
The most difficult credit-authorization decisions are still referred to people
Avoids ‘sensitive’ transactionsRestaurantsAirline queues
The rules were generated by interviewing authorizers with various levels of expertise – comparing good decisions to poor decisions
The system can be adapted quickly to meet changing business requirements
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-35
EXPERT SYSTEMSDEGREE OF EXPERTISE
1. As an assistant, the lowest level of expertise, the expert system can help a person perform routine analysis and point out those portions of the work where the expertise of the human is required
2. As a colleague, the second level of expertise, the system and the human can “talk over” the problem until a “joint decision” has been reached
3. As an expert, the highest level of expertise, the system gives answers that the user accepts, perhaps without question
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-36
AGENT –BASED MODELLING
A simulation technology for studying emergent behaviour (e.g. traffic jam) that emerges from the decisions of a large number of distinct individuals (drivers) Simulation contains computer generated agents, each
making decisions typical of the decisions an individual would make in the real world Trying to understand the mysteries of why businesses,
markets, consumers, and other complex systems behave as they do
Some examples: Nasdaq; Change its tick size Retailer = redesign its incentive program Southwest Airlines = revamp its cargo operations Company changing its recruiting practices
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-37
TECHNOLOGIES THAT SUPPORT DECISION MAKING CONCLUSION
This section has discussed five seemingly competing technologies that support decision making
In reality they often overlap and combine
The next section demonstrates how these decision support technologies and other technologies are being mixed and matched to form the foundation for the real-time enterprise
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-38
TOWARD THE REAL-TIME ENTERPRISE
Through IT, organizations have been able to see the status of operations more and more toward real time
The Internet is giving companies a way to disseminate closer-to-real-time information about events
The essence of the phrase real-time enterprise is that organizations can know how they are doing at the moment, rather than have to wait days, weeks, or months for analysis results
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-39
TOWARD THE REAL-TIME ENTERPRISE CONT.
It is occurring on a whole host of fronts, including: Enterprise nervous systems
To coordinate company operations Straight-through processing
To reduce distortion in supply chains Real-time CRM
To automate decision making relating to customers, and Communicating objects
To gain real-time data about the physical world
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-40
TOWARD THE REAL-TIME ENTERPRISE
ENTERPRISE NERVOUS SYSTEMS These are the technical means to a real-time
enterprise They are:
Message based - because sending messages is efficient and effective in dispersing information among parties simultaneously
Event driven - when an event occurs, it is recorded and made available
Use a publish and subscribe approach - the event is “published” to an electronic address and any system, person, or device authorized to see that information can “subscribe” to that address’s information feed, and
Use common data formats - data formats from disparate systems are reduced to common denominators that can be understood by other systems and hence shared
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-41
DELTA AIRLINESCASE EXAMPLE – ENTERPRISE NERVOUS SYSTEMS
Delta has built an enterprise nervous system to manage its gate operations by incorporating the disparate systems the airline had in the late 1990s
Information about each flight is managed by the system, in real time, and everyone who needs to know about a change can get the data
The system uses a publish-and-subscribe approach using enterprise application integration (EAI) products, whereby the messaging middleware allows disparate applications to share data
When an event occurs, it ripples to everyone
Delta is now expanding those ripples out to their partners who serve their passengers, such as caterers and security companies
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-42
TOWARD THE REAL-TIME ENTERPRISESTRAIGHT-THROUGH PROCESSING
The notion of a real-time enterprise has generated two “buzzwords”
One is zero latency, which means reacting quickly to new information (with no wait time)
The second is straight-through processing, which means that transaction data are entered just once in a process or a supply chain (like at Delta)
The goal is to reduce lags and latency in supply chains
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-43
TOWARD THE REAL-TIME ENTERPRISEREAL-TIME CRM
Another view of a real-time response might occur between a company and a potential customer- Perhaps via a customer call center or a Website
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-44
A REAL-TIME INTERACTION ON A WEB SITECASE EXAMPLE – REAL-TIME CRM
E.piphany CRM software example A potential guest visits the Website of a
hotel chain, checking for a hotel in OrlandoThe real-time CRM system initiates requests to
create a profile of the customer All past interactions with that customer Past billing information Past purchasing history
Using this information, it makes real-time offers to the Website visitor, and the visitor’s responses are recorded and taken into account for future Website visitors
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-45
TOWARD THE REAL-TIME ENTERPRISECOMMUNICATING OBJECTS These are sensors and tags that provide
information about the physical world via real-time data
A communicating object can tell you:What it is attached toWhere it is locatedWhere it belongs, and A lot more information about itself
It is a radio frequency identification device (RFID), also called “smart tags”Based on WW2 technology
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-47
TOWARD THE REAL-TIME ENTERPRISECOMMUNICATING OBJECTS CONT.
In Singapore, cars carry smart tags, and drivers are charged variable prices for where they drive in the city and whenThe prices are set to encourage or discourage
driving at different places at different timesAlso proposed for Sydney’s new toll ways
It’s an example of real-time traffic control
Smart tags will transform industries because they will talk to one another (object-to-object communication), changing how work is handled
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-48
TOWARD THE REAL-TIME ENTERPRISEVIGILANT INFORMATION SYSTEMS
The premise of the real-time enterprise is not only that it can capture data in real time, but that it has the means to act on that data quickly
US Air Force pilot = bet he could win any dogfightNever lost a bet, even to superior aircraftCalled his theory OODA
Observe where his challenger’s plane is Orient himself and size up his own vulnerabilities
and opportunities Decide which manoeuvre to take Act to perform it before the challenger could go
through the same four steps
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-49
WESTERN DIGITALCASE EXAMPLE: VIGILANT INFORMATION SYSTEMS (OODA)
PC disk manufacturer used OODA type of thinking to move itself closer to operating in real time with a sense-and-respond culture that aims to operate faster than its competitors
Built what they call a Vigilant Information System (VIS) which they define as a system that is “alertly watchful”Complex and builds on the firm’s legacy
systemsEssentially has four layers – Figure 11-5
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-50
WESTERN DIGITALCASE EXAMPLE: VIGILANT INFORMATION SYSTEMS (OODA) CONT.
VIS had to be complemented by appropriate business processes
To operate inside it competitors OODA loops Three new company policies were drafted
1. Company’s strategic goals must be translated into time based objectives and aligned across the company
2. KPIs must be captured in real time and be comparable3. Collaborative decision making to co-ordinate actions
company-wide Shop-Floor OODA loop Factory OODA loop Corporate OODA loop
Benefits of the VIS Quickened all 3 OODA loops and helped to link decisions
across them Corporate performance improved measurably
Margins doubled since introduction 3 years ago Sense and response culture where Western digital learns
and adapts quickly in a coordinated fashion
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-52
TOWARD THE REAL-TIME ENTERPRISETHE DARK SIDE OF REAL TIME What are the drawbacks of real-time
activities?Object-to-object communication could compromise
privacy, since knowing the exact location of a company truck every minute of the day and night can be construed as invading the driver’s privacy That’s a political issue, not a technical issue, and many
CEOs are going to face this question in the futureAlso, in the era of speed, a situation can become
very bad very fast, so people must be constantly watching for signals that something negative is likely to happen
Need for circuit breakers? e.g. NYSE
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-53
CONCLUSION Use of IT to support decision making
covers a broad swath of territory
Some technologies aim to alert people to anomalies, discontinuities, and shortfalls
Others aim to make decisions, either as recommendations to people or to act on behalf of people
Handing over decisions to systems has its pros and cons, thus their actions need to be monitored
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-54
CONCLUSION CONT.
CIOs need to alert their management team of potential social and economic effects of computer-based decision making because errant computer-based decisions have devastated corporate reputations and cost a lot of money
With vendors pushing toward the real-time enterprise, this is a use of computers that should give pause to explore the ramifications
©2006 B
arbara C. M
cNurlin.
Published by P
earson E
ducation.
11-55