bi and decision support
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Business Intelligence
and Decision Support
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Changing Business Environment
Companies are moving aggressively tocomputerized support of theiroperations => Business Intelligence
Business PressuresResponsesSupportModel
Business pressures result of today'scompetitive business climate
Responses to counter the pressures
Support to better facilitate the process2
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Business PressuresResponses
Support Model
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The Business Environment
The environment in whichorganizations operate today isbecoming more and more complex,creating:
opportunities, and
problems
Example: globalization
Business environment factors:
markets, consumer demands, technology,
and societal 4
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Business Environment Factors
FACTOR DESCRIPTIONMarkets Strong competition
Expanding global markets
Blooming electronic markets on the Internet
Innovative marketing methods
Opportunities for outsourcing with IT support
Need for real-time, on-demand transactions
Consumer Desire for customizationdemand Desire for quality, diversity of products, and speed of delivery
Customers getting powerful and less loyal
Technology More innovations, new products, and new services
Increasing obsolescence rate
Increasing information overload
Social networking, Web 2.0 and beyondSocietal Growing government regulations and deregulation
Workforce more diversified, older, and composed of more women
Prime concerns of homeland security and terrorist attacks
Necessity of Sarbanes-Oxley Act and other reporting-related legislation- corporate accountability & penalties for acts of wrongdoing
Increasing social responsibility of companies
Greater emphasis on sustainability 5
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Organizational Responses
Be Reactive, Anticipative, Adaptive,and Proactive
Managers may take actions, such as Employ strategic planning
Restructure business processes
Participate in business alliances
Improve partnership relationships Encourage innovation and creativity cont>
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Managers actions, continued
Improve customer service and relationships
Move to make-to-order production and on-demandmanufacturing and services
Respond quickly to competitors' actions (e.g., inpricing, promotions, new products and services)
Automate certain decision processes
Improve decision making by employing analytics
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Closing the Strategy Gap
One of the major objectives ofcomputerized decision support is tofacilitate closing the gap between thecurrent performance of an organizationand its desired performance, asexpressed in its mission, objectives, and
goals, and the strategy to achieve them
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Managerial Decision Making
Management is a process by whichorganizational goals are achieved byusing resources
Inputs: resources
Output: attainment of goals
Measure of success: outputs / inputs
Management Decision Making Decision making: selecting the best
solution from two or more alternatives9
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Mintzberg's 10 Managerial Roles
Interpersonal1. Figurehead
2. Leader
3. Liaison
Informational
4. Monitor5. Disseminator
6. Spokesperson
Decisional7. Entrepreneur8. Disturbance handler
9. Resource allocator
10. Negotiator
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A Decision Support Framework
Degree of Structuredness (Simon, 1977)
Decision are classified as
Highly structured (a.k.a. programmed)
Semi-structured Highly unstructured (i.e., non-programmed)
Types of Control (Anthony, 1965)
Strategic planning (top-level, long-range) Management control (tactical planning)
Operational control
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Computer Support for Structured
Decisions
Structured problems: encounteredrepeatedly, have a high level of structure
It is possible to abstract, analyze, andclassify them into specific categories e.g., make-or-buy decisions, capital
budgeting, resource allocation, distribution,
procurement, and inventory control For each category a solution approach is
developed => Management Science
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Management Science Approach
Also referred to as Operation Research
In solving problems, managers shouldfollow the five-step MS approach1. Define the problem
2. Classify the problem into a standard category (*)
3. Construct a model that describes the real-worldproblem
4. Identify possible solutions to the modeled problemand evaluate the solutions
5. Compare, choose, and recommend a potentialsolution to the problem
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Automated Decision Making
Applies to highly structured decisions
Automated decision systems (ADS)
(or decision automation systems)
An ADS is a rule-based system thatprovides a solution to a repetitivemanagerial problem in a specific area
e.g., simple-loan approval system
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Automated Decision Making
ADS initially appeared in the airlineindustry called revenue (or yield)management (or revenue optimization)
systems dynamically price tickets based on actual
demand
Today, many service industries usesimilar pricing models
ADS are driven by business rules!
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Computer Support for
Unstructured Decisions
Unstructured problems can be onlypartially supported by standardcomputerized quantitative methods
They often require customized solutions They benefit from data and information
Intuition and judgment may play a role
Computerized communication andcollaboration technologies along withknowledge management is often used
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Computer Support for
Semi-structured Problems
Solving semi-structured problems mayinvolve a combination of standardsolution procedures and human
judgment MS handles the structured parts while
DSS deals with the unstructured parts
With proper data and information, arange of alternative solutions, along withtheir potential impacts
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Concept of Decision Support
Systems
Classical Definitions of DSS
Interactive computer-based systems, which helpdecision makers utilize data and models to solve
unstructured problems" - Gorry and Scott-Morton, 1971
Decision support systems couple the intellectualresources of individuals with the capabilities of the
computer to improve the quality of decisions. It is acomputer-based support system for managementdecision makers who deal with semistructuredproblems - Keen and Scott-Morton, 1978
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DSS as an Umbrella Term
The term DSS can be used as an umbrellaterm to describe any computerizedsystem that supports decision making in
an organization E.g., an organization wide knowledge
management system; a decision support
system specific to an organizational function(marketing, finance, accounting,manufacturing, planning, SCM, etc.)
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Business Intelligence (BI)
BI is an umbrella term that combinesarchitectures, tools, databases, analyticaltools, applications, and methodologies
BI's major objective is to enable easy access todata (and models) to provide businessmanagers with the ability to conduct analysis
BI helps transform data, to information (andknowledge), to decisions and finally to action( cf ETL later)
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A Brief History of BI
The term BI was coined by the GartnerGroup in the mid-1990s
However, the concept is much older 1970s - MIS reporting - static/periodic reports
1980s - Executive Information Systems (EIS)
1990s - OLAP, dynamic, multidimensional, ad-hocreporting -> coining of the term BI
2005+ Inclusion of AI and Data/Text Miningcapabilities; Web-based Portals/Dashboards
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Components in a BI Architecture
The Data Warehouse is a large repository ofwell-organized historical data
Business analytics are the tools that allow
transformation of data into information andknowledge
Business performance management (BPM)allows monitoring, measuring, and comparing
key performance indicators
User interface (e.g., dashboards) allows accessand easy manipulation of other BI components
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Business Analytics
Business analytics is the use of models anddata to improve an organization's performanceand/or competitive advantage
Business analytics makes extensive use of data,statistical and quantitative analysis,explanatory and predictive modeling,and fact-based management to drive decision making
Analytics may be used as input for humandecisions or may drive fully automateddecisions
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Business Analytics
OLAP and other database tools can answerquestions such as what happened(description), how many, how often, where the
problem is, and what actions are needed Business analytics can answer questions such
as why is this happening, what if these trendscontinue, what will happen next (prediction),
what is the best that can happen and how canwe get there (optimization)
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Descriptive Analytics
The most traditional of business analytics isdescriptive analytics, and it accounts for themajority of all current business analytics
Descriptive analytics looks at past performanceand understands that performance by mininghistorical data to look for the reasons behindpast success or failure Almost all management reporting such as sales, marketing,
operations, and finance, uses this type of post-mortemanalysis
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Predictive Analytics
The next phase is predictive analytics This is when historical performance data is combined with
rules, algorithms, and occasionally external data to determinethe probable future outcome of an event or a likelihood of a
situation occurring Predictive Models in banking industry is widely developed to
bring certainty across the risk scores for individual customers.Credit Scores are built to predict individuals behaviour andalso scores are widely used to evaluate the credit worthiness
of each applicant and rated while processing loan applications Used in health care to determine which patients are at risk of
developing certain conditions, like diabetes, asthma, heartdisease, and other lifetime illnesses
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Prescriptive Analytics
The final phase is Prescriptive Analytics
Prescriptive analytics goes beyond predicting futureoutcomes by also suggesting actions to benefit from thepredictions and showing the decision maker the
implications of each decision option Prescriptive analytics not only anticipates what will happen
and when it will happen, but also why it will happen
Prescriptive analytics can suggest decision options on howto take advantage of a future opportunity or mitigate a
future risk and illustrate the implication of each decisionoption
prescriptive analytics can continually and automaticallyprocess new data to improve prediction accuracy and
provide better decision options 27
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DescriptiveAnalytics(Past)
PredictiveAnalytics(Future)
PrescriptiveAnalytics(Decision)
Phases of Business Analytics:
Dependencies
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Web Analytics
Web analytics implies using business analytics on real-time Web information to assist in decision making;often related to e-Commerce
Allows marketers to collect session-level information
about interactions on a website The interactions provide the web analytics information
systems with the information to track the referrer,search keywords, IP address, and activities of the
visitor With this information, a marketer can improve the marketing
campaigns, site creative content, and information architecture
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Web Analytics
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Big Data
Big data is a collection of data sets so large andcomplex that it becomes difficult to process usingtraditional data processing tools and applications
capture, curation, storage, search, sharing, analysis,
and visualization The trend to larger data sets is due to the additional
information derivable from analysis of a single largeset of related data, as compared to separate smaller
sets with the same total amount of data, allowingcorrelations to be found to e.g. spot business trends,determine quality of research, prevent diseases,combat crime, and determine real-time roadway
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Big Data
Limits on the size of data sets that are feasible toprocess in a reasonable amount of time are of the orderof exabytes (1018 bytes) of data Walmart handles more than 1 million customer transactions every
hour, which is imported into databases estimated to contain morethan 2.5 petabytes of data the equivalent of 167 times theinformation contained in all the books in the US Library of Congress
Facebook handles 40 billion photos from its user base
FICO Falcon Credit Card Fraud Detection System protects 2.1 billionactive accounts world-wide
What is the covariance between two time series
What if these time series are stock prices and on all trading days forthe past 5 years, and it is wished to obtain such correlation for eachpairing of 3,000 stocks?
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The Benefits of BI
The ability to provide accurate informationwhen needed, including a real-time view ofthe corporate performance and its parts
A survey by Thompson (2004) Faster, more accurate reporting (81%)
Improved decision making (78%)
Improved customer service (56%)
Increased revenue (49%)
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Characteristics of Decision Making
Groupthink group members accept the solution without thinking
for themselves can lead to bad decisions
Evaluating what-if scenarios Experimentation with a real system!
Changes in the decision-making environmentmay occur continuously
Time pressure on the decision maker
Analyzing a problem takes time/money
Insufficient or too much information34
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Characteristics of Decision Making
Better decisions Tradeoff: accuracy versus speed
Fast decision may be detrimental
Areas suffering most from fastdecisions personnel/human resources (27%)
budgeting/finance (24%) organizational structuring (22%)
quality/productivity (20%)
IT selection and installation (17%)
process improvement (17%) 35
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Decision Making
A process of choosing among two ormore alternative courses of action forthe purpose of attaining a goal(s)
e.g., Planning What should be done? When? Where?
Why? How? By whom?
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Decision Making and Problem
Solving
A problem occurs when a system
does not meet its established goals
does not yield the predicted results, or
does not work as planned
Problem is the difference between thedesired and actual outcome
Problem solving also involvesidentification of new opportunities
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Decision Making and Problem
Solving
Are problem solving and decision makingdifferent? Or, are they the same thing?
Consider the phases of the decision process
Phase (1)Intelligence
Phase (2) Design
Phase (3) Choice, and
Phase (4) Implementation (1)-(4): problem solving; (3): decision making
(1)-(3): decision making; (4): problem solving
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Decision Style
The manner by which decision makersthink and react to problems
perceive a problem
cognitive response
values and beliefs
When making decisions, people
follow different steps/sequence
give different emphasis, time allotment,and priority to each steps
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Decision Style
Decision-making styles
Heuristic versus Analytic
Autocratic versus Democratic
Consultative (with individuals or groups)
A successful computerized systemshould fit the decision style and thedecision situation
Should be flexible and adaptable todifferent users (individuals vs. groups)
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Decision Makers
Small organizations
Individuals
Conflicting objectives
Medium-to-large organizations
Groups
Different styles, backgrounds, expectations
Conflicting objectives
Consensus is often difficult to reach
Help: Computer support, GSS, 41
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Model
A significant part of many DSS and BIsystems
A model is a simplified representation
(or abstraction) of reality Often, reality is too complex to describe
Much of the complexity is actually
irrelevant in solving a specific problem
Models can represent systems/problemsat various degrees of abstraction
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Types of Models
Models can be classified based on theirdegree of abstraction
Iconic models (scale models)
Analog models (e.g. organizational chart)
Mental Models
Mathematical (quantitative) models
Degre
eofabstraction
Less
More
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The Benefits of Models
Ease of manipulation
Compression of time
Lower cost of analysis on models
Cost of making mistakes on experiments
Inclusion of risk/uncertainty
Evaluation of many alternatives Reinforce learning and training
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Phases of Decision-Making Process
Humans consciously or sub consciouslyfollow a systematic decision-makingprocess - Simon (1977)
1) Intelligence
2) Design
3) Choice
4) Implementation
5) (?) Monitoring (a part of intelligence?)
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Simons Decision-Making Process
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Decision-Making: Intelligence Phase
Scan the environment, either intermittentlyor continuously
Identify problem situations or opportunities
Monitor the results of the implementation
Problem is the difference between whatpeople desire (or expect) and what is actually
occurring Symptom versus Problem
Timely identification of opportunities is asimportant as identification of problems
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Decision-Making: Intelligence Phase
Potential issues in data/informationcollection and estimation
Lack of data
Cost of data collection
Inaccurate and/or imprecise data
Data estimation is often subjective
Data may be insecure
Key data may be qualitative
Data change over time (time-dependence)
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Decision-Making: Intelligence Phase
Problem Classification
Classification of problems according to the degreeof structuredness
Problem Decomposition Often solving the simpler subproblems may help
in solving a complex problem
Information/data can improve the structuredness
of a problem situation Problem Ownership
Outcome of intelligence phase:A Formal ProblemStatement
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Decision-Making: The Design Phase
Finding/developing and analyzing possiblecourses of actions
A model of the decision-making problem is
constructed, tested, and validated Modeling: conceptualizing a problem and
abstracting it into a quantitative and/orqualitative form (i.e., using symbols/variables)
Abstraction: making assumptions for simplification
Tradeoff (cost/benefit): more or less abstraction
Modeling: both an art and a science
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Decision-Making: The Design Phase
Selection of a Principle of Choice
It is a criterion that describes theacceptability of a solution approach
Reflection of decision-making objective(s)
In a model, it is the result variable
Choosing and validating against
High-risk versus low-risk Optimize versus satisfice
Criterion is not a constraint
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Decision-Making: The Design Phase
Normative models (= optimization) the chosen alternative is demonstrably the
best of all possible alternatives
Assumptions of rational decision makers Humans are economic beings whose objective is
to maximize the attainment of goals
For a decision-making situation, all alternative
courses of action and consequences are known Decision makers have an order or preference
that enables them to rank the desirability of allconsequences
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Decision-Making: The Design Phase
Heuristic models (= suboptimization) the chosen alternative is the best of only a
subset of possible alternatives
Often, it is not feasible to optimize realistic(size/complexity) problems
Suboptimization may also help relaxunrealistic assumptions in models
Help reach a good enough solution faster
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Decision-Making: The Design Phase
Descriptive models describe things as they are or as they are
believed to be (mathematically based)
They do not provide a solution butinformation that may lead to a solution
Simulation - most common descriptivemodeling method (mathematical depictionof systems in a computer environment)
Allows experimentation with the descriptivemodel of a system
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Decision-Making: The Design Phase
Good Enough, or Satisficingsomething less than the best
A form of suboptimization
Seeking to achieving a desired level ofperformance as opposed to the best
Benefit: time saving
Simons idea of bounded rationality
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Bounded Rationality
Herbert Simon - Nobel Prize in EconomicSciences (1978) decision makers do not optimize their decisions
find feasible decisions that are good enough
Limitations of: available data
processing capability
methods
intelligence level of decision makers
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Decision-Making: The Design Phase
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Decision-Making: The Design Phase
Developing (Generating) Alternatives In optimization models (such as linear
programming), the alternatives may be
generated automatically In most MSS situations, however, it is
necessary to generate alternatives manually
Use of GSS helps generating alternatives
Measuring/ranking the outcomes
Using the principle of choice
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Decision-Making: The Design Phase
Risk Lack of precise knowledge (uncertainty)
Risk can be measured with probability
Scenario (what-if case) A statement of assumptions about the
operating environment (variables) of a
particular system at a given time Possible scenarios: best, worst, most likely,
average (and custom intervals)
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Decision-Making: The Choice Phase
The actual decision and the commitment tofollow a certain course of action are made here
The boundary between the design and choice
is often unclear (partially overlapping phases) Generate alternatives while performing evaluations
Includes the search, evaluation, andrecommendation of an appropriate solution to
the model Solving the model versus solving the problem!
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Decision-Making: The Choice Phase
Search approaches Analytic techniques (solving with a formula)
Algorithms (step-by-step procedures)
Heuristics (rule of thumb) Blind search (truly random search)
Additional activities
Sensitivity analysis What-if analysis
Goal seeking
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Decision Making:
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Decision-Making:
The Implementation Phase
Nothing more difficult to carry out, normore doubtful of success, nor moredangerous to handle, than to initiate a
new order of things.- The Prince, Machiavelli 1500s
Solution to a problem = Change
Change management?
Implementation: putting arecommended solution to work
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How Decisions Are Supported
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How Decisions Are Supported
Support for the Intelligence Phase Enabling continuous scanning of external
and internal information sources to
identify problems and/or opportunities Resources/technologies: Web; ES, OLAP,
data warehousing, data/text/Web mining,EIS/Dashboards, KMS, GSS, GIS,
Business activity monitoring (BAM) Business process management (BPM)
Product life-cycle management (PLM)
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How Decisions Are Supported
Support for the Design Phase Enabling generating alternative courses of
action, determining the criteria for choice
Generating alternatives Structured/simple problems: standard and/or
special models
Unstructured/complex problems: human
experts, ES, KMS, brainstorming/GSS, OLAP,data/text mining
A good criteria for choice is critical!
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How Decisions Are Supported
Support for the Choice Phase Enabling selection of the best alternative
given a complex constraint structure
Use sensitivity analyses, what-if analyses,goal seeking
Resources
KMS
CRM, ERP, and SCM
Simulation and other descriptive models
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How Decisions Are Supported
Support for the Implementation Phase Enabling implementation/deployment of
the selected solution to the system
Decision communication, explanation andjustification to reduce resistance to change
Resources
Corporate portals, Web 2.0/Wikis
Brainstorming/GSS
KMS , ES
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DSS Description
An early definition of DSS A system intended to support managerial decision
makers in semistructured and unstructureddecision situations
meant to be adjuncts to decision makers(extending their capabilities but not replacingtheir judgment)
aimed at decisions that required judgment or at
decisions that could not be completely supportedby algorithms
would be computer based; operate interactively;and would have graphical output capabilities
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DSS Description
A DSS is typically built to support thesolution of a certain problem (or to evaluate aspecific opportunity). This is a key difference
between DSS and BI applications BI systems monitor situations and identify
problems and/or opportunities, using variety ofanalytic methods
The user generally must identify whether aparticular situation warrants attention
Reporting/data warehouse plays a major role inBI
DSS often has its own database and models 68
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DSS Description
DSS is an approach (or methodology) forsupporting decision making
uses an interactive, flexible, adaptable computer-based information system (CBIS)
developed (by end user) for supporting the solutionto a specific nonstructured management problem
uses data, model and knowledge along with afriendly (often graphical; Web-based) user interface
incorporate the decision maker's own insights
supports all phases of decision making
can be used by a single user or by many people
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Components of DSS
Data Management Subsystem Includes the database that contains the data
Database management system (DBMS)
Can be connected to a data warehouse Model Management Subsystem
Model base management system (MBMS)
Knowledgebase Management Subsystem Organizational knowledge base
User Interface Subsystem70
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Data Management Subsystem
DSS database
DBMS
Data directory Query facility
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Model Management Subsystem
Model base
MBMS
Model directory
Model execution,integration, andcommandprocessor
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Knowledgebase Management
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Knowledgebase Management
System
Incorporation of intelligence and expertise Knowledge components:
Expert systems,
Knowledge management systems, Neural networks,
Intelligent agents,
Fuzzy logic,
Case-based reasoning systems, and so on Often used to better manage the other DSS
components
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User Interface Subsystem
User InterfaceManagement System
DSS User Interface
Graphical icons Dashboard
Colour coding
Interfacing withmobile devices
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