Chapter 2:
Foundations and Technologies
for Decision Making
Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)
Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)
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Learning Objectives
Understand the conceptual foundations of decision making
Understand Simon’s four phases of decision making: intelligence, design, choice, and implementation
Understand the essential definition of decision support systems (DSS)
Understand different types of DSS classifications
(Continued…)
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Learning Objectives
Learn the capabilities and limitations of DSS in supporting managerial decisions
Learn how DSS support for decision making can be provided in practice
Understand DSS components and how they integrate
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Opening Vignette
Decision Modeling at HP Using Spreadsheets
Background
Problem description
Proposed solution
Results
Answer & discuss the case questions…
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Questions for the Opening Vignette
What are some of the key questions to be asked in supporting decision making through DSS?
What guidelines can be learned from this vignette about developing DSS?
What lessons should be kept in mind for successful model implementation?
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Characteristics of Decision Making
Groupthink
Evaluating what-if scenarios
Experimentation with a real system!
Changes in the decision-making environment may occur continuously
Time pressure on the decision maker
Analyzing a problem takes time/money
Insufficient or too much information
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Characteristics of Decision Making Decision Support Systems (DSS)
Dissecting DSS into its main concepts
Building successful DSS requires a thorough understanding of these concepts
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Decision Making
A process of choosing among two or more alternative courses of action for the purpose of attaining a goal(s)
Managerial decision making is synonymous with the entire management process – Simon (1977)
Example: Planning
What should be done? When? Where? Why? How? By whom?
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Decision-Making Disciplines
Behavioral: anthropology, law, philosophy, political science, psychology, social psychology, and sociology
Scientific: computer science, decision analysis, economics, engineering, the hard sciences (e.g., biology, chemistry, physics), management science/operations research, mathematics, and statistics
Each discipline has its own set of assumptions and each contributes a unique, valid view of how people make decisions
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Decision-Making Disciplines
Better decisions
Tradeoff: accuracy versus speed
Fast decision may be detrimental
Many areas suffer from fast decisions
Effectiveness versus Efficiency
Effectiveness “goodness” “accuracy”
Efficiency “speed” “less resources”
A fine balance is what is needed!
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Decision Style
The manner by which decision makers think 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 step
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Decision Style
Personality temperament tests are often used to determine decision styles
There are many such tests
Meyers/Briggs,
True Colors (Birkman),
Keirsey Temperament Theory, …
Various tests measure somewhat different aspects of personality
They cannot be equated!
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Decision Style
Decision-making styles
Heuristic versus Analytic
Autocratic versus Democratic
Consultative (with individuals or groups)
A successful computerized system should fit the decision style and the decision situation
Should be flexible and adaptable to different 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, …
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Phases of Decision-Making Process
Humans consciously or subconsciously follow a systematic decision-making process – Simon (1977)
Intelligence
Design
Choice
Implementation
(?) Monitoring (a part of intelligence?)
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Simon’s Decision-Making Process
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Decision Making: Intelligence Phase
Scan the environment, either intermittently or continuously
Identify problem situations or opportunities
Monitor the results of the implementation
Problem is the difference between what people desire (or expect) and what is actually occurring
Symptom versus Problem
Timely identification of opportunities is as important as identification of problems
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Decision Making: Intelligence Phase
Potential issues in data/information collection 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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Application Case 2.1
Making Elevators Go Faster!
Background
Problem description
Proposed solution
Results
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Decision Making: Intelligence Phase
Problem Classification
Classification of problems according to the degree of 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 Problem
Statement
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Web and the Decision-Making Process
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Decision Making: The Design Phase
Finding/developing and analyzing possible courses 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/or qualitative 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 the acceptability 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!
See Technology Insight 2.1
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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 or preference that enables them to rank the desirability of all consequences
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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 relax unrealistic 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 but information that may lead to a solution
Simulation – most common descriptive modeling method (mathematical depiction of systems in a computer environment)
Allows experimentation with the descriptive model of a system
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Decision Making: The Design Phase
Good Enough, or Satisficing
“something less than the best”
A form of suboptimization
Seeking to achieve a desired level of performance as opposed to the “best”
Benefit: time saving
Simon’s idea of bounded rationality
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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 generate 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 to follow 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, and recommendation 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: The Implementation Phase
“Nothing more difficult to carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new of things.”
– The Prince, Machiavelli 1500s
Solution to a problem Change
Change management ?..
Implementation: putting a recommended 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 and justification to reduce resistance to change
Resources
Corporate portals, Web 2.0/Wikis
Brainstorming/GSS
KMS, ES
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DSS Capabilities
DSS early definition: it is a system intended to support managerial decisions in semistructured and unstructured decision situations
DSS were meant to be adjuncts to decision makers extending their capabilities
They are computer based and would operate interactively online, and preferably would have graphical output capabilities
Nowadays, simplified via Web browsers and mobile devices
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DSS Capabilities
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DSS Classifications
AIS SIGDSS Classification
Communication-driven and group DSS
Data-driven DSS
Document-driven DSS
Knowledge-driven DSS
Model-driven DSS
Often DSS is a hybrid of many classes
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DSS Classifications
Other DSS Categories
Institutional and ad-hoc DSS
Custom-made systems versus ready-made systems
Personal, group, and organizational support
Individual support system versus group support system (GSS)…
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Components of DSS
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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)
User Interface Subsystem
Knowledgebase Management Subsystem
Organizational knowledge base
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DSS Components: Data Management Subsystem
DSS database
DBMS
Data directory
Query facility
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Application Case 2.2
Station Casinos Wins by Building Customer Relationships Using Its Data
Questions for Discussion
Why is this decision support system classified as a data-focused DSS?
What were some of the benefits from implementing this solution?
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DSS Components: Model Management Subsystem
Model base
MBMS
Modeling language
Model directory
Model execution, integration, and command processor
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Application Case 2.3
SNAP DSS Helps OneNet Make Telecommunications Rate Decisions
Background
Problem description
Proposed solution
Results
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DSS Components: User Interface Subsystem
Interface
Application interface
User Interface (GUI?)
DSS User Interface
Portal
Graphical icons
Dashboard
Color coding
Interfacing with PDAs, cell phones, etc.
See Technology Insight 2.2 for next gen devices
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End of the Chapter
Questions, comments
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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.
ITS 531: Business Intelligence
Week 1: Video Lecture
Lecture Objectives
During this lecture, we will:
Go over and introduce the class with tips on earning an “A” level grade.
Go over and introduce content for the class.
ITS 531: Earning Those “A” Level Grades
Getting Started (Read All Contents Within)
In the classroom, the course syllabus is located under “Content” and then under “Getting Started (Read All Contents Within).”
Other useful information is located in this area of the course including:
Biographical information about me
Place to post questions
As assignment calendar of due dates
Information about books and APA
And more.
As everyone explores introductory content, there will be “3” types of assessments in this including the following:
Graded Discussions
Graded Assignments
A Mid-Term Exam
A Final Exam
Let us now explore some tips on how to earn “A” level grades on all these types of assessments list above.
ITS 531: Discussions (Tips) —
During this course, there will be six discussion forums. Ideally, all students should always meet minimum expectations by doing the following:
Post an initial response and respond to at least two peers. Postings should be on time following the schedule provided in the classroom.
One or more postings should be supported with academic research and avoid postings that are generalized.
Exceeding minimum expectations will greatly increase chances earning “A” level grades when discussion forums are assessed for grading.
I want all my students to earn good grades so avoid incomplete or non-completed discussion boards within assigned discussion weeks.
ITS 531: Assignment (Tips) —
During this course, there will be six assignments allowing students to learn in both the theory and application approaches. Ideally, all students should always meet minimum expectations by doing the following:
Pay very close attention to page and research requirements when reading assignment instructions. This is important because I am very critical in grading when minimum expectations are not met.
Be illustrative by including any visualizations to support written content. Illustrations can account for approximately 25% of page requirements and at least 75% of content should be written.
When creating illustrations any drawing software can be used; however, it is recommended to use of “Smart Art” which is included in Microsoft Word. Other options could be Microsoft Visio.
Professionally format all assignments using APA including an APA cover page, abstract, body pages, and a reference Page.
Complete assignments by assignment due dates from the schedule provided in the classroom.
ITS 531: Exam (Tips) —
During this course, there will be two exams including a mid-term and final exam and the best way to prepare for these exams is by reading and reviewing both chapter readings and chapter PowerPoints.
These exams will have combinations of multiple choice and true/false questions.
Exam questions will be written based on content found in chapter readings and chapter PowerPoints.
Exams will be timed and must be completed in one sitting. Each student will have 120 minutes to complete at least 50 questions.
Multiple attempts will also be allowed up to the due date and these extra attempts are in place in case any student runs into any technical issues. It should be noted that exam results and grading will not be released until after the due date of the exam.
Exams should always be completed by the due dates, as documented, from the schedule of due dates found in the classroom.
ITS 531: Week 1 An Overview of Analytics, and AI
Week 1: An Introduction to Business Intelligence
As we get started with this class, the realm of analytics and business intelligence is basically the transformation of raw data from databases or other storage locations in efforts to transform and manipulate this data into usable business intelligence.
At the same time understanding this fact, there may be issues an organization or business may face in the data collection and data manipulation process. For example:
Data are not available. As a result, the model is made with and relies on potentially inaccurate estimates.
Obtaining data may be expensive and data may also be insecure.
Data may not be accurate or precise enough and data may be subjective in nature. In a way in these cases, data may also be qualitative in nature which can be more challenging to analyze.
There may also be too many data and most if not all of us is seeing more and more these days on the push for big data.
Week 1: An Introduction to Business Intelligence
Even with all the data collection issues just discussed, the motivation to get data and to transform data into usable business intelligence is present.
This is clearly evident because there are many benefits in the real of data analytics used for business and even artificial intelligence. For example:
An organization using aspects business intelligence will find significant reductions in the cost of performing work.
Another benefit of business an artificial intelligence is having information to help create ideas and processes where work can be performed much faster.
In a way, this improved efficiency will create more consistency in how any operational process in association with a work related tasks is performed.
Going even further, we could say that using business an artificial intelligence is a pathway toward environments that work smarter.
This as a return of investment would promote increased productivity and profitability as well as a competitive advantage are the major drivers.
Week 1: An Introduction to Business Intelligence
After understanding the motivation behind the needs for business intelligence, it becomes clear why we need this.
For example:
Management and leadership in various business environments are being exposed big data or data overloads.
Along with the data overload is the complexity of data also increasing making data analysis more challenging. For example, organizations today will experience graphical data in digital form, network structures, document sets, GPS measurement, and many other complex forms of data.
In other words, the complexity of data and the increasing need for shorter data analysis methods creates an environment that is getting bigger, more complicated, and faster.
In a way, this sounds good for productivity and efficiency; however, most of the digitally stored data in organizations relies on the relational database model, which is great for storing transactional data and not so good for analytical purposes.
As a result, we are seeing more aspects data warehouses and data lakes, for example, to store and manage internal and external data sources.
In this process to store and manage data, structuring and turning this informational data into knowledge or business and artificial intelligence is using aspects of predictive analytics.
Week 1: An Introduction to Business Intelligence
Other drivers and motivation behind the needs for business intelligence and even artificial intelligence include and is not limited to:
The interest and push for smart machines and artificial brains.
The correlation of reduced cost when using intelligent applications versus the high cost of manual labor. Of course, this will always create room to have good debates on concerns where technology is used to take away actual jobs.
The need of large technology companies to capture competitive advantages and market share of the intelligence market with the ability and will to invest pretty much any monetary value needed.
The pressure on management to increase productivity and speed. Furthermore in this case, using technology to replace manual labor could decrease liability as a result human labor. Again, good area for debate and discussion.
The availability of quality data contributing to the progress of business and artificial intelligence.
The increasing functionalities and reduced cost of computers in general
The development of new technologies, particularly cloud computing to support business and artificial intelligence.
Any others depending on custom circumstances and need.
Week 1: An Introduction to Business Intelligence
Other more specific drivers and motivation behind the needs for business and artificial intelligence is based on models associated with:
Streamlining processes, including minimizing waste, redesigning processes, and using business
Business Process Management (BPM)
Outsourcing certain business processes, including going offshore
Using intelligence in decision making by deploying artificial intelligence and technology driven analytic processing systems.
Replacing human tasks with intelligent automation.
Digitizing customersʹ experiences
Any others depending on custom circumstances and need.
Week 1: An Introduction to Business Intelligence
After understanding the continued need and motivation behind analytical data analysis or data analytics, we can see many skill sets need.
For example a data scientist or data analyst would ideally need skills in areas below not limited to and in no specific :
Statistical Inference
Operational Research
Regression and Time Series Analysis
Social Network Analysis
Complex Event Processing
Data Mining
Test Mining
Relational Database Management Systems
Big Data and Hadoop
SQL Databases
No SQL Databases
Business Activity Monitoring
Business Case Preparations
SDLC
Any many others as focused to specific work environment.
Week 1: An Introduction to Business Intelligence
The skill sets just covered are very important especially for business intelligence because components of a BI system include the following.
A data warehouse, with its source data
Business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse
Business performance management (BPM) for monitoring and analyzing performance
And a user interface like a user friendly digital dashboard.
Week 1: An Introduction to Business Intelligence
Business Intelligent Systems or BI will typically support the following types of analytics and other methods as needed.
Descriptive or reporting analytics which refers to knowing what is happening in the organization and understanding some underlying trends and causes of such occurrences.
Predictive analytics which aims to determine what is likely to happen in the future. This analysis is based on statistical techniques as well as other more recently developed techniques that fall under the general category of data mining.
Prescriptive analytics which recognizes what is going on as well as the likely forecast and make decisions to achieve the best performance possible.
Week 1: An Introduction to Business Intelligence
When thinking about an overview of analytics and artificial intelligence, we need to understand the need for computerized support of managerial decision making.
In this effort, we need to be aware that computer support can be used for structured, semi structured, and unstructured decisions. For example we have:
Structured Decisions: Structured problems, which are encountered repeatedly, have a high level of structure. It is therefore possible to abstract, analyze, and classify them into specific categories and use a scientific approach for automating portions of this type of managerial decision making.
Semistructured Decisions: Semistructured problems may involve a combination of standard solution procedures and human judgment. Management science can provide models for the portion of a decision-making problem that is structured. For the unstructured portion, a DSS can improve the quality of the information on which the decision is based by providing, for example, not only a single solution but also a range of alternative solutions, along with their potential impacts.
Unstructured Decisions: These can be only partially supported by standard computerized quantitative methods. It is usually necessary to develop customized solutions. However, such solutions may benefit from data and information generated from corporate or external data sources.
Week 1: An Introduction to Business Intelligence
In summary, I want to again welcome everyone to this class and as everyone starts learning about any aspect of data analytics whether used for business or artificial intelligence now that:
The business and organizational environment is continuously changing, and it is becoming more and more complex.
Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate.
Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex.
Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support.
As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways.
This is why we so much motivation for both business or artificial intelligence.
This is cool
stuff!
“A” Level Grades
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