Business Intelligence Research Paper from Chapter 1 and 2 based on attached Screenshots whole requirements are posted in word file and chapter PPT are also attached.
Chapter 1:
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An Overview of Business Intelligence, Analytics, and Decision Support
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 today’s turbulent business environment and describe how organizations survive and even excel in such an environment (solving problems and exploiting opportunities)
Understand the need for computerized support of managerial decision making
Understand an early framework for managerial decision making
…
(Continued…)
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Learning Objectives
Learn the conceptual foundations of the DSS methodology
Describe the BI methodology and concepts and relate them to DSS
Understand the various types of analytics
List the major tools of computerized decision support
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Opening Vignette…
Magpie Sensing Employs Analytics to Manage a Vaccine Supply Chain Effectively and Safely
Company background
Problem
Proposed solution and results
Answer & discuss the case questions…
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Opening Vignette…
Questions for the Opening Vignette
What information is provided by the descriptive analytics employed at Magpie Sensing?
What type of support is provided by the predictive analytics employed at Magpie Sensing?
How does prescriptive analytics help in business decision making?
In what ways can actionable information be reported in real time to concerned users of the system?
In what other situations might real-time monitoring applications be needed?
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Changing Business Environment & Computerized Decision Support
Companies are moving aggressively to computerized support of their operations Business Intelligence
Business Pressures–Responses–Support Model
Business pressures result of today’s competitive business climate
Responses to counter the pressures
Support to better facilitate the process
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Business Pressures–Responses–Support Model
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The Business Environment
The environment in which organizations operate today is becoming more and more complex, creating
opportunities, and
problems.
Example: globalization.
Business environment factors:
markets, consumer demands, technology, and societal…
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Business Environment Factors
FACTOR DESCRIPTION
Markets 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 customization
demand 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 beyond
Societal 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 Increasing social responsibility of companies
Greater emphasis on sustainability
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Organizational Responses
Be Reactive, Anticipative, Adaptive, and Proactive
Managers may take actions, such as
Employ strategic planning.
Use new and innovative business models.
Restructure business processes.
Participate in business alliances.
Improve corporate information systems.
… more [in your book]
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Closing the Strategy Gap
One of the major objectives of computerized decision support is to facilitate closing the gap between the current performance of an organization and its desired performance, as expressed in its mission, objectives, and goals, and the strategy to achieve them.
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Managerial Decision Making
Management is a process by which organizational goals are achieved by using 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 alternatives
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The Nature of Managers’ Work
Mintzberg’s 10 Managerial Roles
Interpersonal
1. Figurehead
2. Leader
3. Liaison
Informational
4. Monitor
5. Disseminator
6. Spokesperson
Decisional
7. Entrepreneur
8. Disturbance handler
9. Resource allocator
10. Negotiator
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Decision-Making Process
Managers usually make decisions by following a four-step process (a.k.a. the scientific approach)
Define the problem (or opportunity)
Construct a model that describes the real-world problem.
Identify possible solutions to the modeled problem and evaluate the solutions.
Compare, choose, and recommend a potential solution to the problem.
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Information Systems Support
for Decision Making
Group communication and collaboration
Improved data management
Managing data warehouses and Big Data
Analytical support
Overcoming cognitive limits in processing and storing information
Knowledge management
Anywhere, anytime support
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An Early Decision Support Framework (by Gory and Scott-Morten, 1971)
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An Early Decision Support Framework
Degree of Structuredness (Simon, 1977)
Decisions are classified as
Highly structured (a.k.a. programmed)
Semi-structured
Highly unstructured (i.e., nonprogrammed)
Types of Control (Anthony, 1965)
Strategic planning (top-level, long-range)
Management control (tactical planning)
Operational control
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The Concept of DSS
DSS – interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems
(Gorry and Scott-Morton, 1971)
Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions.
DS as an Umbrella Term
Evolution of DS into Business Intelligence
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A Framework for
Business Intelligence (BI)
BI is an evolution of decision support concepts over time
Then: Executive Information System
Now: Everybody’s Information System (BI)
BI systems are enhanced with additional visualizations, alerts, and performance measurement capabilities
The term BI emerged from industry
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Definition of BI
BI is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies
BI is a content-free expression, so it means different things to different people
BI’s major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis
BI helps transform data, to information (and knowledge), to decisions, and finally to action
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A Brief History of BI
The term BI was coined by the Gartner Group 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-hoc reporting -> coining of the term “BI”
2010s – Inclusion of AI and Data/Text Mining capabilities; Web-based Portals/Dashboards, Big Data, Social Media, Analytics
2020s – yet to be seen
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The Evolution of BI Capabilities
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The Architecture of BI
A BI system has four major components
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
a user interface (e.g., dashboard)
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A High-Level Architecture of BI
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Business Value of BI Analytical Applications
Customer segmentation
Propensity to buy
Customer profitability
Fraud detection
Customer attrition
Channel optimization
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Application Case 1.1
Sabre Helps Its Clients Through Dashboards and Analytics
Questions for Discussion
What is traditional reporting? How is it used in the organization?
How can analytics be used to transform the traditional reporting?
How can interactive reporting assist organizations in decision making?
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A Multimedia Exercise
in Business Intelligence
Teradata University Network (TUN)
www.teradatauniversitynetwork.com
BSI Videos (Business Scenario Investigations)
www.youtube.com/watch?v=NXEL5F4_aKA
Also look for other BSI Videos at TUN
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DSS-BI Connections
Similarities and differences?
Similar architectures, data focus, …
Direct vs. indirect support
Different target audiences
Commercially available systems versus in-house development of solutions
Origination – Industry vs. Academia
So, is DSS = BI ?
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Analytics Overview
Analytics?
Something new or just a new name for …
A Simple Taxonomy of Analytics (proposed by INFORMS)
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Analytics or Data Science?
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Analytics Overview
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Application Case 1.2
Eliminating Inefficiencies at Seattle Children’s Hospital
Questions for Discussion
Who are the users of the tool?
What is a dashboard?
How does visualization help in decision making?
What are the significant results achieved by the use of Tableau?
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Application Case 1.3
Analysis at the Speed of Thought
Questions for Discussion
What are the desired functionalities of a reporting tool?
What advantages were derived by using a reporting tool in the case?
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Application Case 1.4
Moneyball: Analytics in Sports and Movies
Questions for Discussion
How is predictive analytics applied in Moneyball?
What is the difference between objective and subjective approaches in decision making?
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Application Case 1.5
Analyzing Athletic Injuries
Questions for Discussion
What types of analytics are applied in the injury analysis?
How do visualizations aid in understanding the data and delivering insights into the data?
What is a classification problem?
What can be derived by performing sequence analysis?
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Application Case 1.6
Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Networks
Questions for Discussion
How can analytical techniques help organizations to retain competitive advantage?
How can descriptive and predictive analytics help in pursuing prescriptive analytics?
What kind of prescriptive analytic techniques are employed in the case study?
Are the prescriptive models once built good forever?
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Introduction to Big Data Analytics
Big Data?
Not just big!
Volume
Variety
Velocity
More of Big Data and related analytics tools and techniques are covered in Chapter 13.
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Application Case 1.7
Gilt Groupe’s Flash Sales Streamlined by Big Data Analytics
Questions for Discussion
What makes this case study an example of Big Data analytics?
What types of decisions does Gilt Groupe have to make?
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End-of-Chapter Application Case
Nationwide Insurance Used BI to Enhance Customer Service
Questions for Discussion
Why did Nationwide need an enterprise-wide data warehouse?
How did integrated data drive the business value?
What forms of analytics are employed at Nationwide?
With integrated data available in an enterprise data warehouse, what other applications could Nationwide potentially develop?
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Plan of the Book
Part I – Decision Making and Analytics: An Overview
(Chapters 1 & 2)
Part II – Descriptive Analytics
(Chapters 3 & 4)
Part III – Predictive Analytics
Chapters 5 – 8
Part IV – Prescriptive Analytics
Chapter 9 – 12
Part V – Big Data and Future Directions for Business Analytics
Chapters 13 & 14
PLUS – Online Supplements
…
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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.
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Data
Warehouse
Technical staff
Data Warehouse
Environment
Data
Sources
Business Analytics
Environment
Performance and
Strategy
Business usersManagers / executives
Built the data warehouseAccess
Manipulation
Results
BPM strategy
üOrganizing
üSummarizing
üStandardizing
Future component
intelligent systems
User Interface
-browser
-portal
-dashboard
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 order 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 order 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.
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Graded Assignment: Knowledge and Skills Paper
Paper Section 1: Reflection and Literature Review
Using Microsoft Word and Professional APA format, prepare a professional written paper supported with three sources of research that details what you have learned from chapters 1 and 2. This section of the paper should be a minimum of two pages.
Paper Section 2: Applied Learning Exercises
In this section of the professional paper, apply what you have learned from chapters 1 and 2 to descriptively address and answer the problems below. Important Note : Dot not type the actual written problems within the paper itself.
1. Search the Internet for material regarding the work of managers and the role analytics play. What kind of references to consulting firms, academic departments and programs do you find? What major areas are represented? Select five sites that cover one area and report your findings.
2. Most companies and organizations have downloadable demos or trial versions of their software products on the Web so that you can copy and try them out on your own computer. Others have online demos. Find one that provides decision support, try it out, and write a short report about it. Include details about the intended purpose of the software, how it works, and how it supports decision making.
3. Comment on Simon’s (1977) philosophy that managerial decision making is synonymous with the whole process of management. Does this make sense? Explain. Use a real-world example in your explanation.
Important Note : There is no specific page requirement for this section of the paper but make sure any content provided fully addresses each problem.
Paper Section 3: Conclusions
After addressing the problems, conclude your paper with details on how you will use this knowledge and skills to support your professional and or academic goals. This section of the paper should be around one page including a custom and original process flow or flow diagram to visually represent how you will apply this knowledge going forward. This customized and original flow process flow or flow diagram can be created using the “Smart Art” tools in Microsoft Word.
Paper Section 4: APA Reference Page
The three or more sources of research used to support this overall paper should be included in proper APA format in the final section of the paper.
Paper Review and Preparation to submit for Grading
Please make sure to proof read your post prior to submission. This professional paper should be well written and free of grammatical or typographical errors. Also remember not to plagiarize!!!!!!!!!!!!
