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Analytics, Data Science and A I: Systems for Decision Support

Eleventh Edition

Chapter 1

Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support

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Learning Objectives (1 of 2)

1.1 Understand the need for computerized support of managerial decision making.

1.2 Understand the development of systems for providing decision-making support.

1.3 Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence.

1.4 Describe the business intelligence (B I) methodology and concepts.

1.5 Understand the different types of analytics and review selected applications.

Slide 2 is list of textbook LO numbers and statements

Learning Objectives (2 of 2)

1.6 Understand the basic concepts of artificial intelligence (A I) and see selected applications.

1.7 Understand the analytics ecosystem to identify various key players and career opportunities.

Slide 2 is a list of textbook LO numbers and statements

Opening Vignette (1 of 2)

How Intelligent Systems Work for KONE Elevators and Escalators Company

The problem…

The solution…

The results…

Opening Vignette (2 of 2)

How Intelligent Systems Work for KONE Elevators and Escalators Company

Questions For The Opening Vignette

It is said that K O N E is embedding intelligence across its supply chain and enables smarter buildings. Explain.

Describe the role of I o T in this case.

What makes I B M Watson a necessity in this case?

Check I B M Advanced Analytics. What tools were included that relate to this case?

Check I B M cognitive buildings. How do they relate to this case?

Changing Business Environments And Evolving Needs For Decision Support And Analytics

Big-bet, high-risk decisions.

Cross-cutting decisions, which are repetitive but high risk that require group work.

Ad hoc decisions that arise episodically.

Delegated decisions to individuals or small groups.

Decision Making Process (1 of 2)

The four step managerial process:

Define the problem

Construct a model

Identify and evaluate possible solutions

Compare, choose, and recommend a solution to the problem

Decision Making Process (2 of 2)

A more detailed process is offered by Quain (2018):

Understand the decision you have to make.

Collect all the information.

Identify the alternatives.

Evaluate the pros and cons.

Select the best alternative.

Make the decision.

Evaluate the impact of your decision.

The Influence of the External and Internal Environments on the Process

Technology, I S, Internet, globalization, …

Government regulations, compliance, …

Political factors

Economic factors

Social and psychological factors

Environment factors

Need to make rapid decision, changing market conditions, …

Technologies for Data Analysis and Decision Support

Group communication and collaboration

Improved data management

Managing giant data warehouses and Big Data

Analytical support

Overcoming cognitive limits

Knowledge management

Anywhere, anytime support

Innovation and artificial intelligence

Decision-making Processes And Computerized Decision Support Framework

What is “Decision making”?

Simon’s Decision Making Process

Proposed in 1977 by Herbert Alexander Simon (an American economist and political scientist)

Includes three phases:

Intelligence

Design

Choice

[+] Implementation

[+] Monitoring

The Decision-Making Process

Decision-making Processes (1 of 2)

Phase 1 - The Intelligence Phase: Problem (or Opportunity) Identification

Issues in data collection

Problem classification

Problem decomposition

Problem ownership

Application Case 1.1

Making Elevators Go Faster!

Questions for Discussion:

Why this is an example relevant to decision making?

Relate this situation to the intelligence phase of decision making.

Decision-Making Processes (2 of 2)

Phase 2 - The Design Phase

Models

Phase 3 - The Choice Phase

Evaluating alternatives

Phase 4 - The Implementation Phase

Implementing the solution

Phase 5 – Monitoring

Phase 4 and 5 were not part of Simons’ original model

The Classical Decision Support System Framework

Degree of structuredness

Structured, unstructured, semistructured problems

Type of control

Operational, managerial, strategic

The decision Support matrix

Computer support for …

Structured decisions

Unstructured decisions

Semistructured problems

Decision Support Framework

Key Characteristics and Capabilities of Decision Support System (D S S)

Components of a D S S (1 of 2)

The Data Management System

D S S database

Database management system (D B M S)

Data directory

Query facility

Components of a D S S (2 of 2)

The Model Management Subsystem

Model base

M B M S

Modeling language

Model directory

Model execution, integration, and command processor

The User Interface Subsystem

The Knowledge-Based Subsystem

Evolution of Computerized Decision Support to Business Intelligence, Analytics, Data Science

Figure 1.5 Evolution of Decision Support, Business Intelligence, Analytics, and A I.

A Framework for Business Intelligence

Definitions of business intelligence (B I)

A brief history of B I

The architecture of B I

Data warehousing (D W) [as a foundation of B I]

Business performance management (B P M)

User interface (dashboard)

Transaction processing versus analytics processing

Appropriate planning and alignment of B I with the business strategy

Evolution of Business Intelligence (B I)

The Origins and Drivers of B I

Figure 1.7 A High-Level Architecture of B I.

Source: Based on W. Eckerson. (2003). Smart Companies in the 21st Century: The Secrets of Creating Successful Business Intelligent Solutions Seattle, W A: The Data Warehousing Institute, p. 32, Illustration 5.

Data Warehouse Framework

A Multimedia Exercise in B I

Teradata University Network (T U N)

B S I (Business Scenario Investigations) [like C S I]

Go to https://www.teradatauniversitynetwork.com/Library/Items/BSI-The-Case-of-the-Misconnecting-Passengers/ or www.youtube.com/watch?v=NXEL5F4_aKA

Watch the video

Analyze the video - www.slideshare.net/teradata/bsi-how-we-did-itthe-case-of-the-misconnecting-passengers

Analytics Overview (1 of 2)

Analytics Overview (2 of 2)

Three types of analytics

Descriptive (or reporting) analytics …

Predictive analytics …

Prescriptive analytics …

Analytics applied to different domains

Analytics or data science?

What is Big Data?

Application Case 1.3

Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities

Questions for Discussion:

What was the challenge faced by Silvaris?

How did Silvaris solve its problem using data visualization with Tableau?

Application Case 1.4

Siemens Reduces Cost with the Use of Data Visualization

Questions for Discussion:

What challenges were faced by Siemens visual analytics group?

How did the data visualization tool Dundas B I help Siemens in reducing cost?

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?

Application Case 1.6

A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates

Questions for Discussion:

Why would reallocation of inventory from one customer to another be a major issue for discussion?

How could a D S S help make these decisions?

Analytics Examples in Selected Domains (1 of 2)

Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics

Example 1: Business office

Example 2: The Coach

Healthcare—Humana Examples

Example 1: Preventing Falls in a Senior Population

Example 2: Define the Right Metrics

Example 3: Predictive Models to Identify the Highest Risk Membership in a Health Insurer

Retail—Retail Value Chain …

Image Analytics

Analytics Examples in Selected Domains (2 of 2)

Retail …

Figure 1.15 Example of Analytics Applications in a Retail Value Chain.

Source: Contributed by Abhishek Rathi, C E O, vCreaTek.com.

Application Case 1.7

Image Analysis Helps Estimate Plant Cover

Questions for Discussion:

What is the purpose of knowing how much ground is covered by green foliage on a farm? In a forest?

Why would image analysis of foliage through an app be better than a visual check?

Explore research papers to understand the underlying algorithmic logic of image analysis. What did you learn?

What other applications of image analysis can you think of?

Artificial Intelligence Overview

What Is artificial intelligence (A I)?

Technology that can learn to do things better over time.

Technology that can understand human language.

Technology that can answer questions.

The major benefits of A I

Reduction in the cost of performing work.

Work can be performed much faster.

Work is more consistent than human work.

Increased productivity, profitability, …

The Landscape of A I

Major technologies

Knowledge-based technologies

Biometric related technologies

Tools and platforms …

A I applications …

Narrow (weak) versus general (strong) A I

The three flavors of A I decisions

Assisted intelligence

Autonomous A I

Augmented Intelligence

Application Case 1.8

A I Increases Passengers’ Comfort and Security in Airports and Borders

Questions for Discussion:

List the benefits of A I devices to travelers.

List the benefits to governments and airline companies.

Relate this case to machine vision and other A I tools that deal with people’s biometrics.

Societal Impacts of A I

Impact on agriculture

Contribution to health and medical care

Other societal applications

Transportation

Utilities

Education

Social services

Also see Chapter 13 for smart cities

Application Case 1.9

Robots Took the Job of Camel-Racing Jockeys for Societal Benefits

Questions for Discussion:

It is said that the robots eradicated the child slavery. Explain.

Why do the owners need to drive by their camels while they are racing?

Why not duplicate the technology for horse racing?

Summarize ethical aspects of this case (Read Boddington, 2017). Do this exercise after you have read about ethics in Chapter 14.

Convergence of Analytics and A I

Major differences between analytics and a i

Why combine intelligent systems?

How convergence can help?

Big Data Is empowering A I technologies

The convergence of A I and the IoT

The convergence with blockchain and other technologies

I B M and Microsoft support for intelligent systems convergence

Application Case 1.10

Amazon Go Is Open for Business

Questions for Discussion:

Watch the video. What did you like and/or dislike?

Compare the process described here to a selfcheck available today in many supermarkets and “big box” stores (Home Depot, etc.).

The store was opened in downtown Seattle. Why was the downtown location selected?

What are the benefits to customers? To Amazon?

Will customers be ready to trade privacy for convenience? Discuss.

Overview of Analytics Ecosystem

Plan of the Book

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