Assignment

Part I Introduction to Analytics and AI

Chapter 1Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support

Learning Objectives • Understand the need for computerized support of managerial decision making • Understand the development of systems for providing decision-making support • Recognize the evolution of such computerized support to the current state of

analytics/data science and artificial intelligence • Describe the business intelligence (BI) methodology and concepts • Understand the different types of analytics and review selected applications • Understand the basic concepts of artificial intelligence (AI) and see selected

applications • Understand the analytics ecosystem to identify various key players and career

opportunities

The business environment (climate) is constantly 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 book is about using business analytics and artificial intelligence (AI) as a computerized

support portfolio for managerial decision making. It concentrates on the theoretical and

conceptual foundations of decision support as well as on the commercial tools and techniques

that are available. The book presents the fundamentals of the techniques and the manner in which

these systems are constructed and used. We follow an EEE (exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired

to learn from how various organizations have employed these technologies to make decisions or

to gain a competitive edge. We believe that such exposure to what is being accomplished with

analytics and that how it can be achieved is the key component of learning about analytics. In

describing the techniques, we also give examples of specific software tools that can be used for

developing such applications. However, the book is not limited to any one software tool, so

students can experience these techniques using any number of available software tools. We hope

that this exposure and experience enable and motivate readers to explore the potential of these

techniques in their own domain. To facilitate such exploration, we include exercises that direct

the reader to Teradata University Network (TUN) and other sites that include team-oriented

exercises where appropriate. In our own teaching experience, projects undertaken in the class

facilitate such exploration after students have been exposed to the myriad of applications and

concepts in the book and they have experienced specific software introduced by the professor.

This introductory chapter provides an introduction to analytics and artificial intelligence as well

as an overview of the book. The chapter has the following sections:

1. 1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and Escalators Company 3

2. 1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics 5

3. 1.3 Decision-Making Processes and Computer Decision Support Framework 9 4. 1.4 Evolution of Computerized Decision Support to Business Intelligence/Analytics/Data

Science 22 5. 1.5 Analytics Overview 30 6. 1.6 Analytics Examples in Selected Domains 38 7. 1.7 Artificial Intelligence Overview 52 8. 1.8 Convergence of Analytics and AI 59 9. 1.9 Overview of the Analytics Ecosystem 63 10. 1.10 Plan of the Book 65 11. 1.11 Resources, Links, and the Teradata University Network Connection 66

1.1 Opening Vignette: How Intelligent Systems Work for KONE

Elevators and Escalators Company

KONE is a global industrial company (based in Finland) that manufactures mostly elevators and

escalators and also services over 1.1 million elevators, escalators, and related equipment in

several countries. The company employs over 50,000 people.

The Problem

Over 1 billion people use the elevators and escalators manufactured and serviced by KONE every day. If equipment does not work properly, people may be late to work, cannot get home in time, and may miss important meetings and events. So, KONE’s objective is to minimize the downtime and users’ suffering.

The company has over 20,000 technicians who are dispatched to deal with the elevators anytime a problem occurs. As buildings are getting higher (the trend in many places), more people are using elevators, and there is more pressure on elevators to handle the growing amount of traffic. KONE faced the responsibility to serve users smoothly and safely.

The Solution

KONE decided to use IBM Watson IoT Cloud platform. As we will see in Chapter 6, IBM installed cognitive abilities in buildings that make it possible to recognize situations and behavior of both people and equipment. The Internet of Things (IoT), as we will see in Chapter 13, is a platform that can connect millions of “things” together and to a central command that can manipulate the connected things. Also, the IoT connects sensors that are attached to KONE’s elevators and escalators. The sensors collect information and data about the elevators (such as noise level) and other equipment in real time. Then, the IoT transfers to information centers via the collected data “cloud.” There, analytic systems (IBM Advanced Analytic Engine) and AI process the collected data and predict things such as potential failures. The systems also identify the likely causes of problems and suggest potential remedies. Note the predictive power of IBM Watson Analytics (using machine learning, an AI technology described in Chapters 4–6) for finding problems before they occur. The KONE system collects a significant amount of data that are analyzed for other purposes so that future design of equipment can be improved. This is because Watson Analytics offers a convenient environment for communication of and collaboration around the data. In addition, the analysis suggests how to optimize buildings and equipment operations. Finally, KONE and its customers can get insights regarding the financial aspects of managing the elevators.

KONE also integrates the Watson capabilities with Salesforce’s service tools (Service Cloud Lightning and Field Service Lightning). This combination helps KONE to immediately respond to emergencies or soon-to-occur failures as quickly as possible, dispatching some of its 20,000 technicians to the problems’ sites. Salesforce also provides superb customer relationship management (CRM). The people–machine communication, query, and collaboration in the system are in a natural language (an AI capability of Watson Analytics; see Chapter 6). Note that IBM Watson analytics includes two types of analytics: predictive, which predicts when failures may occur, and prescriptive, which recommends actions (e.g., preventive maintenance).

The Results

KONE has minimized downtime and shortened the repair time. Obviously, elevators/- escalators users are much happier if they do not have problems because of equipment downtime, so they enjoy trouble-free rides. The prediction of “soon-to-happen” can save many problems for the equipment owners. The owners can also optimize the schedule of their own employees (e.g., cleaners and maintenance workers). All in all, the decision makers at both KONE and the buildings can make informed and better decisions. Some day in the future, robots may perform maintenance and repairs of elevators and escalators.

NOTE: This case is a sample of IBM Watson’s success using its cognitive buildings capability. To learn more, we suggest you view the following YouTube videos: (1) youtube.com/watch?v=6UPJHyiJft0 (1:31 min.) (2017); (2) youtube.com/watch?v=EVbd3ejEXus (2:49 min.) (2017).

Sources: Compiled from J. Fernandez. (2017, April). “A Billion People a Day. Millions of Elevators. No Room for Downtime.” IBM

developer Works Blog. developer.ibm.com/dwblog/2017/kone-watson-video/ (accessed September 2018); H. Srikanthan. “KONE

Improves ‘People Flow’ in 1.1 Million Elevators with IBM Watson IoT.” Generis. https://generisgp.com/2018/01/08/ibm-case-study-

kone-corp/ (accessed September 2018); L. Slowey. (2017, February 16). “Look Who’s Talking: KONE Makes Elevator Services Truly

Intelligent with Watson IoT.” IBM Internet of Things Blog. ibm.com/blogs/internet-of-things/kone/ (accessed September 2018).

Questions for the Opening Vignette

1. It is said that KONE is embedding intelligence across its supply chain and enables smarter buildings. Explain.

2. Describe the role of IoT in this case. 3. What makes IBM Watson a necessity in this case? 4. Check IBM Advanced Analytics. What tools were included that relate to this case? 5. Check IBM cognitive buildings. How do they relate to this case?

What Can We Learn from This Vignette?

Today, intelligent technologies can embark on large-scale complex projects when they include

AI combined with IoT. The capabilities of integrated intelligent platforms, such as IBM Watson,

make it possible to solve problems that were economically and technologically unsolvable just a

few years ago. The case introduces the reader to several of the technologies, including advanced

analytics, sensors, IoT, and AI that are covered in this book. The case also points to the use of

“cloud.” The cloud is used to centrally process large amounts of information using analytics and

AI algorithms, involving “things” in different locations. This vignette also introduces us to two

major types of analytics: predictive analytics (Chapters 4–6) and prescriptive analytics

(Chapter 8).

Several AI technologies are discussed: machine learning, natural language processing, computer

vision, and prescriptive analysis.

The case is an example of augmented intelligence in which people and machines work together. The case illustrates the benefits to the vendor, the implementing companies, and their employees

and to the users of the elevators and escalators.

1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics

Decision making is one of the most important activities in organizations of all kind—

probably the most important one. Decision making leads to the success or failure of

organizations and how well they perform. Making decisions is getting difficult due to

internal and external factors. The rewards of making appropriate decisions can be very

high and so can the loss of inappropriate ones.

Unfortunately, it is not simple to make decisions. To begin with, there are several types

of decisions, each of which requires a different decision-making approach. For

example, De Smet et al. (2017) of McKinsey & Company management consultants

classify organizational decision into the following four groups:

• Big-bet, high-risk decisions.

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

(Chapter 11).

• Ad hoc decisions that arise episodically.

• Delegated decisions to individuals or small groups.

Therefore, it is necessary first to understand the nature of decision making. For a

comprehensive discussion, see (De Smet et al. 2017).

Modern business is full of uncertainties and rapid changes. To deal with these,

organizational decision makers need to deal with ever-increasing and changing data.

This book is about the technologies that can assist decision makers in their jobs.

Decision-Making Process

For years, managers considered decision making purely an art—a talent acquired over a long

period through experience (i.e., learning by trial and error) and by using intuition. Management

was considered an art because a variety of individual styles could be used in approaching and

successfully solving the same types of managerial problems. These styles were often based on

creativity, judgment, intuition, and experience rather than on systematic quantitative methods

grounded in a scientific approach. However, recent research suggests that companies with top

managers who are more focused on persistent work tend to outperform those with leaders whose

main strengths are interpersonal communication skills. It is more important to emphasize

methodical, thoughtful, analytical decision making rather than flashiness and interpersonal

communication skills.

Managers usually make decisions by following a four-step process (we learn more about these in

the next section):

1. Define the problem (i.e., a decision situation that may deal with some difficulty or with an opportunity).

2. Construct a model that describes the real-world problem. 3. Identify possible solutions to the modeled problem and evaluate the solutions. 4. Compare, choose, and recommend a potential solution to the problem.

A more detailed process is offered by Quain (2018), who suggests the following steps:

1. Understand the decision you have to make.

2. Collect all the information. 3. Identify the alternatives. 4. Evaluate the pros and cons. 5. Select the best alternative. 6. Make the decision. 7. Evaluate the impact of your decision.

We will return to this process in Section 1.3.

The Influence of the External and Internal Environments on the Process

To follow these decision-making processes, one must make sure that sufficient alternative

solutions, including good ones, are being considered, that the consequences of using these

alternatives can be reasonably predicted, and that comparisons are done properly. However,

rapid changes in internal and external environments make such an evaluation process difficult for

the following reasons:

• Technology, information systems, advanced search engines, and globalization result in

more and more alternatives from which to choose.

• Government regulations and the need for compliance, political instability and terrorism,

competition, and changing consumer demands produce more uncertainty, making it more

difficult to predict consequences and the future.

▪ POLITICAL FACTORS. Major decisions may be influenced by both external and

internal politics. An example is the 2018 trade war on tariffs.

▪ ECONOMIC FACTORS. These range from competition to the genera and state of

the economy. These factors, both in the short and long run, need to be considered.

▪ SOCIOLOGICAL AND PSYCHOLOGICAL FACTORS REGARDING

EMPLOYEES AND CUSTOMERS. These need to be considered when changes are

being made.

▪ ENVIRONMENT FACTORS. The impact on the physical environment must be

assessed in many decision-making situations.

Other factors include the need to make rapid decisions, the frequent and unpredictable changes

that make trial-and-error learning difficult, and the potential costs of making mistakes that may

be large.

These environments are growing more complex every day. Therefore, making decisions today is

indeed a complex task. For further discussion, see Charles (2018). For how to make effective

decisions under uncertainty and pressure, see Zane (2016).

Because of these trends and changes, it is nearly impossible to rely on a trial-and-error approach

to management. Managers must be more sophisticated; they must use the new tools and

techniques of their fields. Most of those tools and techniques are discussed in this book. Using

them to support decision making can be extremely rewarding in making effective decisions.

Further, many tools that are evolving impact even the very existence of several decision-making

tasks that are being automated. This impacts future demand for knowledge workers and begs

many legal and societal impact questions.

Data and Its Analysis in Decision Making

We will see several times in this book how an entire industry can employ analytics to develop

reports on what is happening, predict what is likely to happen, and then make decisions to make

the best use of the situation at hand. These steps require an organization to collect and analyze

vast stores of data. In general, the amount of data doubles every two years. From traditional uses

in payroll and bookkeeping functions, computerized systems are now used for complex

managerial areas ranging from the design and management of automated factories to the

application of analytical methods for the evaluation of proposed mergers and acquisitions.

Nearly all executives know that information technology is vital to their business and extensively

use these technologies.

Computer applications have moved from transaction-processing and monitoring activities to

problem analysis and solution applications, and much of the activity is done with cloud-based

technologies, in many cases accessed through mobile devices. Analytics and BI tools such as

data warehousing, data mining, online analytical processing (OLAP), dashboards, and the use of

cloud-based systems for decision support are the cornerstones of today’s modern management.

Managers must have high-speed, networked information systems (wired or wireless) to assist

them with their most important task: making decisions. In many cases, such decisions are

routinely being fully automated (see Chapter 2), eliminating the need for any managerial

intervention.

Technologies for Data Analysis and Decision Support

Besides the obvious growth in hardware, software, and network capacities, some developments

have clearly contributed to facilitating the growth of decision support and analytics technologies

in a number of ways:

• GROUP COMMUNICATION AND COLLABORATION. Many decisions are made

today by groups whose members may be in different locations. Groups can collaborate and

communicate readily by using collaboration tools as well as the ubiquitous smartphones.

Collaboration is especially important along the supply chain, where partners—all the way

from vendors to customers—must share information. Assembling a group of decision

makers, especially experts, in one place can be costly. Information systems can improve the

collaboration process of a group and enable its members to be at different locations (saving

travel costs). More critically, such supply chain collaboration permits manufacturers to

know about the changing patterns of demand in near real time and thus react to marketplace

changes faster. For a comprehensive coverage and the impact of AI, see Chapters 2, 10,

and 14.

• IMPROVED DATA MANAGEMENT. Many decisions involve complex computations.

Data for these can be stored in different databases anywhere in the organization and even

possibly outside the organization. The data may include text, sound, graphics, and video,

and these can be in different languages. Many times it is necessary to transmit data quickly

from distant locations. Systems today can search, store, and transmit needed data quickly,

economically, securely, and transparently. See Chapters 3 and 9 and the online chapter for

details.

• MANAGING GIANT DATA WAREHOUSES AND BIG DATA. Large data

warehouses (DWs), like the ones operated by Walmart, contain huge amounts of data.

Special methods, including parallel computing and Hadoop/Spark, are available to organize,

search, and mine the data. The costs related to data storage and mining are declining

rapidly. Technologies that fall under the broad category of Big Data have enabled massive

data coming from a variety of sources and in many different forms, which allows a very

different view of organizational performance that was not possible in the past.

See Chapter 9 for details.

• ANALYTICAL SUPPORT. With more data and analysis technologies, more alternatives

can be evaluated, forecasts can be improved, risk analysis can be performed quickly, and

the views of experts (some of whom may be in remote locations) can be collected quickly

and at a reduced cost. Expertise can even be derived directly from analytical systems. With

such tools, decision makers can perform complex simulations, check many possible

scenarios, and assess diverse impacts quickly and economically.This, of course, is the focus

of several chapters in the book. See Chapters 4–7.

• OVERCOMING COGNITIVE LIMITS IN PROCESSING AND STORING

INFORMATION. The human mind has only a limited ability to process and store

information. People sometimes find it difficult to recall and use information in an error-free

fashion due to their cognitive limits. The term cognitive limits indicates that an individual’s problem-solving capability is limited when a wide range of diverse information and

knowledge is required. Computerized systems enable people to overcome their cognitive

limits by quickly accessing and processing vast amounts of stored information. One way to

overcome humans’ cognitive limitations is to use AI support. For coverage of cognitive

aspects, see Chapter 6.

• KNOWLEDGE MANAGEMENT. Organizations have gathered vast stores of

information about their own operations, customers, internal procedures, employee

interactions, and so forth through the unstructured and structured communications taking

place among various stakeholders. Knowledge management systems (KMS) have become

sources of formal and informal support for decision making to managers, although

sometimes they may not even be called KMS. Technologies such as text analytics and IBM Watson are making it possible to generate value from such knowledge stores.

(See Chapters 6 and 12 for details.

• ANYWHERE, ANYTIME SUPPORT. Using wireless technology, managers can access

information anytime and from any place, analyze and interpret it, and communicate with

those using it. This perhaps is the biggest change that has occurred in the last few years. The

speed at which information needs to be processed and converted into decisions has truly

changed expectations for both consumers and businesses. These and other capabilities have

been driving the use of computerized decision support since the late 1960s, especially since

the mid-1990s. The growth of mobile technologies, social media platforms, and analytical

tools has enabled a different level of information systems (IS) to support managers. This

growth in providing data-driven support for any decision extends not just to managers but

also to consumers. We will first study an overview of technologies that have been broadly

referred to as BI. From there we will broaden our horizons to introduce various types of

analytics.

• INNOVATION AND ARTIFICIAL INTELLIGENCE. Because of the complexities in

the decision-making process discussed earlier and the environment surrounding the process,

a more innovative approach is frequently need. A major facilitation of innovation is

provided by AI. Almost every step in the decision-making process can be influenced by AI.

AI is also integrated with analytics, creating synergy in making decisions (Section 1.8).

Section 1.2 Review Questions

1. Why is it difficult to make organizational decisions? 2. Describe the major steps in the decision-making process. 3. Describe the major external environments that can impact decision making. 4. What are some of the key system-oriented trends that have fostered IS-supported

decision making to a new level? 5. List some capabilities of information technologies that can facilitate managerial

decision making.

1.3 Decision-Making Processes and Computerized Decision Support Framework

In this section, we focus on some classical decision-making fundamentals and in more

detail on the decision-making process. These two concepts will help us ground much of

what we will learn in terms of analytics, data science, and artificial intelligence.

Decision making is a process of choosing among two or more alternative courses of

action for the purpose of attaining one or more goals. According to Simon (1977),

managerial decision making is synonymous with the entire management process.

Consider the important managerial function of planning. Planning involves a series of

decisions: What should be done? When? Where? Why? How? By whom? Managers set

goals, or plan; hence, planning implies decision making. Other managerial functions,

such as organizing and controlling, also involve decision making.

Simon’s Process: Intelligence, Design, and Choice

It is advisable to follow a systematic decision-making process. Simon (1977) said that this

involves three major phases: intelligence, design, and choice. He later added a fourth phase:

implementation. Monitoring can be considered a fifth phase—a form of feedback. However, we

view monitoring as the intelligence phase applied to the implementation phase. Simon’s model is the most concise and yet complete characterization of rational decision making. A conceptual

picture of the decision-making process is shown in Figure 1.1. It is also illustrated as a decision

support approach using modeling.

Figure 1.1 The Decision-Making/Modeling Process.

There is a continuous flow of activity from intelligence to design to choice (see the solid lines

in Figure 1.1), but at any phase, there may be a return to a previous phase (feedback). Modeling

is an essential part of this process. The seemingly chaotic nature of following a haphazard path

from problem discovery to solution via decision making can be explained by these feedback

loops.

The decision-making process starts with the intelligence phase; in this phase, the decision maker examines reality and identifies and defines the problem. Problem ownership is established as well. In the design phase, a model that represents the system is constructed. This is done by making assumptions that simplify reality and by writing down the relationships

among all the variables. The model is then validated, and criteria are determined in a principle of

choice for evaluation of the alternative courses of action that are identified. Often, the process of

model development identifies alternative solutions and vice versa.

The choice phase includes the selection of a proposed solution to the model (not necessarily to the problem it represents). This solution is tested to determine its viability. When the proposed

solution seems reasonable, we are ready for the last phase: implementation of the decision (not

necessarily of a system). Successful implementation results in solving the real problem. Failure

leads to a return to an earlier phase of the process. In fact, we can return to an earlier phase

during any of the latter three phases. The decision-making situations described in the opening

vignette follow Simon’s four-phase model, as do almost all other decision-making situations.

The Intelligence Phase: Problem (or Opportunity) Identification

The intelligence phase begins with the identification of organizational goals and objectives

related to an issue of concern (e.g., inventory management, job selection, lack of or incorrect

Web presence) and determination of whether they are being met. Problems occur because of

dissatisfaction with the status quo. Dissatisfaction is the result of a difference between what

people desire (or expect) and what is occurring. In this first phase, a decision maker attempts to

determine whether a problem exists, identify its symptoms, determine its magnitude,

and explicitly define it. Often, what is described as a problem (e.g., excessive costs) may be only

a symptom (i.e., measure) of a problem (e.g., improper inventory levels). Because real-world

problems are usually complicated by many interrelated factors, it is sometimes difficult to

distinguish between the symptoms and the real problem. New opportunities and problems

certainly may be uncovered while investigating the causes of symptoms.

The existence of a problem can be determined by monitoring and analyzing the organization’s

productivity level. The measurement of productivity and the construction of a model are based

on real data. The collection of data and the estimation of future data are among the most difficult

steps in the analysis.

Issues in Data Collection

The following are some issues that may arise during data collection and estimation and thus

plague decision makers:

• Data are not available. As a result, the model is made with and relies on potentially

inaccurate estimates.

• Obtaining data may be expensive.

• Data may not be accurate or precise enough.

• Data estimation is often subjective.

• Data may be insecure.

• Important data that influence the results may be qualitative (soft).

• There may be too many data (i.e., information overload).

• Outcomes (or results) may occur over an extended period. As a result, revenues, expenses,

and profits will be recorded at different points in time. To overcome this difficulty, a

present-value approach can be used if the results are quantifiable.

• It is assumed that future data will be similar to historical data. If this is not the case, the

nature of the change has to be predicted and included in the analysis.

When the preliminary investigation is completed, it is possible to determine whether a problem

really exists, where it is located, and how significant it is. A key issue is whether an information

system is reporting a problem or only the symptoms of a problem. For example, if reports

indicate that sales are down, there is a problem, but the situation, no doubt, is symptomatic of the

problem. It is critical to know the real problem. Sometimes it may be a problem of perception,

incentive mismatch, or organizational processes rather than a poor decision model.

To illustrate why it is important to identify the problem correctly, we provide a classical example

in Application Case 1.1.

Application Case 1.1 Making Elevators Go Faster!

This story has been reported in numerous places and has almost become a classic example to

explain the need for problem identification. Ackoff (as cited in Larson, 1987) described the

problem of managing complaints about slow elevators in a tall hotel tower. After trying many

solutions for reducing the complaint—staggering elevators to go to different floors, adding

operators, and so on—the management determined that the real problem was not about

the actual waiting time but rather the perceived waiting time. So the solution was to install full- length mirrors on elevator doors on each floor. As Hesse and Woolsey (1975) put it, “The

women would look at themselves in the mirrors and make adjustments, while the men would

look at the women, and before they knew it, the elevator was there.” By reducing the perceived

waiting time, the problem went away. Baker and Cameron (1996) give several other examples of

distractions, including lighting and displays, that organizations use to reduce perceived waiting

time. If the real problem is identified as perceived waiting time, it can make a big difference in the proposed solutions and their costs. For example, full-length mirrors probably cost a whole lot

less than adding an elevator! Sources: Based on J. Baker and M. Cameron. (1996, September). “The Effects of the Service Environment on Affect and Consumer Perception of Waiting Time: An Integrative Review and Research Propositions,” Journal of the Academy of Marketing Science, 24, pp. 338–349; R. Hesse and G. Woolsey (1975). Applied Management Science: A Quick and Dirty Approach. Chicago, IL: SRA Inc; R. C. Larson. (1987, November/December). “Perspectives on Queues: Social Justice and the Psychology of Queuing.” Operations Research, 35(6), pp. 895–905.

Questions for Case 1.1

1. Why this is an example relevant to decision making? 2. Relate this situation to the intelligence phase of decision making.

Problem Classification

Problem classification is the conceptualization of a problem in an attempt to place it in a

definable category, possibly leading to a standard solution approach. An important approach

classifies problems according to the degree of structuredness evident in them. This ranges from

totally structured (i.e., programmed) to totally unstructured (i.e., unprogrammed).

Problem Decomposition

Many complex problems can be divided into subproblems. Solving the simpler subproblems may

help in solving a complex problem. Also, seemingly poorly structured problems sometimes have

highly structured subproblems. Just as a semistructured problem results when some phases of

decision making are structured whereas other phases are unstructured, and when some

subproblems of a decision-making problem are structured with others unstructured, the problem

itself is semistructured. As a decision support system is developed and the decision maker and

development staff learn more about the problem, it gains structure.

Problem Ownership

In the intelligence phase, it is important to establish problem ownership. A problem exists in an

organization only if someone or some group takes the responsibility for attacking it and if the

organization has the ability to solve it. The assignment of authority to solve the problem is

called problem ownership. For example, a manager may feel that he or she has a problem because interest rates are too high. Because interest rate levels are determined at the national and

international levels and most managers can do nothing about them, high interest rates are the

problem of the government, not a problem for a specific company to solve. The problem that

companies actually face is how to operate in a high interest-rate environment. For an individual

company, the interest rate level should be handled as an uncontrollable (environmental) factor to

be predicted.

When problem ownership is not established, either someone is not doing his or her job or the

problem at hand has yet to be identified as belonging to anyone. It is then important for someone

to either volunteer to own it or assign it to someone.

The intelligence phase ends with a formal problem statement.

The Design Phase

The design phase involves finding or developing and analyzing possible courses of action. These

include understanding the problem and testing solutions for feasibility. A model of the decision-

making problem is constructed, tested, and validated. Let us first define a model.

Models

A major characteristic of computerized decision support and many BI tools (notably those of

business analytics) is the inclusion of at least one model. The basic idea is to perform the

analysis on a model of reality rather than on the real system. A model is a simplified representation or abstraction of reality. It is usually simplified because reality is too complex to

describe exactly and because much of the complexity is actually irrelevant in solving a specific

problem.

Modeling involves conceptualizing a problem and abstracting it to quantitative and/or qualitative

form. For a mathematical model, the variables are identified and their mutual relationships are

established. Simplifications are made, whenever necessary, through assumptions. For example, a

relationship between two variables may be assumed to be linear even though in reality there may

be some nonlinear effects. A proper balance between the level of model simplification and the

representation of reality must be obtained because of the cost–benefit trade-off. A simpler model

leads to lower development costs, easier manipulation, and a faster solution but is less

representative of the real problem and can produce inaccurate results. However, a simpler model

generally requires fewer data, or the data are aggregated and easier to obtain.

The Choice Phase

Choice is the critical act of decision making. The choice phase is the one in which the actual

decision and the commitment to follow a certain course of action are made. The boundary

between the design and choice phases is often unclear because certain activities can be

performed during both of them and because the decision maker can return frequently from choice

activities to design activities (e.g., generate new alternatives while performing an evaluation of

existing ones). The choice phase includes the search for, evaluation of, and recommendation of

an appropriate solution to a model. A solution to a model is a specific set of values for the

decision variables in a selected alternative. Choices can be evaluated as to their viability and

profitability.

Each alternative must be evaluated. If an alternative has multiple goals, they must all be

examined and balanced against each other. Sensitivity analysis is used to determine the

robustness of any given alternative; slight changes in the parameters should ideally lead to slight

or no changes in the alternative chosen. What-if analysis is used to explore major changes in the

parameters. Goal seeking helps a manager determine values of the decision variables to meet a

specific objective. These topics are addressed in Chapter 8.

The Implementation Phase

In The Prince, Machiavelli astutely noted some 500 years ago that there was “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 implementation of a proposed solution to a problem is, in effect, the

initiation of a new order of things or the introduction of change. And change must be managed.

User expectations must be managed as part of change management.

The definition of implementation is somewhat complicated because implementation is a long, involved process with vague boundaries. Simplistically, the implementation phase involves putting a recommended solution to work, not necessarily implementing a computer system.

Many generic implementation issues, such as resistance to change, degree of support of top

management, and user training, are important in dealing with information system–supported

decision making. Indeed, many previous technology-related waves (e.g., business process

reengineering [BPR] and knowledge management) have faced mixed results mainly because of

change management challenges and issues. Management of change is almost an entire discipline

in itself, so we recognize its importance and encourage readers to focus on it independently.

Implementation also includes a thorough understanding of project management. The importance

of project management goes far beyond analytics, so the last few years have witnessed a major

growth in certification programs for project managers. A very popular certification now is the

Project Management Professional (PMP). See pmi.org for more details. Implementation must also involve collecting and analyzing data to learn from the previous

decisions and improve the next decision. Although analysis of data is usually conducted to

identify the problem and/or the solution, analytics should also be employed in the feedback

process. This is especially true for any public policy decisions. We need to be sure that the data

being used for problem identification is valid. Sometimes people find this out only after the

implementation phase.

The decision-making process, though conducted by people, can be improved with computer

support, which is introduced next.

The Classical Decision Support System Framework

The early definitions of decision support system (DSS) identified it as a system intended to

support managerial decision makers in semistructured and unstructured decision situations. DSS

was meant to be an adjunct to decision makers, extending their capabilities but not replacing

their judgment. DSS was aimed at decisions that required judgment or at decisions that could not

be completely supported by algorithms. Not specifically stated but implied in the early

definitions was the notion that the system would be computer based, would operate interactively

online, and preferably would have graphical output capabilities, now simplified via browsers and

mobile devices.

An early framework for computerized decision support includes several major concepts that are

used in forthcoming sections and chapters of this book. Gorry and Scott-Morton created and used

this framework in the early 1970s, and the framework then evolved into a new technology

called DSS. Gorry and Scott-Morton (1971) proposed a framework that is a 3-by-3 matrix, as shown

in Figure 1.2. The two dimensions are the degree of structuredness and the types of control.

Figure 1.2 Decision Support Frameworks.

Degree of Structuredness

The left side of Figure 1.2 is based on Simon’s (1977) idea that decision-making

processes fall along a continuum that ranges from highly structured (sometimes

called programmed) to highly unstructured (i.e., non-programmed) decisions. Structured

processes are routine and typically repetitive problems for which standard solution

methods exist.

Unstructured processes are fuzzy, complex problems for which there are no cut-and-

dried solution methods.

An unstructured problem is one where the articulation of the problem or the solution

approach may be unstructured in itself. In a structured problem, the procedures for

obtaining the best (or at least a good enough) solution are known. Whether the problem

involves finding an appropriate inventory level or choosing an optimal investment

strategy, the objectives are clearly defined. Common objectives are cost minimization

and profit maximization.

Semistructured problems fall between structured and unstructured problems, having

some structured elements and some unstructured elements. Keen and Scott-Morton

(1978) mentioned trading bonds, setting marketing budgets for consumer products, and

performing capital acquisition analysis as semistructured problems.

Types of Control

The second half of the Gorry and Scott-Morton (1971) framework (refer to Figure 1.2) is based

on Anthony’s (1965) taxonomy, which defines three broad categories that encompass all

managerial activities: strategic planning, which involves defining long-range goals and policies for resource allocation; management control, the acquisition and efficient use of resources in the accomplishment of organizational goals; and operational control, the efficient and effective execution of specific tasks.

The Decision Support Matrix

Anthony’s (1965) and Simon’s (1977) taxonomies are combined in the nine-cell decision support

matrix shown in Figure 1.2. The initial purpose of this matrix was to suggest different types of

computerized support to different cells in the matrix. Gorry and Scott-Morton (1971) suggested,

for example, that for making semistructured decisions and unstructured decisions, conventional management information systems (MIS) and management science (MS) tools are

insufficient. Human intellect and a different approach to computer technologies are necessary.

They proposed the use of a supportive information system, which they called a DSS. Note that the more structured and operational control-oriented tasks (such as those in cells 1, 2,

and 4 of Figure 1.2) are usually performed by lower-level managers, whereas the tasks in cells 6,

8, and 9 are the responsibility of top executives or highly trained specialists.

Computer Support for Structured Decisions

Since the 1960s, computers have historically supported structured and some semistructured

decisions, especially those that involve operational and managerial control. Operational and

managerial control decisions are made in all functional areas, especially in finance and

production (i.e., operations) management.

Structured problems, which are encountered repeatedly, have a high level of structure, as their

name suggests. It is therefore possible to abstract, analyze, and classify them into specific

categories. For example, a make-or-buy decision is one category. Other examples of categories

are capital budgeting, allocation of resources, distribution, procurement, planning, and inventory

control decisions. For each category of decision, an easy-to-apply prescribed model and solution

approach have been developed, generally as quantitative formulas. Therefore, it is possible to use

a scientific approach for automating portions of managerial decision making. Solutions to many structured problems can be fully automated (see Chapters 2 and 12).

Computer Support for Unstructured Decisions

Unstructured problems 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. Intuition

and judgment may play a large role in these types of decisions, as may computerized

communication and collaboration technologies, as well as cognitive computing (Chapter 6) and

deep learning (Chapter 5).

Computer Support for Semistructured Problems

Solving 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. These capabilities

help managers to better understand the nature of problems and, thus, to make better decisions.

Decision Support System: Capabilities

The early definitions of DSS identified it as a system intended to support managerial decision

makers in semistructured and unstructured decision situations. DSS was meant to be an adjunct

to decision makers, extending their capabilities but not replacing their judgment. It was aimed at

decisions that required judgment or at decisions that could not be completely supported by

algorithms. Not specifically stated but implied in the early definitions was the notion that the

system would be computer based, would operate interactively online, and preferably would have

graphical output capabilities, now simplified via browsers and mobile devices.

A DSS Application

A DSS is typically built to support the solution of a certain problem or to evaluate an

opportunity. This is a key difference between DSS and BI applications. In a very strict

sense, business intelligence (BI) systems monitor situations and identify problems and/or opportunities using analytic methods. Reporting plays a major role in BI; the user generally must

identify whether a particular situation warrants attention and then can apply analytical methods.

Again, although models and data access (generally through a data warehouse) are included in BI,

a DSS may have its own databases and is developed to solve a specific problem or set of

problems and are therefore called DSS applications.

Formally, a DSS is an approach (or methodology) for supporting decision making. It uses an

interactive, flexible, adaptable computer-based information system (CBIS) especially developed

for supporting the solution to a specific unstructured management problem. It uses data, provides

an easy user interface, and can incorporate the decision maker’s own insights. In addition, a DSS

includes models and is developed (possibly by end users) through an interactive and iterative

process. It can support all phases of decision making and may include a knowledge component.

Finally, a DSS can be used by a single user or can be Web based for use by many people at

several locations.

The Characteristics and Capabilities of DSS

Because there is no consensus on exactly what a DSS is, there is obviously no agreement

on the standard characteristics and capabilities of DSS. The capabilities

in Figure 1.3 constitute an ideal set, some members of which are described in the

definitions of DSS and illustrated in the application cases. Figure 1.3 Key Characteristics and Capabilities of DSS.

The key characteristics and capabilities of DSS (as shown in Figure 1.3) are as follows: 1. Supports decision makers, mainly in semistructured and unstructured situations,

by bringing together human judgment and computerized information. Such problems cannot be solved (or cannot be solved conveniently) by other

computerized systems or through use of standard quantitative methods or tools. Generally, these problems gain structure as the DSS is developed. Even some structured problems have been solved by DSS.

2. Supports all managerial levels, ranging from top executives to line managers. 3. Supports individuals as well as groups. Less-structured problems often require

the involvement of individuals from different departments and organizational levels or even from different organizations. DSS supports virtual teams through collaborative Web tools. DSS has been developed to support individual and group work as well as to support individual decision making and groups of decision makers working somewhat independently.

4. Supports interdependent and/or sequential decisions. The decisions may be made once, several times, or repeatedly.

5. Supports all phases of the decision-making process: intelligence, design, choice, and implementation.

6. Supports a variety of decision-making processes and styles. 7. Is flexible, so users can add, delete, combine, change, or rearrange basic elements.

The decision maker should be reactive, able to confront changing conditions quickly, and able to adapt the DSS to meet these changes. It is also flexible in that it can be readily modified to solve other, similar problems.

8. Is user-friendly, has strong graphical capabilities, and a natural language interactive human-machine interface can greatly increase the effectiveness of DSS. Most new DSS applications use Web-based interfaces or mobile platform interfaces.

9. Improves the effectiveness of decision making (e.g., accuracy, timeliness, quality) rather than its efficiency (e.g., the cost of making decisions). When DSS is deployed, decision making often takes longer, but the decisions are better.

10. Provides complete control by the decision maker over all steps of the decision-- making process in solving a problem. A DSS specifically aims to support, not to replace, the decision maker.

11. Enables end users to develop and modify simple systems by themselves. Larger systems can be built with assistance from IS specialists. Spreadsheet packages have been utilized in developing simpler systems. OLAP and data mining software in conjunction with data warehouses enable users to build fairly large, complex DSS.

12. Provides models that are generally utilized to analyze decision-making situations. The modeling capability enables experimentation with different strategies under different configurations.

13. Provides access to a variety of data sources, formats, and types, including GIS, multimedia, and object-oriented data.

14. Can be employed as a stand-alone tool used by an individual decision maker in one location or distributed throughout an organization and in several organizations along the supply chain. It can be integrated with other DSS and/or applications, and it can be distributed internally and externally, using networking and Web technologies.

These key DSS characteristics and capabilities allow decision makers to make better,

more consistent decisions in a timely manner, and they are provided by major DSS

components,

Components of a Decision Support System

A DSS application can be composed of a data management subsystem, a model

management subsystem, a user interface subsystem, and a knowledge-based

management subsystem. We show these in Figure 1.4. Figure 1.4 Schematic View of DSS.

The Data Management Subsystem

The data management subsystem includes a database that contains relevant data for the situation

and is managed by software called the database management system (DBMS). DBMS is used as both singular and plural (system and systems) terms, as are many other acronyms in this text. The data management subsystem can be interconnected with the corporate data warehouse, a

repository for corporate relevant decision-making data. Usually, the data are stored or accessed

via a database Web server. The data management subsystem is composed of the following

elements:

• DSS database

• Database management system

• Data directory

• Query facility

Many of the BI or descriptive analytics applications derive their strength from the data

management side of the subsystems.

The Model Management Subsystem

The model management subsystem is the component that includes financial, statistical,

management science, or other quantitative models that provide the system’s analytical

capabilities and appropriate software management. Modeling languages for building custom

models are also included. This software is often called a model base management system

(MBMS). This component can be connected to corporate or external storage of models. Model

solution methods and management systems are implemented in Web development systems (such

as Java) to run on application servers. The model management subsystem of a DSS is composed

of the following elements:

• Model base

• MBMS

• Modeling language

• Model directory

• Model execution, integration, and command processor

Because DSS deals with semistructured or unstructured problems, it is often necessary to

customize models, using programming tools and languages. Some examples of these are .NET

Framework languages, C++, and Java. OLAP software may also be used to work with models in

data analysis. Even languages for simulations such as Arena and statistical packages such as

those of SPSS offer modeling tools developed through the use of a proprietary programming

language. For small- and medium-sized DSS or for less complex ones, a spreadsheet (e.g., Excel)

is usually used. We use Excel for several examples in this book. Application Case 1.2 describes a

spreadsheet-based DSS.

Application Case 1.2 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions

Telecommunications network services to educational institutions and government entities are

typically provided by a mix of private and public organizations. Many states in the United States

have one or more state agencies that are responsible for providing network services to schools,

colleges, and other state agencies. One example of such an agency is OneNet in Oklahoma.

OneNet is a division of the Oklahoma State Regents for Higher Education and operated in

cooperation with the Office of State Finance.

Usually agencies such as OneNet operate as an enterprise-type fund. They must recover their

costs through billing their clients and/or by justifying appropriations directly from the state

legislatures. This cost recovery should occur through a pricing mechanism that is efficient,

simple to implement, and equitable. This pricing model typically needs to recognize many

factors: convergence of voice, data, and video traffic on the same infrastructure; diversity of user

base in terms of educational institutions and state agencies; diversity of applications in use by

state clients from e-mail to videoconferences, IP telephoning, and distance learning; recovery of

current costs as well as planning for upgrades and future developments; and leverage of the

shared infrastructure to enable further economic development and collaborative work across the

state that leads to innovative uses of OneNet.

These considerations led to the development of a spreadsheet-based model. The system, SNAP-

DSS, or Service Network Application and Pricing (SNAP)-based DSS, was developed in

Microsoft Excel 2007 and used the VBA programming language.

The SNAP-DSS offers OneNet the ability to select the rate card options that best fit the preferred

pricing strategies by providing a real-time, user-friendly, graphical user interface (GUI). In

addition, the SNAP-DSS not only illustrates the influence of the changes in the pricing factors on

each rate card option but also allows the user to analyze various rate card options in different

scenarios using different parameters. This model has been used by OneNet financial planners to

gain insights into their customers and analyze many what-if scenarios of different rate plan

options.

Source: Based on J. Chongwatpol and R. Sharda. (2010, December). “SNAP: A DSS to Analyze Network Service Pricing for State Networks.” Decision Support Systems, 50(1), pp. 347–359.

The User Interface Subsystem

The user communicates with and commands the DSS through the user interface subsystem. The

user is considered part of the system. Researchers assert that some of the unique contributions of

DSS are derived from the intensive interaction between the computer and the decision maker. A

difficult user interface is one of the major reasons that managers do not use computers and

quantitative analyses as much as they could, given the availability of these technologies. The

Web browser provided a familiar, consistent GUI structure for many DSS in the 2000s. For

locally used DSS, a spreadsheet also provides a familiar user interface. The Web browser has

been recognized as an effective DSS GUI because it is flexible, user-friendly, and a gateway to

almost all sources of necessary information and data. Essentially, Web browsers have led to the

development of portals and dashboards, which front end many DSS.

Explosive growth in portable devices, including smartphones and tablets, has changed the DSS

user interfaces as well. These devices allow either handwritten input or typed input from internal

or external keyboards. Some DSS user interfaces utilize natural language input (i.e., text in a

human language) so that the users can easily express themselves in a meaningful way. Cell

phone inputs through short message service (SMS) or chatbots are becoming more common for

at least some consumer DSS-type applications. For example, one can send an SMS request for

search on any topic to GOOGL (46645). Such capabilities are most useful in locating nearby

businesses, addresses, or phone numbers, but it can also be used for many other decision support

tasks. For example, users can find definitions of words by entering the word “define” followed

by a word, such as “define extenuate.” Some of the other capabilities include

• Price lookups: “Price 64GB iPhone X.”

• Currency conversions: “10 US dollars in euros.”

• Sports scores and game times: Just enter the name of a team (“NYC Giants”), and Google

SMS will send the most recent game’s score and the date and time of the next match.

This type of SMS-based search capability is also available for other search engines such as

Microsoft’s search engine Bing.

With the emergence of smartphones such as Apple’s iPhone and Android smartphones from

many vendors, many companies are developing apps to provide purchasing-decision support. For example, Amazon’s app allows a user to take a picture of any item in a store (or wherever)

and send it to Amazon.com. Amazon.com’s graphics-understanding algorithm tries to match the

image to a real product in its databases and sends the user a page similar to Amazon.com’s

product info pages, allowing users to perform price comparisons in real time. Millions of other

apps have been developed that provide consumers support for decision making on finding and

selecting stores/restaurants/service providers on the basis of location, recommendations from

others, and especially from your own social circles. Search activities noted in the previous

paragraph are also largely accomplished now through apps provided by each search provider.

Voice input for these devices and the new smart speakers such as Amazon Echo (Alexa) and

Google Home is common and fairly accurate (but not perfect). When voice input with

accompanying speech-recognition software (and readily available text-to-speech software) is

used, verbal instructions with accompanied actions and outputs can be invoked. These are readily

available for DSS and are incorporated into the portable devices described earlier. An example of

voice inputs that can be used for a general-purpose DSS is Apple’s Siri application and Google’s

Google Now service. For example, a user can give her or his zip code and say “pizza delivery.”

These devices provide the search results and can even place a call to a business.

The Knowledge-Based Management Subsystem

Many of the user interface developments are closely tied to the major new advances in their

knowledge-based systems. The knowledge-based management subsystem can support any of the

other subsystems or act as an independent component. It provides intelligence to augment the

decision maker’s own or to help understand a user’s query so as to provide a consistent answer.

It can be interconnected with the organization’s knowledge repository (part of a KMS), which is

sometimes called the organizational knowledge base, or connect to thousands of external knowledge sources. Many artificial intelligence methods have been implemented in the current

generation of learning systems and are easy to integrate into the other DSS components. One of

the most widely publicized knowledge-based DSS is IBM’s Watson, which was introduced in the

opening vignette and will be described in more detail later.

This section has covered the history and progression of Decision Support Systems in brief. In the

next section we discuss evolution of this support to business intelligence, analytics, and data

science.

Section 1.3 Review Questions

1. List and briefly describe Simon’s four phases of decision making. 2. What is the difference between a problem and its symptoms? 3. Why is it important to classify a problem? 4. Define implementation. 5. What are structured, unstructured, and semistructured decisions? Provide two

examples of each. 6. Define operational control, managerial control, and strategic planning. Provide

two examples of each. 7. What are the nine cells of the decision framework? Explain what each is for. 8. How can computers provide support for making structured decisions? 9. How can computers provide support for making semistructured and unstructured

decisions?

1.4 Evolution of Computerized Decision Support to Business Intelligence/Analytics/Data Science

The timeline in Figure 1.5 shows the terminology used to describe analytics since the

1970s. During the 1970s, the primary focus of information systems support for decision

making focused on providing structured, periodic reports that a manager could use for

decision making (or ignore them). Businesses began to create routine reports to inform

decision makers (managers) about what had happened in the previous period (e.g., day,

week, month, quarter). Although it was useful to know what had happened in the past,

managers needed more than this: They needed a variety of reports at different levels of

granularity to better understand and address changing needs and challenges of the

business. These were usually called management information systems (MIS). In the early

1970s, Scott-Morton first articulated the major concepts of DSS. He defined DSS as

“interactive computer-based systems, which help decision makers

utilize data and models to solve unstructured problems” (Gorry and Scott-Morton,

1971). The following is another classic DSS definition provided by Keen and Scott-

Morton (1978): Figure 1.5 Evolution of Decision Support, Business Intelligence, Analytics, and AI.

Decision support systems couple the intellectual resources of individuals with the

capabilities of the computer to improve the quality of decisions. It is a computer-based

support system for management decision makers who deal with semistructured

problems.

Note that the term decision support system, like management information system and

several other terms in the field of IT, is a content-free expression (i.e., it means different

things to different people). Therefore, there is no universally accepted definition of DSS.

During the early days of analytics, data were often obtained from the domain experts

using manual processes (i.e., interviews and surveys) to build mathematical or

knowledge-based models to solve constrained optimization problems. The idea was to

do the best with limited resources. Such decision support models were typically called

operations research (OR). The problems that were too complex to solve optimally (using

linear or nonlinear mathematical programming techniques) were tackled using heuristic

methods such as simulation models. (We will introduce these as prescriptive analytics

later in this chapter).

In the late 1970s and early 1980s, in addition to the mature OR models that were being

used in many industries and government systems, a new and exciting line of models

had emerged: rule-based expert systems (ESs). These systems promised to capture

experts’ knowledge in a format that computers could process (via a collection of if-then-

else rules or heuristics) so that these could be used for consultation much the same way

that one would use domain experts to identify a structured problem and to prescribe

the most probable solution. ESs allowed scarce expertise to be made available where

and when needed, using an “intelligent” DSS.

The 1980s saw a significant change in the way organizations captured business-related

data. The old practice had been to have multiple disjointed information systems tailored

to capture transactional data of different organizational units or functions (e.g.,

accounting, marketing and sales, finance, manufacturing). In the 1980s, these systems

were integrated as enterprise-level information systems that we now commonly

call enterprise resource planning (ERP) systems. The old mostly sequential and

nonstandardized data representation schemas were replaced by relational database

management (RDBM) systems. These systems made it possible to improve the capture

and storage of data as well as the relationships between organizational data fields while

significantly reducing the replication of information. The need for RDBM and ERP

systems emerged when data integrity and consistency became an issue, significantly

hindering the effectiveness of business practices. With ERP, all the data from every

corner of the enterprise is collected and integrated into a consistent schema so that

every part of the organization has access to the single version of the truth when and

where needed. In addition to the emergence of ERP systems, or perhaps because of

these systems, business reporting became an on-demand, as-needed business practice.

Decision makers could decide when they needed to or wanted to create specialized

reports to investigate organizational problems and opportunities.

In the 1990s, the need for more versatile reporting led to the development of executive

information systems (EISs; DSS designed and developed specifically for executives and

their decision-making needs). These systems were designed as graphical dashboards

and scorecards so that they could serve as visually appealing displays while focusing

on the most important factors for decision makers to keep track of the key performance

indicators. To make this highly versatile reporting possible while keeping the

transactional integrity of the business information systems intact, it was necessary to

create a middle data tier known as a DW as a repository to specifically support business

reporting and decision making. In a very short time, most large- to medium-sized

businesses adopted data warehousing as their platform for enterprise-wide decision

making. The dashboards and scorecards got their data from a DW, and by doing so,

they were not hindering the efficiency of the business transaction systems mostly

referred to as ERP systems.

In the 2000s, the DW-driven DSS began to be called BI systems. As the amount of

longitudinal data accumulated in the DWs increased, so did the capabilities of

hardware and software to keep up with the rapidly changing and evolving needs of the

decision makers. Because of the globalized competitive marketplace, decision makers

needed current information in a very digestible format to address business problems

and to take advantage of market opportunities in a timely manner. Because the data in a

DW are updated periodically, they do not reflect the latest information. To elevate this

information latency problem, DW vendors developed a system to update the data more

frequently, which led to the terms real-time data warehousing and, more

realistically, right-time data warehousing, which differs from the former by adopting a

data-refreshing policy based on the needed freshness of the data items (i.e., not all data

items need to be refreshed in real time). DWs are very large and feature rich, and it

became necessary to “mine” the corporate data to “discover” new and useful

knowledge nuggets to improve business processes and practices, hence, the terms data

mining and text mining. With the increasing volumes and varieties of data, the needs for

more storage and more processing power emerged. Although large corporations had

the means to tackle this problem, small- to medium-sized companies needed more

financially manageable business models. This need led to service-oriented architecture

and software and infrastructure-as-a-service analytics business models. Smaller

companies, therefore, gained access to analytics capabilities on an as-needed basis and

paid only for what they used, as opposed to investing in financially prohibitive

hardware and software resources.

In the 2010s, we are seeing yet another paradigm shift in the way that data are captured

and used. Largely because of the widespread use of the Internet, new data generation

mediums have emerged. Of all the new data sources (e.g., radio-frequency

identification [RFID] tags, digital energy meters, clickstream Web logs, smart home

devices, wearable health monitoring equipment), perhaps the most interesting and

challenging is social networking/social media. These unstructured data are rich in

information content, but analysis of such data sources poses significant challenges to

computational systems from both software and hardware perspectives. Recently, the

term Big Data has been coined to highlight the challenges that these new data streams

have brought on us. Many advancements in both hardware (e.g., massively parallel

processing with very large computational memory and highly parallel multiprocessor

computing systems) and software/algorithms (e.g., Hadoop with MapReduce and

NoSQL, Spark) have been developed to address the challenges of Big Data.

The last few years and the upcoming decade are bringing massive growth in many

exciting dimensions. For example, streaming analytics and the sensor technologies have

enabled the IoT. Artificial Intelligence is changing the shape of BI by enabling new ways

of analyzing images through deep learning, not just traditional visualization of data.

Deep learning and AI are also helping grow voice recognition and speech synthesis,

leading to new interfaces in interacting with technologies. Almost half of U.S.

households already have a smart speaker such as Amazon Echo or Google Home and

have begun to interact with data and systems using voice interfaces. Growth in video

interfaces will eventually enable gesture-based interaction with systems. All of these are

being enabled due to massive cloud-based data storage and amazingly fast processing

capabilities. And more is yet to come.

It is hard to predict what the next decade will bring and what the new analytics-related

terms will be. The time between new paradigm shifts in information systems and

particularly in analytics has been shrinking, and this trend will continue for the

foreseeable future. Even though analytics is not new, the explosion in its popularity is

very new. Thanks to the recent explosion in Big Data, ways to collect and store these

data and intuitive software tools, data-driven insights are more accessible to business

professionals than ever before. Therefore, in the midst of global competition, there is a

huge opportunity to make better managerial decisions by using data and analytics to

increase revenue while decreasing costs by building better products, improving

customer experience, and catching fraud before it happens, improving customer

engagement through targeting and customization, and developing entirely new lines of

business, all with the power of analytics and data. More and more companies are now

preparing their employees with the know-how of business analytics to drive

effectiveness and efficiency in their day-to-day decision-making processes.

The next section focuses on a framework for BI. Although most people would agree that

BI has evolved into analytics and data science, many vendors and researchers still use

that term. So the next few paragraphs pay homage to that history by specifically

focusing on what has been called BI. Following the next section, we introduce analytics

and use that as the label for classifying all related concepts.

A Framework for Business Intelligence

The decision support concepts presented in Sections 1.2 and 1.3 have been implemented

incrementally, under different names, by many vendors that have created tools and

methodologies for decision support. As noted in Section 1.2, as the enterprise-wide systems

grew, managers were able to access user-friendly reports that enabled them to make decisions

quickly. These systems, which were generally called EISs, then began to offer additional

visualization, alerts, and performance measurement capabilities. By 2006, the

major commercial products and services appeared under the term business intelligence (BI).

Definitions of BI

Business intelligence (BI) is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. It is, like DSS, a content-free expression, so it

means different things to different people. Part of the confusion about BI lies in the flurry of

acronyms and buzzwords that are associated with it (e.g., business performance management

[BPM]). BI’s major objective is to enable interactive access (sometimes in real time) to data, to

enable manipulation of data, and to give business managers and analysts the ability to conduct

appropriate analyses. By analyzing historical and current data, situations, and performances,

decision makers get valuable insights that enable them to make more informed and better

decisions. The process of BI is based on the transformation of data to information, then to decisions, and finally to actions.

A Brief History of BI

The term BI was coined by the Gartner Group in the mid-1990s. However, as the history in the previous section points out, the concept is much older; it has its roots in the MIS reporting

systems of the 1970s. During that period, reporting systems were static, were two dimensional,

and had no analytical capabilities. In the early 1980s, the concept of EISs emerged. This concept

expanded the computerized support to top-level managers and executives. Some of the

capabilities introduced were dynamic multidimensional (ad hoc or on-demand) reporting,

forecasting and prediction, trend analysis, drill-down to details, status access, and critical success

factors. These features appeared in dozens of commercial products until the mid-1990s. Then the

same capabilities and some new ones appeared under the name BI. Today, a good BI-based

enterprise information system contains all the information that executives need. So, the original

concept of EIS was transformed into BI. By 2005, BI systems started to include artificial intelligence capabilities as well as powerful analytical capabilities. Figure 1.6 illustrates the various tools and techniques that may be included in a BI system. It illustrates the evolution of

BI as well. The tools shown in Figure 1.6 provide the capabilities of BI. The most sophisticated

BI products include most of these capabilities; others specialize in only some of them.

Figure 1.6 Evolution of Business Intelligence (BI).

The Architecture of BI

A BI system has four major components: a DW, with its source data; business analytics, a

collection of tools for manipulating, mining, and analyzing the data in the DW; BPM for

monitoring and analyzing performance; and a user interface (e.g., a dashboard). The

relationship among these components is illustrated in Figure 1.7. Figure 1.7 A High-Level Architecture of BI.

The Origins and Drivers of BI

Where did modern approaches to DW and BI come from? What are their roots, and how do those

roots affect the way organizations are managing these initiatives today? Today’s investments in

information technology are under increased scrutiny in terms of their bottom-line impact and

potential. The same is true of DW and the BI applications that make these initiatives possible.

Organizations are being compelled to capture, understand, and harness their data to support

decision making to improve business operations. Legislation and regulation (e.g., the Sarbanes-

Oxley Act of 2002) now require business leaders to document their business processes and to

sign off on the legitimacy of the information they rely on and report to stakeholders. Moreover,

business cycle times are now extremely compressed; faster, more informed, and better decision

making is, therefore, a competitive imperative. Managers need the right information at the right time and in the right place. This is the mantra for modern approaches to BI. Organizations have to work smart. Paying careful attention to the management of BI initiatives is

a necessary aspect of doing business. It is no surprise, then, that organizations are increasingly

championing BI and under its new incarnation as analytics.

Data Warehouse as a Foundation for Business Intelligence

BI systems rely on a DW as the information source for creating insight and supporting

managerial decisions. A multitude of organizational and external data is captured, transformed,

and stored in a DW to support timely and accurate decisions through enriched business insight.

In simple terms, a DW is a pool of data produced to support decision making; it is also a repository of current and historical data of potential interest to managers throughout the

organization. Data are usually structured to be available in a form ready for analytical processing

activities (i.e., OLAP, data mining, querying, reporting, and other decision support applications).

A DW is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of

management’s decision-making process.

Whereas a DW is a repository of data, data warehousing is literally the entire process. Data

warehousing is a discipline that results in applications that provide decision support capability,

allows ready access to business information, and creates business insight. The three main types

of data warehouses are data marts (DMs), operational data stores (ODS), and enterprise data

warehouses (EDW). Whereas a DW combines databases across an entire enterprise, a DM is

usually smaller and focuses on a particular subject or department. A DM is a subset of a data

warehouse, typically consisting of a single subject area (e.g., marketing, operations). An

operational data store (ODS) provides a fairly recent form of customer information file. This

type of database is often used as an interim staging area for a DW. Unlike the static contents of a

DW, the contents of an ODS are updated throughout the course of business operations. An EDW

is a large-scale data warehouse that is used across the enterprise for decision support. The large-

scale nature of an EDW provides integration of data from many sources into a standard format

for effective BI and decision support applications. EDWs are used to provide data for many types

of DSS, including CRM, supply chain management (SCM), BPM, business activity monitoring,

product life-cycle management, revenue management, and sometimes even KMS.

In Figure 1.8, we show the DW concept. Data from many different sources can be extracted,

transformed, and loaded into a DW for further access and analytics for decision support. Further

details of DW are available in an online chapter on the book’s Web site.

Figure 1.8 Data Warehouse Framework and Views.

Transaction Processing versus Analytic Processing

To illustrate the major characteristics of BI, first we will show what BI is not—namely,

transaction processing. We are all familiar with the information systems that support our

transactions, like ATM withdrawals, bank deposits, and cash register scans at the grocery store.

These transaction processing systems are constantly involved in handling updates to what we might call operational databases. For example, in an ATM withdrawal transaction, we need to reduce our bank balance accordingly; a bank deposit adds to an account; and a grocery store

purchase is likely reflected in the store’s calculation of total sales for the day, and it should

reflect an appropriate reduction in the store’s inventory for the items we bought, and so on.

These online transaction processing (OLTP) systems handle a company’s routine ongoing business. In contrast, a DW is typically a distinct system that provides storage for data that will

be used for analysis. The intent of that analysis is to give management the ability to scour data for information about the business, and it can be used to provide tactical or operational decision

support whereby, for example, line personnel can make quicker and/or more informed decisions.

DWs are intended to work with informational data used for online analytical processing (OLAP) systems. Most operational data in ERP systems—and in their complementary siblings like SCM or CRM— are stored in an OLTP system, which is a type of computer processing where the computer

responds immediately to user requests. Each request is considered to be a transaction, which is a computerized record of a discrete event, such as the receipt of inventory or a customer order. In

other words, a transaction requires a set of two or more database updates that must be completed

in an all-or-nothing fashion.

The very design that makes an OLTP system efficient for transaction processing makes it

inefficient for end-user ad hoc reports, queries, and analysis. In the 1980s, many business users

referred to their mainframes as “black holes” because all the information went into them, but

none ever came back. All requests for reports had to be programmed by the IT staff, whereas

only “precanned” reports could be generated on a scheduled basis, and ad hoc real-time querying

was virtually impossible. Although the client/server-based ERP systems of the 1990s were

somewhat more report friendly, they have still been a far cry from a desired usability by regular,

nontechnical end users for things such as operational reporting and interactive analysis. To

resolve these issues, the notions of DW and BI were created.

DWs contain a wide variety of data that present a coherent picture of business conditions at a

single point in time. The idea was to create a database infrastructure that was always online and

contained all the information from the OLTP systems, including historical data, but reorganized

and structured in such a way that it was fast and efficient for querying, analysis, and decision

support. Separating the OLTP from analysis and decision support enables the benefits of BI that

were described earlier.

A Multimedia Exercise in Business Intelligence

TUN includes videos (similar to the television show CSI) to illustrate concepts of analytics in different industries. These are called “BSI Videos (Business Scenario Investigations).” Not only

are these entertaining, but they also provide the class with some questions for discussion. For

starters, please 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 that appears on YouTube. Essentially, you have to assume the role of a customer service

center professional. An incoming flight is running late, and several passengers are likely to miss

their connecting flights. There are seats on one outgoing flight that can accommodate two of the

four passengers. Which two passengers should be given priority? You are given information

about customers’ profiles and relationships with the airline. Your decisions might change as you

learn more about those customers’ profiles.

Watch the video, pause it as appropriate, and answer the questions on which passengers should

be given priority. Then resume the video to get more information. After the video is complete,

you can see the slides related to this video and how the analysis was prepared on a slide set

at www.slideshare.net/teradata/bsi-how-we-did-it-the-case-of-the-misconnecting-passengers.

This multimedia excursion provides an example of how additional available information through

an enterprise DW can assist in decision making.

Although some people equate DSS with BI, these systems are not, at present, the same. It is

interesting to note that some people believe that DSS is a part of BI—one of its analytical tools.

Others think that BI is a special case of DSS that deals mostly with reporting, communication,

and collaboration (a form of data-oriented DSS). Another explanation (Watson, 2005) is that BI

is a result of a continuous revolution, and as such, DSS is one of BI’s original elements. Further,

as noted in the next section onward, in many circles, BI has been subsumed by the new

terms analytics or data science.

Appropriate Planning and Alignment with the Business Strategy

First and foremost, the fundamental reasons for investing in BI must be aligned with the

company’s business strategy. BI cannot simply be a technical exercise for the information

systems department. It has to serve as a way to change the manner in which the company

conducts business by improving its business processes and transforming decision-making

processes to be more data driven. Many BI consultants and practitioners involved in successful

BI initiatives advise that a framework for planning is a necessary precondition. One framework,

proposed by Gartner, Inc. (2004), decomposed planning and execution into business, organization, functionality, and infrastructure components. At the business and organizational levels, strategic and operational objectives must be defined while considering the available

organizational skills to achieve those objectives. Issues of organizational culture surrounding BI

initiatives and building enthusiasm for those initiatives and procedures for the intra-

organizational sharing of BI best practices must be considered by upper management—with

plans in place to prepare the organization for change. One of the first steps in that process is to

assess the IS organization, the skill sets of the potential classes of users, and whether the culture

is amenable to change. From this assessment, and assuming there are justification and the need to

move ahead, a company can prepare a detailed action plan. Another critical issue for BI

implementation success is the integration of several BI projects (most enterprises use several BI

projects) among themselves and with the other IT systems in the organization and its business

partners.

Gartner and many other analytics consulting organizations promoted the concept of a BI

competence center that would serve the following functions:

• A center can demonstrate how BI is clearly linked to strategy and execution of strategy.

• A center can serve to encourage interaction between the potential business user

communities and the IS organization.

• A center can serve as a repository and disseminator of best BI practices between and among

the different lines of business.

• Standards of excellence in BI practices can be advocated and encouraged throughout the

company.

• The IS organization can learn a great deal through interaction with the user communities,

such as knowledge about the variety of types of analytical tools that are needed.

• The business user community and IS organization can better understand why the DW

platform must be flexible enough to provide for changing business requirements.

• The center can help important stakeholders like high-level executives see how BI can play

an important role.

Over the last 10 years, the idea of a BI competence center has been abandoned because many

advanced technologies covered in this book have reduced the need for a central group to

organize many of these functions. Basic BI has now evolved to a point where much of it can be

done in “self-service” mode by the end users. For example, many data visualizations are easily

accomplished by end users using the latest visualization packages (Chapter 3 will introduce some

of these). As noted by Duncan (2016), the BI team would now be more focused on producing

curated data sets to enable self-service BI. Because analytics is now permeating across the whole

organization, the BI competency center could evolve into an analytics community of excellence

to promote best practices and ensure overall alignment of analytics initiatives with organizational

strategy.

BI tools sometimes needed to be integrated among themselves, creating synergy. The need for

integration pushed software vendors to continuously add capabilities to their products.

Customers who buy an all-in-one software package deal with only one vendor and do not have to

deal with system connectivity. But they may lose the advantage of creating systems composed

from the “best-of-breed” components. This led to major chaos in the BI market space. Many of

the software tools that rode the BI wave (e.g., Savvion, Vitria, Tibco, MicroStrategy, Hyperion)

have either been acquired by other companies or have expanded their offerings to take advantage

of six key trends that have emerged since the initial wave of surge in business intelligence:

• Big Data.

• Focus on customer experience as opposed to just operational efficiency.

• Mobile and even newer user interfaces—visual, voice, mobile.

• Predictive and prescriptive analytics, machine learning, artificial intelligence.

• Migration to cloud.

• Much greater focus on security and privacy protection.

This book covers many of these topics in significant detail by giving examples of how the

technologies are evolving and being applied, and the managerial implications.

Section 1.4 Review Questions

1. List three of the terms that have been predecessors of analytics.

2. What was the primary difference between the systems called MIS, DSS, and Executive Information Systems?

3. Did DSS evolve into BI or vice versa? 4. Define BI. 5. List and describe the major components of BI. 6. Define OLTP. 7. Define OLAP. 8. List some of the implementation topics addressed by Gartner’s report. 9. List some other success factors of BI.

1.5 Analytics Overview

The word analytics has largely replaced the previous individual components of

computerized decision support technologies that have been available under various

labels in the past. Indeed, many practitioners and academics now use the

word analytics in place of BI. Although many authors and consultants have defined it

slightly differently, one can view analytics as the process of developing actionable

decisions or recommendations for actions based on insights generated from historical

data. According to the Institute for Operations Research and Management Science

(INFORMS), analytics represents the combination of computer technology,

management science techniques, and statistics to solve real problems. Of course, many

other organizations have proposed their own interpretations and motivations for

analytics. For example, SAS Institute Inc. proposed eight levels of analytics that begin

with standardized reports from a computer system. These reports essentially provide a

sense of what is happening with an organization. Additional technologies have enabled

us to create more customized reports that can be generated on an ad hoc basis. The next

extension of reporting takes us to OLAP-type queries that allow a user to dig deeper

and determine specific sources of concern or opportunities. Technologies available

today can also automatically issue alerts for a decision maker when performance

warrants such alerts. At a consumer level, we see such alerts for weather or other issues.

But similar alerts can also be generated in specific settings when sales fall above or

below a certain level within a certain time period or when the inventory for a specific

product is running low. All of these applications are made possible through analysis

and queries of data being collected by an organization. The next level of analysis might

entail statistical analysis to better understand patterns. These can then be taken a step

further to develop forecasts or models for predicting how customers might respond to a

specific marketing campaign or ongoing service/product offerings. When an

organization has a good view of what is happening and what is likely to happen, it can

also employ other techniques to make the best decisions under the circumstances.

This idea of looking at all the data to understand what is happening, what will happen,

and how to make the best of it has also been encapsulated by INFORMS in proposing

three levels of analytics. These three levels are identified as descriptive, predictive, and

prescriptive. Figure 1.9 presents a graphical view of these three levels of analytics. It

suggests that these three are somewhat independent steps and one type of

analytics applications leads to another. It also suggests that there is actually some

overlap across these three types of analytics. In either case, the interconnected nature of

different types of analytics applications is evident. We next introduce these three levels

of analytics. Figure 1.9 Three Types of Analytics.

Descriptive Analytics

Descriptive (or reporting) analytics refers to knowing what is happening in the organization and understanding some underlying trends and causes of such occurrences. First, this involves

the consolidation of data sources and availability of all relevant data in a form that enables

appropriate reporting and analysis. Usually, the development of this data infrastructure is part of

DWs. From this data infrastructure, we can develop appropriate reports, queries, alerts, and

trends using various reporting tools and techniques.

A significant technology that has become a key player in this area is visualization. Using the

latest visualization tools in the marketplace, we can now develop powerful insights in the

operations of our organization. Application Cases 1.3 and 1.4 highlight some such applications.

Application Case 1.3 Silvaris Increases Business with Visual Analysis and Real- Time Reporting Capabilities

Silvaris Corporation was founded in 2000 by a team of forest industry professionals to provide

technological advancement in the lumber and building material sector. Silvaris is the first e--

commerce platform in the United States specifically for forest products and is headquartered in

Seattle, Washington. It is a leading wholesale provider of industrial wood products and surplus

building materials.

Silvaris sells its products and provides international logistics services to more than 3,500

customers. To manage various processes that are involved in a transaction, the company created

a proprietary online trading platform to track information flow related to transactions between

traders, accounting, credit, and logistics. This allowed Silvaris to share its real-time information

with its customers and partners. But due to the rapidly changing prices of materials, it became

necessary for Silvaris to get a real-time view of data without moving them into a separate

reporting format.

Silvaris started using Tableau because of its ability to connect with and visualize live data. With

dashboards created by Tableau that are easy to understand and explain, Silvaris started using it

for reporting purposes. This helped Silvaris in pulling out information quickly from the data and

identifying issues that impact its business. Silvaris succeeded in managing online versus offline

orders with the help of reports generated by Tableau. Now, Silvaris keeps track of online orders

placed by customers and knows when to send renew pushes to which customers to keep them

purchasing online. Also, analysts of Silvaris can save time by generating dashboards instead of

writing hundreds of pages of reports by using Tableau.

Sources: Tableau.com. “Silvaris Augments Proprietary Technology Platform with Tableau’s Real-Time Reporting Capabilities.” http://www.tableau.com/sites/default/files/case-studies/silvaris-business-dashboards_0.pdf (accessed September 2018); Silvaris.com. http://www.silvaris.com (accessed September 2018).

Questions for Case 1.3

1. What was the challenge faced by Silvaris? 2. How did Silvaris solve its problem using data visualization with Tableau?

What We Can Learn from This Application Case

Many industries need to analyze data in real time. Real-time analysis enables the analysts to

identify issues that impact their business. Visualization is sometimes the best way to begin

analyzing the live data streams. Tableau is one such data visualization tool that has the capability

to analyze live data without bringing live data into a separate reporting format.

Application Case 1.4 Siemens Reduces Cost with the Use of Data Visualization

Siemens is a German company headquartered in Berlin, Germany. It is one of the world’s largest

companies focusing on the areas of electrification, automation, and digitalization. It has an

annual revenue of 76 billion euros.

The visual analytics group of Siemens is tasked with end-to-end reporting solutions and

consulting for all of Siemens internal BI needs. This group was facing the challenge of providing

reporting solutions to the entire Siemens organization across different departments while

maintaining a balance between governance and self-service capabilities. Siemens needed a

platform that could analyze its multiple cases of customer satisfaction surveys, logistic processes,

and financial reporting. This platform should be easy to use for their employees so that they

could use these data for analysis and decision making. In addition, the platform should be easily

integrated with existing Siemens systems and give employees a seamless user experience.

Siemens started using Dundas BI, a leading global provider of BI and data visualization

solutions. It allowed Siemens to create highly interactive dashboards that enabled it to detect

issues early and thus save a significant amount of money. The dashboards developed by Dundas

BI helped Siemens global logistics organization answer questions like how different supply rates

at different locations affect the operation, thus helping the company reduce cycle time by 12

percent and scrap cost by 25 percent.

Questions for Case 1.4

1. What challenges were faced by Siemens visual analytics group? 2. How did the data visualization tool Dundas BI help Siemens in reducing cost?

What We Can Learn from This Application Case

Many organizations want tools that can be used to analyze data from multiple divisions. These

tools can help them improve performance and make data discovery transparent to their users so

that they can identify issues within the business easily.

Sources: Dundas.com. “How Siemens Drastically Reduced Cost with Managed BI Applications.” https://www.dundas.com/Content/pdf/siemens-case-study.pdf (accessed September 2018); Wikipedia.org. “SIEMENS.” https://en.wikipedia.org/wiki/Siemens (accessed September 2018); Siemens.com. “About Siemens.” http://www.siemens.com/about/en/ (accessed September 2018).

Predictive Analytics

Predictive analytics 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. The goal of these techniques is to be able to predict whether the customer is likely to switch to a competitor (“churn”), what and how much the

customer would likely buy next, what promotions the customer would respond to, whether the

customer is a creditworthy risk, and so forth. A number of techniques are used in developing

predictive analytical applications, including various classification algorithms. For example, as

described in Chapters 4 and 5, we can use classification techniques such as logistic regression,

decision tree models, and neural networks to predict how well a motion picture will do at the box

office. We can also use clustering algorithms for segmenting customers into different clusters to

be able to target specific promotions to them. Finally, we can use association mining techniques

(Chapters 4 and 5) to estimate relationships between different purchasing behaviors. That is, if a

customer buys one product, what else is the customer likely to purchase? Such analysis can assist

a retailer in recommending or promoting related products. For example, any product search

on Amazon.com results in the retailer also suggesting similar products that a customer may be

interested in. We will study these techniques and their applications

in Chapters 3 through 6. Application Case 1.5 illustrates one such application in sports.

Application Case 1.5 Analyzing Athletic Injuries

Any athletic activity is prone to injuries. If the injuries are not handled properly, then the team

suffers. Using analytics to understand injuries can help in deriving valuable insights that would

enable coaches and team doctors to manage the team composition, understand player profiles,

and ultimately aid in better decision making concerning which players might be available to play

at any given time.

In an exploratory study, Oklahoma State University analyzed U.S. football-related sports injuries

by using reporting and predictive analytics. The project followed the Cross-Industry Standard

Process for Data Mining (CRISP-DM) methodology (to be described in Chapter 4) to understand

the problem of making recommendations on managing injuries, understanding the various data

elements collected about injuries, cleaning the data, developing visualizations to draw various

inferences, building predictive models to analyze the injury healing time period, and drawing

sequence rules to predict the relationships among the injuries and the various body part parts

afflicted with injuries.

The injury data set consisted of more than 560 football injury records, which were categorized

into injury-specific variables—body part/site/laterality, action taken, severity, injury type, injury

start and healing dates—and player/sport-specific variables—player ID, position played, activity,

onset, and game location. Healing time was calculated for each record, which was classified into

different sets of time periods: 0–1 month, 1–2 months, 2–4 months, 4–6 months, and 6–24

months.

Various visualizations were built to draw inferences from injury–data set information depicting

the healing time period associated with players’ positions, severity of injuries and the healing

time period, treatment offered and the associated healing time period, major injuries afflicting

body parts, and so forth.

Neural network models were built to predict each of the healing categories using IBM SPSS

Modeler. Some of the predictor variables were current status of injury, severity, body part, body

site, type of injury, activity, event location, action taken, and position played. The success of

classifying the healing category was quite good: Accuracy was 79.6 percent. Based on the

analysis, many recommendations were suggested, including employing more specialists’ input

from injury onset instead of letting the training room staff screen the injured players; training

players at defensive positions to avoid being injured; and holding practice to thoroughly safety-

check mechanisms.

Sources: “Sharda, R., Asamoah, D., & Ponna, N. (2013). “Research and Pedagogy in Business Analytics: Opportunities and Illustrative Examples.” Journal of Computing and Information Technology, 21(3), pp. 171–182.

Questions for Case 1.5

1. What types of analytics are applied in the injury analysis? 2. How do visualizations aid in understanding the data and delivering insights into

the data? 3. What is a classification problem? 4. What can be derived by performing sequence analysis?

What We Can Learn from This Application Case

For any analytics project, it is always important to understand the business domain and the

current state of the business problem through extensive analysis of the only resource—historical

data. Visualizations often provide a great tool for gaining the initial insights into data, which can

be further refined based on expert opinions to identify the relative importance of the data

elements related to the problem. Visualizations also aid in generating ideas for obscure problems,

which can be pursued in building PMs that could help organizations in decision making.

Prescriptive Analytics

The third category of analytics is termed prescriptive analytics. The goal of prescriptive analytics is to recognize what is going on as well as the likely forecast and make decisions to

achieve the best performance possible. This group of techniques has historically been studied

under the umbrella of OR or management sciences and is generally aimed at optimizing the

performance of a system. The goal here is to provide a decision or a recommendation for a

specific action. These recommendations can be in the form of a specific yes/no decision for a

problem, a specific amount (say, price for a specific item or airfare to charge), or a complete set

of production plans. The decisions may be presented to a decision maker in a report or may be

used directly in an automated decision rules system (e.g., in airline pricing systems). Thus, these

types of analytics can also be termed decision or normative analytics. Application Case 1.6 gives an example of such prescriptive analytic applications. We will learn about some

aspects of prescriptive analytics in Chapter 8.

Application Case 1.6A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates

This application case is based on a project that involved one of the coauthors A company that

does not wish to disclose its name (or even its precise industry) was facing a major problem of

making decisions on which inventory of raw materials to use to satisfy which customers. This

company supplies custom configured steel bars to its customers. These bars may be cut into

specific shapes or sizes and may have unique material and finishing requirements. The company

procures raw materials from around the world and stores them in its warehouse. When a

prospective customer calls the company to request a quote for the specialty bars meeting specific

material requirements (composition, origin of the metal, quality, shapes, sizes, etc.), the

salesperson usually has just a little bit of time to submit such a quote including the date when the

product can be delivered and, of course, prices, and so on. It must make available-to-promise

(ATP) decisions, which determine in real time the dates when the salesperson can promise

delivery of products that customers requested during the quotation stage. Previously, a

salesperson had to make such decisions by analyzing reports on available inventory of raw

materials. Some of the available raw material may have already been committed to another

customer’s order. Thus, the inventory in stock might not really be inventory available. On the

other hand, there may be raw material that is expected to be delivered in the near future that

could also be used for satisfying the order from this prospective customer. Finally, there might

even be an opportunity to charge a premium for a new order by repurposing previously

committed inventory to satisfy this new order while delaying an already committed order. Of

course, such decisions should be based on the cost–benefit analyses of delaying a previous order.

The system should thus be able to pull real-time data about inventory, committed orders,

incoming raw material, production constraints, and so on.

To support these ATP decisions, a real-time DSS was developed to find an optimal assignment

of the available inventory and to support additional what-if analysis. The DSS uses a suite of

mixed-integer programming models that are solved using commercial software. The company

has incorporated the DSS into its enterprise resource planning system to seamlessly facilitate its

use of business analytics.

Questions for Case 1.6

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

2. How could a DSS help make these decisions?

Source: M. Pajouh Foad, D. Xing, S. Hariharan, Y. Zhou, B. Balasundaram, T. Liu, & R. Sharda, R. (2013). “Available-to-Promise in Practice: An Application of Analytics in the Specialty Steel Bar Products Industry.” Interfaces, 43(6), pp. 503– 517. http://dx.doi.org/10.1287/inte.2013.0693 (accessed September 2018).

Analytics Applied to Different Domains

Applications of analytics in various industry sectors have spawned many related areas or at least

buzzwords. It is almost fashionable to attach the word analytics to any specific industry or type of data. Besides the general category of text analytics—aimed at getting value out of text (to be

studied in Chapter 7)—or Web analytics—analyzing Web data streams (also in Chapter 7)—

many industry- or problem-specific analytics professions/streams have been developed.

Examples of such areas are marketing analytics, retail analytics, fraud analytics, transportation

analytics, health analytics, sports analytics, talent analytics, behavioral analytics, and so forth.

For example, we will soon see several applications in sports analytics. Application Case 1.5 could also be termed a case study in health analytics. The next section will introduce

health analytics and market analytics broadly. Literally, any systematic analysis of data in a

specific sector is being labeled as “(fill-in-blanks)” analytics. Although this may result in

overselling the concept of analytics, the benefit is that more people in specific industries are

aware of the power and potential of analytics. It also provides a focus to professionals

developing and applying the concepts of analytics in a vertical sector. Although many of the

techniques to develop analytics applications may be common, there are unique issues within each

vertical segment that influence how the data may be collected, processed, analyzed, and the

applications implemented. Thus, the differentiation of analytics based on a vertical focus is good

for the overall growth of the discipline.

Analytics or Data Science?

Even as the concept of analytics is receiving more attention in industry and academic circles,

another term has already been introduced and is becoming popular. The new term is data science. Thus, the practitioners of data science are data scientists. D. J. Patil of LinkedIn is sometimes credited with creating the term data science. There have been some attempts to describe the differences between data analysts and data scientists (e.g., see “Data Science

Revealed,” 2018) (emc.com/collateral/about/news/emc-data-science-study-wp.pdf). One view is

that data analyst is just another term for professionals who were doing BI in the form of data compilation, cleaning, reporting, and perhaps some visualization. Their skill sets included Excel

use, some SQL knowledge, and reporting. You would recognize those capabilities as descriptive

or reporting analytics. In contrast, data scientists are responsible for predictive analysis,

statistical analysis, and use of more advanced analytical tools and algorithms. They may have a

deeper knowledge of algorithms and may recognize them under various labels—data mining,

knowledge discovery, or machine learning. Some of these professionals may also need deeper

programming knowledge to be able to write code for data cleaning/analysis in current Web-

oriented languages such as Java or Python and statistical languages such as R. Many analytics

professionals also need to build significant expertise in statistical modeling, experimentation, and

analysis. Again, our readers should recognize that these fall under the predictive and prescriptive

analytics umbrella. However, prescriptive analytics also includes more significant expertise in

OR including optimization, simulation, and decision analysis. Those who cover these fields are

more likely to be called data scientists than analytics professionals. Our view is that the distinction between analytics professional and data scientist is more of a

degree of technical knowledge and skill sets than functions. It may also be more of a distinction

across disciplines. Computer science, statistics, and applied mathematics programs appear to

prefer the data science label, reserving the analytics label for more business-oriented

professionals. As another example of this, applied physics professionals have proposed

using network science as the term for describing analytics that relate to groups of people—social networks, supply chain networks, and so forth. See http://barabasi.com/networksciencebook/ for

an evolving textbook on this topic.

Aside from a clear difference in the skill sets of professionals who only have to do

descriptive/reporting analytics versus those who engage in all three types of analytics, the

distinction between the two labels is fuzzy at best. We observe that graduates of our analytics

programs tend to be responsible for tasks that are more in line with data science professionals (as

defined by some circles) than just reporting analytics. This book is clearly aimed at introducing

the capabilities and functionality of all analytics (which include data science), not just reporting

analytics. From now on, we will use these terms interchangeably.

What is Big Data?

Any book on analytics and data science has to include significant coverage of what is called Big Data analytics. We cover it in Chapter 9 but here is a very brief introduction. Our brains work extremely quickly and efficiently and are versatile in processing large amounts of all kinds of

data: images, text, sounds, smells, and video. We process all different forms of data relatively

easily. Computers, on the other hand, are still finding it hard to keep up with the pace at which

data are generated, let alone analyze them quickly. This is why we have the problem of Big Data.

So, what is Big Data? Simply put, Big Data refers to data that cannot be stored in a single storage

unit. Big Data typically refers to data that come in many different forms: structured,

unstructured, in a stream, and so forth. Major sources of such data are clickstreams from Web

sites, postings on social media sites such as Facebook, and data from traffic, sensors, or weather.

A Web search engine such as Google needs to search and index billions of Web pages to give

you relevant search results in a fraction of a second. Although this is not done in real time,

generating an index of all the Web pages on the Internet is not an easy task. Luckily for Google,

it was able to solve this problem. Among other tools, it has employed Big Data analytical

techniques.

There are two aspects to managing data on this scale: storing and processing. If we could

purchase an extremely expensive storage solution to store all this at one place on one unit,

making this unit fault tolerant would involve a major expense. An ingenious solution was

proposed that involved storing these data in chunks on different machines connected by a

network—putting a copy or two of this chunk in different locations on the network, both

logically and physically. It was originally used at Google (then called the Google File System)

and later developed and released by an Apache project as the Hadoop Distributed File System

(HDFS).

However, storing these data is only half of the problem. Data are worthless if they do not provide

business value, and for them to provide business value, they must be analyzed. How can such

vast amounts of data be analyzed? Passing all computation to one powerful computer does not

work; this scale would create a huge overhead on such a powerful computer. Another ingenious

solution was proposed: Push computation to the data instead of pushing data to a computing

node. This was a new paradigm and gave rise to a whole new way of processing data. This is

what we know today as the MapReduce programming paradigm, which made processing Big

Data a reality. MapReduce was originally developed at Google, and a subsequent version was

released by the Apache project called Hadoop MapReduce. Today, when we talk about storing, processing, or analyzing Big Data, HDFS and MapReduce

are involved at some level. Other relevant standards and software solutions have been proposed.

Although the major toolkit is available as an open source, several companies have been launched

to provide training or specialized analytical hardware or software services in this space. Some

examples are HortonWorks, Cloudera, and Teradata Aster.

Over the past few years, what was called Big Data changed more and more as Big Data

applications appeared. The need to process data coming in at a rapid rate added velocity to the

equation. An example of fast data processing is algorithmic trading. This uses electronic

platforms based on algorithms for trading shares on the financial market, which operates in

microseconds. The need to process different kinds of data added variety to the equation. Another

example of a wide variety of data is sentiment analysis, which uses various forms of data from

social media platforms and customer responses to gauge sentiments. Today, Big Data is

associated with almost any kind of large data that have the characteristics of volume, velocity,

and variety. As noted before, these are evolving quickly to encompass stream analytics, IoT,

cloud computing, and deep learning–enabled AI. We will study these in various chapters in the

book.

Section 1.5 Review Questions

1. Define analytics. 2. What is descriptive analytics? What are the various tools that are employed in

descriptive analytics? 3. How is descriptive analytics different from traditional reporting? 4. What is a DW? How can DW technology help enable analytics? 5. What is predictive analytics? How can organizations employ predictive analytics? 6. What is prescriptive analytics? What kinds of problems can be solved by

prescriptive analytics?

7. Define modeling from the analytics perspective. 8. Is it a good idea to follow a hierarchy of descriptive and predictive analytics

before applying prescriptive analytics? 9. How can analytics aid in objective decision making? 10. What is Big Data analytics? 11. What are the sources of Big Data? 12. What are the characteristics of Big Data? 13. What processing technique is applied to process Big Data?

1.6 Analytics Examples in Selected Domains

You will see examples of analytics applications throughout various chapters. That is

one of the primary approaches (exposure) of this book. In this section, we highlight

three application areas—sports, healthcare, and retail—where there have been the most

reported applications and successes.

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

The application of analytics to business problems is a key skill, one that you will learn in this

book. Many of these techniques are now being applied to improve decision making in all aspects

of sports, a very hot area called sports analytics. It is the art and science of gathering data about athletes and teams to create insights that improve sports decisions, such as deciding which

players to recruit, how much to pay them, who to play, how to train them, how to keep them

healthy, and when they should be traded or retired. For teams, it involves business decisions such

as ticket pricing as well as roster decisions, analysis of each competitor’s strengths and

weaknesses, and many game-day decisions.

Indeed, sports analytics is becoming a specialty within analytics. It is an important area because

sport is a big business, generating about $145$145 billion in revenues each year plus an additional $100$100 billion in legal and $300$300 billion in illegal gambling, according to Price Waterhouse (“Changing the Game: Outlook for the Global Sports Market to 2015” (2015)).

In 2014, only $125$125 million was spent on analytics (less than 0.1 percent of revenues). This is expected to grow at a healthy rate to $4.7$4.7 billion by 2021 (“Sports Analytics Market Worth $4.7B$4.7B by 2021” (2015)). The use of analytics for sports was popularized by the Moneyball book by Michael Lewis in 2003 and the movie starring Brad Pitt in 2011. It showcased Oakland A’s general manager Billy

Beane and his use of data and analytics to turn a losing team into a winner. In particular, he hired

an analyst who used analytics to draft players who were able to get on base as opposed to players

who excelled at traditional measures like runs batted in or stolen bases. These insights allowed

the team to draft prospects overlooked by other teams at reasonable starting salaries. It worked—

the team made it to the playoffs in 2002 and 2003.

Now analytics are being used in all parts of sports. The analytics can be divided between the

front office and back office. A good description with 30 examples appears in Tom Davenport’s

survey article (). Front-office business analytics include analyzing fan behavior ranging from

predictive models for season ticket renewals and regular ticket sales to scoring tweets by fans

regarding the team, athletes, coaches, and owners. This is very similar to traditional CRM.

Financial analysis is also a key area such as when salary cap (for pros) or scholarship (for

colleges) limits are part of the equation.

Back-office uses include analysis of both individual athletes and team play. For individual

players, there is a focus on recruitment models and scouting analytics, analytics for strength and

fitness as well as development, and PMs for avoiding overtraining and injuries. Concussion

research is a hot field. Team analytics include strategies and tactics, competitive assessments,

and optimal roster choices under various on-field or on-court situations.

The following representative examples illustrate how two sports organizations use data and

analytics to improve sports operations in the same way that analytics have improved traditional

industry decision making.

Example 1: The Business Office Dave Ward works as a business analyst for a major pro baseball team, focusing on revenue. He

analyzes ticket sales, both from season ticket holders and single-ticket buyers. Sample questions

in his area of responsibility include why season ticket holders renew (or do not renew) their

tickets as well as what factors drive last-minute individual seat ticket purchases. Another

question is how to price the tickets.

Some of the analytical techniques Dave uses include simple statistics on fan behavior such as

overall attendance and answers to survey questions about likelihood to purchase again. However,

what fans say versus what they do can be different. Dave runs a survey of fans by ticket seat

location (“tier”) and asks about their likelihood of renewing their season tickets. But when he

compares what they say versus what they do, he discovers big differences. (See Figure 1.10.) He

found that 69 percent of fans in Tier 1 seats who said on the survey that they would “probably

not renew” actually did. This is useful insight that leads to action—customers in the green cells

are the most likely to renew tickets and so require fewer marketing touches and dollars to convert

compared to customers in the blue cells.

Figure 1.10 Season Ticket Renewals—Survey Scores.

However, many factors influence fan ticket purchase behavior, especially price, which

drives more sophisticated statistics and data analysis. For both areas, but especially

single-game tickets, Dave is driving the use of dynamic pricing—moving the business

from simple static pricing by seat location tier to day-by-day up-and-down pricing of

individual seats. This is a rich research area for many sports teams and has huge upside

potential for revenue enhancement. For example, his pricing takes into account the

team’s record, who they are playing, game dates and times, which star athletes play for

each team, each fan’s history of renewing season tickets or buying single tickets, and

factors such as seat location, number of seats, and real-time information like traffic

congestion historically at game time and even the weather. See Figure 1.11. Figure 1.11 Dynamic Pricing Previous Work—Major League Baseball.

Source: Based on C. Kemper and C. Breuer, “How Efficient is Dynamic Pricing for Sports Events? Designing a Dynamic Pricing

Model for Bayern Munich”, Intl. Journal of Sports Finance, 11, pp. 4–25, 2016.

Figure 1.11 Full Alternative Text

Which of these factors are important and by how much? Given his extensive statistics

background, Dave builds regression models to pick out key factors driving these historic

behaviors and create PMs to identify how to spend marketing resources to drive revenues. He

builds churn models for season ticket holders to create segments of customers who will renew,

will not renew, or are fence-sitters, which then drives more refined marketing campaigns.

In addition, Dave does sentiment scoring on fan comments such as tweets that help him segment

fans into different loyalty segments. Other studies about single-game attendance drivers help the

marketing department understand the impact of giveaways like bobble-heads or T-shirts or

suggestions on where to make spot TV ad buys.

Beyond revenues, there are many other analytical areas that Dave’s team works on, including

merchandising, TV and radio broadcast revenues, inputs to the general manager on salary

negotiations, draft analytics especially given salary caps, promotion effectiveness including

advertising channels, and brand awareness, as well as partner analytics. He’s a very busy guy!

Example 2: The Coach Bob Breedlove is the football coach for a major college team. For him, everything is about

winning games. His areas of focus include recruiting the best high school players, developing

them to fit his offense and defense systems, and getting maximum effort from them on game

days. Sample questions in his area of responsibility include: Whom do we recruit? What drills

help develop their skills? How hard do I push our athletes? Where are opponents strong or weak,

and how do we figure out their play tendencies?

Fortunately, his team has hired a new team operations expert, Dar Beranek, who specializes in

helping the coaches make tactical decisions. She is working with a team of student interns who

are creating opponent analytics. They used the coach’s annotated game film to build a cascaded

decision tree model (Figure 1.12) to predict whether the next play will be a running play or

passing play. For the defensive coordinator, they have built heat maps (Figure 1.13) of each

opponent’s passing offense, illustrating their tendencies to throw left or right and into which

defensive coverage zones. Finally, they built some time-series analytics (Figure 1.14) on

explosive plays (defined as a gain of more than 16 yards for a passing play or more than 12 yards

for a run play). For each play, they compare the outcome with their own defensive formations

and the other team’s offensive formations, which help Coach Breedlove react more quickly to

formation shifts during a game. We explain the analytical techniques that generated these figures

in much more depth in Chapters 3–6 and Chapter 9.

Figure 1.12 Cascaded Decision Tree for Run or Pass Plays.

Source: Contributed by Dr. Dave Schrader, who retired after 24 years in advanced development and marketing at Teradata. He

has remained on the Board of Advisors of the Teradata University Network, where he spends his retirement helping students and

faculty learn more about sports analytics. Graphics by Peter Liang and Jacob Pearson, graduate students at Oklahoma State

University, as part of a student project in the spring of 2016 in Prof. Ramesh Sharda’s class under Dr. Dave Schrader’s coaching.

Figure 1.13 Heat Map Zone Analysis for Passing Plays.

Source: Contributed by Dr. Dave Schrader, who retired after 24 years in advanced development and marketing at Teradata. He

has remained on the Board of Advisors of the Teradata University Network, where he spends his retirement helping students and

faculty learn more about sports analytics. Graphics by Peter Liang and Jacob Pearson, graduate students at Oklahoma State

University, as part of a student project in the spring of 2016 in Prof. Ramesh Sharda’s class under Dr. Dave Schrader’s coaching.

Figure 1.14 Time-Series Analysis of Explosive Plays.

New work that Dar is fostering involves building better high school athlete recruiting models.

For example, each year the team gives scholarships to three students who are wide receiver

recruits. For Dar, picking out the best players goes beyond simple measures like how fast

athletes run, how high they jump, or how long their arms are to newer criteria like how quickly

they can rotate their heads to catch a pass, what kinds of reaction times they exhibit to multiple

stimuli, and how accurately they run pass routes. Some of her ideas illustrating these concepts

appear on the TUN Web site; look for the Business Scenario Investigation (2015) “The Case of

Precision Football.”

What Can We Learn from These Examples?

Beyond the front-office business analysts, the coaches, trainers, and performance experts, there

are many other people in sports who use data, ranging from golf groundskeepers who measure

soil and turf conditions for PGA tournaments to baseball and basketball referees who are rated

on the correct and incorrect calls they make. In fact, it is hard to find an area of sports that

is not being impacted by the availability of more data, especially from sensors. Skills you will learn in this book for business analytics will apply to sports. If you want to dig

deeper into this area, we encourage you to look at the Sports Analytics section of the TUN, a free

resource for students and faculty. On its Web site, you will find descriptions of what to read to

find out more about sports analytics, compilations of places where you can find publically

available data sets for analysis, as well as examples of student projects in sports analytics and

interviews of sports professionals who use data and analytics to do their jobs. Good luck learning

analytics!

Analytics Applications in Healthcare—Humana Examples

Although healthcare analytics span a wide variety of applications from prevention to diagnosis to

efficient operations and fraud prevention, we focus on some applications that have been

developed at a major health insurance company in the United States, Humana. According to its

Web site, “The company’s strategy integrates care delivery, the member experience, and clinical

and consumer insights to encourage engagement, behavior change, proactive clinical outreach

and wellness. . . .” Achieving these strategic goals includes significant investments in information

technology in general and analytics in particular. Brian LeClaire is senior vice president and CIO

of Humana. He has a PhD in MIS from Oklahoma State University. He has championed

analytics as a competitive differentiator at Humana—including cosponsoring the creation of a

center for excellence in analytics. He described the following projects as examples of Humana’s

analytics initiatives, led by Humana’s chief clinical analytics officer, Vipin Gopal.

Humana Example 1: Preventing Falls in a Senior Population— An Analytic Approach Accidental falls are a major health risk for adults age 65 years and older with one-third

experiencing a fall every year.1 The costs of falls pose a significant strain on the U.S. healthcare

system; the direct costs of falls were estimated at $34$34 billion in 2013 alone.1 With the percent of seniors in the U.S. population on the rise, falls and associated costs are anticipated to

increase. According to the Centers for Disease Control and Prevention (CDC), “Falls are a public

health problem that is largely preventable”

(www.cdc.gov/homeandrecreationalsafety/falls/adultfalls.html).1 Falls are also the leading factor for both fatal and nonfatal injuries in older adults with injurious falls increasing

the risk of disability by up to 50 percent (Gill et al., 2013).2 Humana is the nation’s second-

largest provider of Medicare Advantage benefits with approximately 3.2 million members, most

of whom are seniors. Keeping its senior members well and helping them live safely at their

homes is a key business objective of which prevention of falls is an important component.

However, no rigorous methodology was available to identify individuals most likely to fall, for

whom falls prevention efforts would be beneficial. Unlike chronic medical conditions such as

diabetes and cancer, a fall is not a well-defined medical condition. In addition, falls are usually

underreported in claims data as physicians typically tend to code the consequence of a fall such

as fractures and dislocations. Although many clinically administered assessments to identify

fallers exist, they have limited reach and lack sufficient predictive power (Gates et al.,

2008).3 As such, there is a need for a prospective and accurate method to identify individuals at

greatest risk of falling so that they can be proactively managed for fall prevention. The Humana

analytics team undertook the development of a Falls Predictive Model in this context. It is the

first comprehensive PM reported that utilizes administrative medical and pharmacy claims,

clinical data, temporal clinical patterns, consumer information, and other data to identify

individuals at high risk of falling over a time horizon.

Today, the Falls PM is central to Humana’s ability to identify seniors who could benefit from fall

mitigation interventions. An initial proof-of-concept with Humana consumers, representing the

top 2 percent of those at the highest risk of falling, demonstrated that the consumers had

increased utilization of physical therapy services, indicating consumers are taking active steps to

reduce their risk for falls. A second initiative utilizes the Falls PM to identify high-risk

individuals for remote monitoring programs. Using the PM, Humana was able to identify 20,000

consumers at a high risk of falling who benefited from this program. Identified consumers wear a

device that detects falls and alerts a 24/724/7 service for immediate assistance. This work was recognized by the Analytics Leadership Award by Indiana University Kelly

School of Business in 2015, for innovative adoption of analytics in a business environment.

Contributors: Harpreet Singh, PhD; Vipin Gopal, PhD; Philip Painter, MD.

Humana Example 2: Humana’s Bold Goal—Application of Analytics to Define the

Right Metrics In 2014, Humana, Inc. announced its organization’s Bold Goal to improve the health of the

communities it serves by 20 percent by 2020 by making it easy for people to achieve their best

health. The communities that Humana serves can be defined in many ways, including

geographically (state, city, neighborhood), by product (Medicare Advantage, employer-based

plans, individually purchased), or by clinical profile (priority conditions including diabetes,

hypertension, congestive heart failure [CHF], coronary artery disease [CAD], chronic obstructive

pulmonary disease [COPD], or depression). Understanding the health of these communities and

how they track over time is critical not only for the evaluation of the goal, but also in crafting

strategies to improve the health of the whole membership in its entirety.

A challenge before the analytics organization was to identify a metric that captures the essence

of the Bold Goal. Objectively measured traditional health insurance metrics such as hospital

admissions or emergency room visits per 1,000 persons would not capture the spirit of this new

mission. The goal was to identify a metric that captures health and its improvement in a

community and was relevant to Humana as a business. Through rigorous analytic evaluations,

Humana eventually selected “Healthy Days,” a four-question, quality-of-life questionnaire

originally developed by the CDC to track and measure Humana’s overall progress toward the

Bold Goal.

It was critical to make sure that the selected metric was highly correlated to health and business

metrics so that any improvement in Healthy Days resulted in improved health and better business

results. Some examples of how “Healthy Days” is correlated to metrics of interest include the

following:

• Individuals with more unhealthy days (UHDs) exhibit higher utilization and cost patterns.

For a five-day increase in UHDs, there are (1) an $82$82 increase in average monthly medical and pharmacy costs, (2) an increase of 52 inpatient admits per 1,000 patients, and

(3) a 0.28-day increase in average length of stay (Havens, Peña, Slabaugh, Cordier, Renda,

& Gopal, 2015).1

• Individuals who exhibit healthy behaviors and have their chronic conditions well managed

have fewer UHDs. For example, when we look at individuals with diabetes, UHDs are

lower if they obtained an LDL screening (−4.3−4.3 UHDs) or a diabetic eye exam (−2.3−2.3 UHDs). Likewise, if they have controlled blood sugar levels measured by HbA1C (−1.8−1.8 UHDs) or LDL levels (−1.3−1.3 UHDs) (Havens, Slabaugh, Peña, Haugh, & Gopal 2015).2

• Individuals with chronic conditions have more UHDs than those who do not have (1) CHF

(16.9 UHDs), (2) CAD (14.4 UHDs), (3) hypertension (13.3 UHDs), (4) diabetes (14.7

UHDs), (5) COPD (17.4 UHDs), or (6) depression (22.4 UHDs) (Havens, Peña, Slabaugh et

al., 2015; Chiguluri, Guthikonda, Slabaugh, Havens, Peña, & Cordier, 2015; Cordier et al.,

2015).1,3,4

Humana has since adopted Healthy Days as their metric for the measurement of progress toward

Bold Goal (Humana, http://populationhealth.humana.com/wp-

content/uploads/2016/05/BoldGoal2016ProgressReport_1.pdf).5 Contributors: Tristan Cordier, MPH; Gil Haugh, MS; Jonathan Peña, MS; Eriv Havens, MS; Vipin Gopal, PhD.

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

Health Insurer

The 80/2080/20 rule generally applies in healthcare; that is, roughly 20 percent of consumers account for 80 percent of healthcare resources due to their deteriorating health and chronic

conditions. Health insurers like Humana have typically enrolled the highest-risk enrollees in

clinical and disease management programs to help manage the chronic conditions the members

have.

Identification of the correct members is critical for this exercise, and in the recent years, PMs

have been developed to identify enrollees with high future risk. Many of these PMs were

developed with heavy reliance on medical claims data, which results from the medical services

that the enrollees use. Because of the lag that exists in submitting and processing claims data,

there is a corresponding lag in identification of high-risk members for clinical program

enrollment. This issue is especially relevant when new members join a health insurer as they

would not have a claims history with an insurer. A claims-based PM could take on average of 9–

12 months after enrollment of new members to identify them for referral to clinical programs.

In the early part of this decade, Humana attracted large numbers of new members in its Medicare

Advantage products and needed a better way to clinically manage this membership. As such, it

became extremely important that a different analytic approach be developed to rapidly and

accurately identify high-risk new members for clinical management, to keep this group healthy

and costs down.

Humana’s Clinical Analytics team developed the New Member Predictive Model (NMPM) that

would quickly identify at-risk individuals soon after their new plan enrollments with Humana

rather than waiting for sufficient claim history to become available for compiling clinical profiles

and predicting future health risk. Designed to address the unique challenges associated with new

members, NMPM developed a novel approach that leveraged and integrated broader data sets

beyond medical claims data such as self-reported health risk assessment data and early indicators

from pharmacy data, employed advanced data mining techniques for pattern discovery, and

scored every Medicare Advantage (MA, a specific insurance plan) consumer daily based on the

most recent data Humana has to date. The model was deployed with a cross-functional team of

analytics, IT, and operations to ensure seamless operational and business integration.

Since NMPM was implemented in January 2013, it has been rapidly identifying high-risk new

members for enrollment in Humana’s clinical programs. The positive outcomes achieved through

this model have been highlighted in multiple senior leader communications from Humana. In the

first quarter 2013 earnings release presentation to investors, Bruce Broussard, CEO of Humana,

stated the significance of “improvement in new member PMs and clinical assessment processes,”

which resulted in 31,000 new members enrolled in clinical programs, compared to 4,000 in the

same period a year earlier, a 675 percent increase. In addition to the increased volume of clinical

program enrollments, outcome studies showed that the newly enrolled consumers identified by

NMPM were also referred to clinical programs sooner with over 50 percent of the referrals

identified within the first three months after new MA plan enrollments. The consumers identified

also participated at a higher rate and had longer tenure in the programs.

Contributors: Sandy Chiu, MS; Vipin Gopal, PhD.

These examples illustrate how an organization explores and implements analytics applications to

meet its strategic goals. You will see several other examples of healthcare applications

throughout various chapters in the book.

Analytics in the Retail Value Chain

The retail sector is where you would perhaps see the most applications of analytics.

This is the domain where the volumes are large but the margins are usually thin.

Customers’ tastes and preferences change frequently. Physical and online stores face

many challenges to succeed. And market dominance at one time does not guarantee

continued success. So investing in learning about your suppliers, customers, employees,

and all the stakeholders that enable a retail value chain to succeed and using that

information to make better decisions has been a goal of the analytics industry for a long

time. Even casual readers of analytics probably know about Amazon’s enormous

investments in analytics to power their value chain. Similarly, Walmart, Target, and

other major retailers have invested millions of dollars in analytics for their supply

chains. Most of the analytics technology and service providers have a major presence in

retail analytics. Coverage of even a small portion of those applications to achieve our

exposure goal could fill a whole book. So this section highlights just a few potential

applications. Most of these have been fielded by many retailers and are available

through many technology providers, so in this section, we will take a more general

view rather than point to specific cases. This general view has been proposed by

Abhishek Rathi, CEO of vCreaTek.com. vCreaTek, LLC is a boutique analytics software

and service company that has offices in India, the United States, the United Arab

Emirates (UAE), and Belgium. The company develops applications in multiple

domains, but retail analytics is one of its key focus areas.

Figure 1.15 highlights selected components of a retail value chain. It starts with

suppliers and concludes with customers but illustrates many intermediate strategic and

operational planning decision points where analytics—descriptive, predictive, or

prescriptive—can play a role in making better data-driven decisions. Table 1.1 also

illustrates some of the important areas of analytics applications, examples of key

questions that can be answered through analytics, and of course, the potential business

value derived from fielding such analytics. Some examples are discussed next.

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

Table 1.1 Examples of Analytics Applications in the Retail Value Chain

Analytic

Application

Business Question Business Value

Inventory

Optimization

1. Which products have high demand?

2. Which products are slow moving or

becoming obsolete?

1. Forecast the consumption of fast-moving

products and order them with sufficient

inventory to avoid a stock out scenario.

2. Perform fast inventory turnover of slow-

moving products by combining them with

one in high demand.

Price

Elasticity

1. How much net margin do I have on

the product?

2. How much discount can I give on

this product?

1. Markdown prices for each product can be

optimized to reduce the margin dollar

loss.

2. Optimized price for the bundle of

products is identified to save the margin

dollar.

Market-Basket

Analysis

1. What products should I combine to

create a bundle offer?

2. Should I combine products based on

slow-moving and fast-moving

characteristics?

1. The affinity analysis identifies the hidden

correlations between the products, which

can help in following values:

a. Strategize the product bundle offering

based on focus on inventory or margin.

Analytic

Application

Business Question Business Value

3. Should I create a bundle from the

same category or a different

category line?

b. Increase cross-selling or up-selling by

creating bundle from different

categories or the same categories,

respectively.

Shopper

Insight

1. Which customer is buying what

product at what location?

1. By customer segmentation, the business

owner can create personalized offers

resulting in better customer experience

and retention of the customer.

Customer

Churn

Analysis

1. Who are the customers who will not

return?

2. How much business will I lose?

3. How can I retain the customers?

4. What demography of customer is

my loyal customer?

1. Businesses can identify the customer and

product relationships that are not

working and show high churn. Thus, they

can have better focus on product quality

and the reason for that churn.

2. Based on the customer lifetime value

(LTV), the business can do targeted

marketing resulting in retention of the

customer.

Analytic

Application

Business Question Business Value

Channel

Analysis

1. Which channel has lower customer

acquisition cost?

2. Which channel has better customer

retention?

3. Which channel is more profitable?

1. Marketing budget can be optimized based

on insight for better return on

investment.

New Store

Analysis

1. What location should I open?

2. What and how much opening

inventory should I keep?

1. Best practices of other locations and

channels can be used to get a jump-start.

2. Comparison with competitor data can

help to create a differentiator to attract

the new customers.

Store Layout 1. How should I do store layout for

better topline?

2. How can I increase my in-store

customer experience?

1. Understand the association of products to

decide store layout and better alignment

with customer needs.

2. Workforce deployment can be planned

for better customer interactivity and thus

satisfying customer experience.

Analytic

Application

Business Question Business Value

Video

Analytics

1. What demography is entering the

store during the peak period of

sales?

2. How can I identify a customer with

high LTV at the store entrance so

that a better personalized

experience can be provided to this

customer?

1. In-store promotions and events can be

planned based on the demography of

incoming traffic.

2. Targeted customer engagement and

instant discount enhances the customer

experience resulting in higher retention.

An online retail site usually knows its customer as soon as the customer signs in, and

thus they can offer customized pages/offerings to enhance the experience. For any retail

store, knowing its customer at the store entrance is still a huge challenge. By combining

the video analytics and information/badge issued through its loyalty program, the store

may be able to identify the customer at the entrance itself and thus enable an extra

opportunity for a cross-selling or up-selling. Moreover, a personalized shopping

experience can be provided with more customized engagement during the customer’s

time in the store.

Store retailers invest lots of money in attractive window displays, promotional events,

customized graphics, store decorations, printed ads, and banners. To discern the

effectiveness of these marketing methods, the team can use shopper analytics by

observing closed-circuit television (CCTV) images to figure out the demographic details

of the in-store foot traffic. The CCTV images can be analyzed using advanced

algorithms to derive demographic details such as age, gender, and mood of the person

browsing through the store.

Further, the customer’s in-store movement data when combined with shelf layout and

planogram can give more insight to the store manager to identify the hot-

selling/profitable areas within the store. Moreover, the store manager also can use this

information to plan the workforce allocation for those areas for peak periods.

Market-basket analysis has commonly been used by the category managers to push the

sale of slowly moving stock keeping units (SKUs). By using advanced analytics of data

available, the product affinity can be identified at the lowest level of SKU to drive better

returns on investments (ROIs) on the bundle offers. Moreover, by using price elasticity

techniques, the markdown or optimum price of the bundle offer can also be deduced,

thus reducing any loss in the profit margin.

Thus, by using data analytics, a retailer can not only get information on its current

operations but can also get further insight to increase the revenue and decrease the

operational cost for higher profit. A fairly comprehensive list of current and potential

retail analytics applications that a major retailer such as Amazon could use is proposed

by a blogger at Data Science Central. That list is available

at www.datasciencecentral.com/profiles/blogs/20-data-science-systems-used-by-

amazon-to-operate-its-business. As noted earlier, there are too many examples of these

opportunities to list here, but you will see many examples of such applications

throughout the book.

Image Analytics

As seen in this section, analytics techniques are being applied to many diverse industries and

data. An area of particular growth has been analysis of visual images. Advances in image

capturing through high-resolution cameras, storage capabilities, and deep learning algorithms

have enabled very interesting analyses. Satellite data have often proven their utility in many

different fields. The benefits of satellite data at high resolution and in different forms of imagery

including multi-spectral are significant to scientists who need to regularly monitor global change,

land usage, and weather. In fact, by combining the satellite imagery and other data including

information on social media, government filings, and so on, one can surmise business planning

activities, traffic patterns, changes in parking lots or open spaces. Companies, government

agencies, and non-governmental organizations (NGOs) have invested in satellites to try to image

the whole globe every day so that daily changes can be tracked at any location and the

information can be used for forecasting. In the last few months, many interesting examples of

more reliable and advanced forecasts have been reported. Indeed, this activity is being led by

different industries across the globe, and has added a term to Big Data called Alternative Data. Here are a few examples from Tartar et al. (2018). We will see more in Chapter 9 when we study

Big Data.

• World Bank researchers used satellite data to propose strategic recommendations for urban

planners and officials from developing nations. This analysis arose due to the recent natural

disaster where at least 400 people died in Freetown, Sierra Leone. Researchers clearly

demonstrated that Freetown and some other developing cities lacked systematic planning of

their infrastructure that resulted in the loss of life. The bank researchers are using satellite

imagery now to make critical decisions regarding risk-prone urban areas.

• EarthCast provides accurate weather updates for a large commercial U.S. airline based on

the data it pulls from a constellation of 60 government-operated satellites combined with

ground and aircraft-based sensors, tracking almost anything from lightning to turbulence. It

has even developed the capability to map out conditions along a flight path and provides

customized forecasts for everything from hot air balloons to drones.

• Imazon started using satellite data to develop a picture of close real-time information on

Amazon deforestation. It uses advanced optical and infrared imagery that has led to

identifying illegal sawmills. Imazon is now focused more on getting data to local

governments through its “green municipalities” program that trains officials to identify and

curb deforestation.

• The Indonesian government teamed up with international nonprofit Global Fishing Watch,

which processes satellite extracted information on ship movement to spot where and when

vessels are fishing illegally (Emmert, 2018). This initiative delivered instant results:

Government revenue from fishing went up by 129 percent in 2017 compared to 2014. It is

expected that by next decade, the organization would track vessels that are responsible for

75 percent of the world’s catch.

These examples illustrate just a sample of ways that satellite data can be combined with analytics

to generate new insights. In anticipation of the coming era of abundant earth observations from

satellites, scientists and communities must put some thought into recognizing key applications

and key scientific issues for the betterment of society. Although such concerns will eventually be

resolved by policymakers, what is clear is that new and interesting ways of combining satellite

data and many other data sources is spawning a new crop of analytics companies.

Such image analysis is not limited to satellite images. Cameras mounted on drones and traffic

lights on every conveyable pole in buildings and streets provide the ability to capture images

from just a few feet high. Analysis of these images coupled with facial recognition technologies

is enabling all kinds of new applications from customer recognition to governments’ ability to

track all subjects of interest. See Yue (2017) as an example. Applications of this type are leading

to much discussion on privacy issues. In Application Case 1.7, we learn about a more benevolent

application of image analytics where the images are captured by a phone and a mobile

application provides immediate value to the user of the app.

Application Case 1.7 Image Analysis Helps Estimate Plant Cover

Estimating how much ground is covered by green vegetation is important in analysis of a forest

or even a farm. In case of a forest, such analysis helps users understand how the forest is

evolving, its impact on surrounding areas, and even climate. For a farm, similar analysis can help

understand likely plant growth and help estimate future crop yields. It is obviously impossible to

measure all forest cover manually and is challenging for a farm. The common method is to

record images of a forest/farm and then analyze these images to estimate the ground cover. Such

analysis is expensive to perform visually and is also error prone. Different experts looking at the

ground cover might estimate the percentage of ground covering differently. Thus, automated

methods to analyze these images and estimate the percentage of ground covered by vegetation

are being developed. One such method and an app to make it practical through a mobile phone

has been developed at Oklahoma State University by researchers in the Department of Plant and

Soil Sciences in partnership with the university’s App Center and the Information Technology

group within the Division of Agricultural Sciences and Natural Resources.

Canopeo is a free desktop or mobile app that estimates green canopy cover in near real-time from

images taken with a smartphone or digital camera. In experiments in corn, wheat, canola, and

other crops, Canopeo calculated the percentage of canopy covering dozens to thousands of times

faster than existing software without sacrificing accuracy. And unlike other programs, the app

can acquire and analyze video images, says Oklahoma State University (OSU) soil physicist,

Tyson Ochsner—a feature that should reduce the sampling error associated with canopy cover

estimates. “We know that plant cover, plant canopies, can be quite variable in space,” says

Ochsner, who led the app’s development with former doctoral student Andres Patrignani, now a

faculty member at Kansas State University. “With Canopeo, you can just turn on your [video]

device, start walking across a portion of a field, and get results for every frame of video that

you’re recording.” By using a smartphone or tablet’s digital camera, Canopeo users in the field

can take photos or videos of green plants, including crops, forages, and turf, and import them to

the app, which analyzes each image pixel, classifying them based on its red-green-blue

(RGB) color values. Canopeo analyzes pixels based on a ratio of red to green and blue to green

pixels as well as an excess green index. The result is an image where color pixels are converted

into black and white with white pixels corresponding to green canopy and black pixels

representing the background. Comparison tests showed that Canopeo analyzes images more

quickly and just as accurately as two other available software packages.

Developers of Canopeo expect the app to help producers judge when to remove grazing cattle

from winter wheat in “dual-purpose” systems where wheat is also harvested for grain. Research

by others at OSU found that taking cattle off fields when at least 60 percent green canopy cover

remained ensured a good grain yield. “So, Canopeo would be useful for that decision,”

Patrignani says. He and Ochsner also think the app could find use in turfgrass management; in

assessments of crop damage from weather or herbicide drift; as a surrogate for the Normalized

Difference Vegetation Index (NDVI) in fertilizer recommendations; and even in UAV-based

photos of forests or aquatic systems.

Analysis of images is a growing application area for deep learning as well as many other AI

techniques. Chapter 9 includes several examples of image analysis that have spawned another

term—alternative data. Applications of alternative data are emerging in many

fields. Chapter 6 also highlights some applications. Imagining innovative applications by being

exposed to others’ ideas is one of the main goals of this book!

Questions for Discussion 1. What is the purpose of knowing how much ground is covered by green foliage on

a farm? In a forest? 2. Why would image analysis of foliage through an app be better than a visual

check?

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

4. What other applications of image analysis can you think of?

Source: Compiled from A. Patrignani and T. E. Ochsner. (2015). “Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover.” Agronomy Journal, 107(6), pp. 2312–2320; R. Lollato, A. Patrignani, T. E. Ochsner, A. Rocatelli, P. Tomlinson, & J. T. Edwards. (2015). Improving Grazing Management Using a Smartphone App. www.bookstore.ksre.ksu.edu/pubs/MF3304.pdf (accessed October 2018); http://canopeoapp.com/ (accessed October 2018); Oklahoma State University press releases.

Analytics/data science initiatives are quickly embracing and even merging with new

developments in artificial intelligence. The next section provides an overview of artificial

intelligence followed by a brief discussion of convergence of the two.

Section 1.6 Review Questions

1. What are three factors that might be part of a PM for season ticket renewals? 2. What are two techniques that football teams can use to do opponent analysis? 3. What other analytics uses can you envision in sports? 4. Why would a health insurance company invest in analytics beyond fraud

detection? Why is it in its best interest to predict the likelihood of falls by patients?

5. What other applications similar to prediction of falls can you envision? 6. How would you convince a new health insurance customer to adopt healthier

lifestyles (Humana Example 3)? 7. Identify at least three other opportunities for applying analytics in the retail value

chain beyond those covered in this section. 8. Which retail stores that you know of employ some of the analytics applications

identified in this section? 9. What is a common thread in the examples discussed in image analytics? 10. Can you think of other applications using satellite data along the lines presented

in this section?

1.7 Artificial Intelligence Overview

On September 1, 2017, the first day of the school year in Russia, Vladimir Putin, the

Russian President, lectured to over 1,000,000 school children in what is called in Russia

the National Open Lesson Day. The televised speech was titled “Russia Focused on the

Future.” In this presentation, the viewers saw what Russian scientists are achieving in

several fields. But, what everyone remembers from this presentation is one sentence:

“The country that takes the lead in the sphere of computer-based artificial intelligence

will become the ruler of the world.”

Putin is not the only one who knows the value of AI. Governments and corporations are

spending billions of dollars in a race to become a leader in AI. For example, in July 2017,

China unveiled a plan to create an AI industry worth $150$150 billion to the Chinese

economy by 2030 (Metz, 2018). China’s Badu Company today employs over 5,000 AI

engineers. The Chinese government facilitates research and applications as a national

top priority. The accounting firm PricewaterhouseCoopers calculated that AI will

add $15.7$15.7 trillion to the global economy by 2030 (about 14 percent; see Liberto, 2017). Thus, there is no wonder that AI is clearly the most talked about technology topic

in 2018.

What is Artificial Intelligence?

There are several definitions of what is AI (Chapter 2). The reason is that AI is based on theories

from several scientific fields, and it encompasses a wide collection of technologies and

applications. So, it may be beneficial to look at some of the characteristics of AI in order to

understand what it is. The major goal of AI is to create intelligent machines that can do tasks

currently done by people. Ideally, these tasks include reasoning, thinking, learning, and problem

solving. AI studies the human thought processes’ ability to understand what intelligence is so AI

scientists can duplicate the human processes in machines. eMarketer (2017) provides a

comprehensive report, describing AI as

• 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 AI

Since AI appears in many shapes, it has many benefits. They are listed in Chapter 2. The major

benefits are as follows:

• Significant reduction in the cost of performing work. This reduction continues over time

while the cost of doing the same work manually increases with time.

• Work can be performed much faster.

• Work is consistent in general, more consistent than human work.

• Increased productivity and profitability as well as a competitive advantage are the major

drivers of AI.

The Landscape of AI

There are many parts in the landscape (or ecosystem) of AI. We decided to organize them into

five groups as illustrated in Figure 1.16. Four of the groups constitute the basis for the fifth one,

which is the AI applications. The groups are as follows:

Major Technologies

Here we elected to include machine learning (Chapter 5), deep learning (Chapter 6), and

intelligent agents (Chapter 2).

Knowledge-Based Technologies

(all covered in Chapter 12) Topics covered are expert systems, recommendation engines,

chatbots, virtual personal assistants, and robo-advisors.

Biometric-Related Technologies

This includes natural language processing (understanding and generation, machine vision and

scene and image recognition and voice and other biometric recognition (Chapter 6).

Support Theories, Tools, and Platforms

Academic disciplines include computer science, cognitive science, control theory, linguistics,

mathematics, neuroscience, philosophy, psychology, and statistics.

Devices and methods include sensors, augmented reality, context awareness, logic, gestural

computing collaborative filtering, content recognition, neural networks, data mining, humanoid

theories, case-based reasoning, predictive application programming interfaces (APIs), knowledge

management, fuzzy logic, genetic algorithm, bin data, and much more.

Tools and Platforms

These are available from IBM, Microsoft, Nvidia, and several hundred vendors specializing in

the various aspects of AI.

AI Applications

There are several hundred or may be thousands of them. We provide here only a sample:

Smart cities, smart homes, autonomous vehicles (Chapter 13), automatic decisions (Chapter 2),

language translation, robotics (Chapter 10), fraud detection, security protection, content

screening, prediction, personalized services, and more. Applications are in all business areas

(Chapter 2), and in almost any other area ranging from medicine and healthcare to transportation

and education.

NOTE: Lists of all these are available at Faggela (2018) and Jacquet (2017). Also see

Wikipedia, “Outline of artificial intelligence,” and a list of “AI projects” (several hundred items.)

Figure 1.16 The Landscape (Ecosystem) of AI.

In Application Case 1.8, we describe how several of these technologies are combined in

improving security and in expediting the processing of passengers in airports.

Application Case 1.8 AI Increases Passengers’ Comfort and Security in Airports and Borders

We may not like the security lines at airports or the idea that terrorists may board our plane or

enter our country. Several AI devices are designed to minimize these possibilities.

1. FACIAL RECOGNITION AT AIRPORTS. Jet Blue is experimenting with facial- recognition technology (a kind of machine vision to match travelers’ faces against prestored photos, such as passport, driver’s license). This will eliminate the need for boarding passes and increase security. The match is of high quality. The technology pioneered by British Airways is used by Delta, KLM, and other airlines using similar technologies for self-checking of bags. Similar technology is used by the U.S. Immigration and Customs Enforcement agency where people’s photos taken at arrivals are matched against the database of photos and other documents.

2. CHINA’S SYSTEM. The major airports in China are using a system similar to that of Jet Blue, using facial recognition for verifying the identity of passengers. The idea is to eliminate boarding passes and expedite the flow of boarding. The system is also used to recognize airport employees entering restricted areas.

3. USING BOTS. Several airports (e.g., New York, Beijing) offer conversational bots (Chapter 12) to provide travelers with airport guidance. Bots provide also information about customs and immigration services.

4. SPOTTING LIARS AT AIRPORT. This application is emerging to help immigration services to vet passengers at airports and land entry borders. With increased security, both immigration and airline personal may need to query passengers. Here is the solution that can be economically used to query all passengers at high

speed, so there will be short waiting lines. This emerging system is called Automated Virtual Agent for Truth Assessments in Real Time (AVATAR). The essentials of the system are as follows:

a. AVATAR is a bot in which you first scan your passport. b. AVATAR asks you a few questions. Several AI technologies are used in

this project, such as AI, Big Data analytics, the “Cloud,” robotics, machine learning, machine vision, and bots.

c. You answer the questions. d. AVATAR’s sensors and other AI technologies collect data from your

body, such as voice variability, facial expression (e.g., muscle engagement), eyes’ position and movements, mouth movements, and body posture. Researchers feel that it takes less effort to tell the truth than to die, so researchers compared the answers to routine questions.

The machine then will flag suspects for further investigation. The machine is already in use by

immigration agents in several countries.

Sources: Condensed from Thibodeaux, W. (2017, June 29). “This Artificial Intelligence Kiosk Is Designed to Spot Liars at Airports.” Inc.com.; Silk, R. (2017, November). “Biometrics: Facial Recognition Tech Coming to an Airport Near You.” Travel Weekly, 21.

Questions for Case 1.8 1. List the benefits of AI devices to travelers. 2. List the benefits to governments and airline companies. 3. Relate this case to machine vision and other AI tools that deal with people’s

biometrics.

Narrow (Weak) versus General (Strong) AI

The AI field can be divided into two major categories of applications: narrow (or weak) and

general (or strong).

A Narrow AI Field Focuses on One Narrow Field (Domain)

Well-known examples of this are SIRI and Alexa (Chapter 12) that, at least in their early years of

life, operated in limited, predefined areas. As time has passed, they have become more general,

acquiring additional knowledge. Most expert systems (Chapter 12) were operating in fairly

narrow domains. If you notice, when you converse with an automated call center, the

computer (which is usually based on some AI technology) is not too intelligent. But, it is getting

“smarter” with time. Speech recognition allows computers to convert sound to text with great

accuracy. Similarly, computer vision is improving, recognizes objects, classifies them, and even

understands their movements. In sum, there are millions of narrow AI applications, and the

technology is improving every day. However, AI is not strong enough yet because it does not

exhibit the true capabilities of human intelligence (Chapter 2).

General (Strong) AI

To exhibit real intelligence, machines need to perform the full range of human cognitive

capabilities. Computers can have some cognitive capabilities (e.g., some reasoning and problem

solving) as will be shown in Chapter 6 on cognitive computing.

The difference between the two classes of AI is getting smaller as AI is getting smarter. Ideally,

strong AI will be able to replicate humans. But true intelligence is happening only in narrow

domains, such as game playing, medical diagnosis, and equipment failure diagnosis.

Some feel that we never will be able to build a truly strong AI machine. Others think differently;

see the debate in Section 14.9. The following is an example of a strong AI bot in a narrow

domain.

Example 3: AI Makes Coke Vending Machine Smarter If you live in Australia or New Zealand and you are near a Coca-Cola vending machine, you can

order a can or a bottle of the soft drink using your smartphone. The machines are cloud

connected, which means you can order the Coke from any place in the world, not only for

yourself but also for any friend who is near a vending machine in Australia or New Zealand.

See Olshansky (2017).

In addition, the company can adjust pricing remotely, offer promotions, and collect inventory

data so that restocking can be made. Converting existing machines to AI-enabled takes about 1

hour each.

Wait a minute, what if something goes wrong? No problem, you can chat with Coca-Cola’s bot

via Facebook Messenger (Chapter 12).

The Three Flavors of AI Decisions

Staff (2017) divided the capabilities of AI systems into three levels: assisted, autonomous, and

augmented.

Assisted Intelligence

This is equivalent mostly to the weak AI, which works only in narrow domains. It requires

clearly defined inputs and outputs. Examples are some monitoring systems and low-level virtual

personal assistants (Chapter 12). Our cars are full of such monitoring systems that give us alerts.

Similarly, there are many healthcare applications (monitoring, diagnosis).

Autonomous AI

These systems are in the realm of the strong AI but in very narrow domain. Eventually, the

computer will take over. Machines will act as experts and have absolute decision-making power.

Pure robo-advisors (Chapter 12) are examples of such machines. Autonomous vehicles and

robots that can fix themselves are also good examples.

Augmented Intelligence

Most of the existing AI applications are between assisted and autonomous and/are referred to

as augmented intelligence (or intelligence augmentation). The technology focuses on augmenting computer abilities to extend human cognitive abilities (see Chapter 6 on cognitive

computing), resulting in high performance as described in Technology Insights 1.1.

Technology Insights 1.1 Augmented Intelligence The idea of combining the performance of people and machines is not new. Here we combine

(augmenting) human capabilities with powerful machine intelligence. That is, not replacing

people which is done by autonomous AI, but extending human cognitive abilities. The result is

the ability to solve complex human problems as in the opening vignette to this chapter. The

computers enabled people to solve problems that were unsolved before. Padmanabhan

(2018) distinguishes the following differences between traditional and augmented AI:

1. Augmented machines extend rather than replace human decision making, and they facilitate creativity.

2. Augmentation excels in solving complex human and industry problems in specific domains in contrast with strong, general AI.

3. In contrast with a “black box” model of some AI and analytics, augmented intelligence provides insights and recommendations, including explanations.

4. In addition, augmentation technology can offer new solutions by combining existing and discovered information in contrast with assisted AI, which identifies problems or symptoms and suggests predetermined solutions.

Padmanabhan (2018) and many others believe that at the moment, augmented AI is the best

option to move toward the transformation of the AI world.

In contrast with autonomous AI, which describes machines with a wide range of cognitive

abilities (e.g., driverless vehicles), augmented intelligence has only a few cognitive abilities.

Examples of Augmented Intelligence Staff (2017) provides the following examples:

• CYBERCRIME FIGHTING. AI can identify forthcoming attacks and suggest

solutions.

• E-COMMERCE DECISIONS. Marketing tools make testing 100 times faster and adapt

the layout and response functions of a Web site to users. The machines make

recommendations and the marketers can accept or reject them.

• HIGH-FREQUENCY STOCK MARKET TRADING. This is done either completely

autonomously or in some cases with control and calibration by humans.

Discussion Questions 1. What is the basic premise of augmented intelligence? 2. List the major differences between augmented intelligence and traditional AI. 3. What are some benefits of augmented intelligence? 4. How does technology relate to cognitive computing?

Societal Impacts

Much talk is on the topics of AI and productivity, speed, and cost reduction. In a national

conference hosted by Gartner, the famous IT consulting company, nearly half of 3,000

participating U.S. CIOs reported plans to deploy AI-now (Weldon, 2018). Industry cannot ignore

the potential benefits of AI, especially its increased productivity gains, cost reduction and

quality, and speed. Conference participants there talked about strategy and implementation

(Chapter 14). It seems that every company is at least involved in piloting and experimentation

AI. However, in all this excitement, we should not neglect the societal impacts. Many of these

are positive, some are negative, and most are unknown. A comprehensive discussion is provided

in Chapter 14. Here we provide three examples of AI impacts.

Impact on Agriculture

A major impact of AI will be on agriculture. One major anticipated result is to provide more

food, especially in third world countries. Here are a few examples:

• According to Lopez (2017), using AI and robots can help farmers produce 70 percent more

food by 2050. This increase is a result of higher productivity of farm equipment boosted by

IoT (see opening vignette to Chapter 13) and a reduced cost of producing food. (Today only

10 percent of a family’s budget is spent on food versus. 17.5 percent in 1960).

• Machine vision helps in improved planting and harvesting. Also, AI helps to pick good

kernels of grain.

• AI will help to improve the nutrition of food.

• AI will reduce the cost of food processing.

• Driverless tractors are already being experimented with.

• Robots know how to pick fruits and to plant vegetables can solve the shortage of farm

workers.

• Crop yields are continuously increasing in India and other countries.

• Pest control improves. For example, AI can predict pest attacks, facilitating planning.

• Weather conditions are monitored by satellites. AI algorithms tell farmers when to plant

and/or harvest.

The list can go on and on. For countries such as India and Bangladesh, these activities will

critically improve the life of farmers. All in all, AI will help farmers make better decisions. For a

Bangladesh case, see PE Report (2017). See alsonews.microsoft.com/en-in/features/ai-

agriculture-icrisat-upl-india/.

NOTE: AI can help hungry pets too. A food and water dispenser, called Catspad, is available in

the United Kingdom for about US $470$470 You need to put an ID tag on your pet (only cats and small dogs). The dispenser knows which pet comes to the food and dispenses the type and

amount of appropriate food. In addition, sensors (Chapter 13) can tell you how much food each

pet ate. You will also be notified if water needs to be added. Interested? See Deahl (2018) for

details.

Intelligent Systems Contribution to Health and Medical Care

Intelligent systems provide a major contribution to our health and medical care. New innovations

arrive almost any day in some place in the world (governments, research institutions, and

corporation-sponsored active medical AI research). Here are some interesting innovations.

• AI excels in disease prediction (e.g., predicting the occurrence of infective diseases one

week in advance).

• AI can detect brain bleeds.

• AI can track medication intake, send medical alerts, order medicine refills, and improve

prescription compliance.

• Mobile telepresence robots remotely connect physicians and patients.

• NVIDIA’s medical imaging supercomputer helps diagnosticians and facilitates cures of

diseases.

• Robotics and AI can redesign pharmaceutical supply chains.

• AI predicts cardiovascular risks from retinal images.

• Cancer predictions are made with deep learning, and machine learning performs melanoma

diagnosis.

• A virtual personal assistant can assess a patient’s mood and feeling by cues provided (e.g.,

speech gesture or inflection).

• Many portals provide medical information to patients and even surgeons. Adoptive spine IT

is an example.

• Aging-based AI center for research on people who are elderly operates in the United States.

Similar activities exist in Japan.

• The use of bionic hands and legs is improving with AI.

• Healthcare IT News (2017) describes how AI is solving healthcare problems by using

virtual assistants (Chapter 12).

The list can go on and on. Norman (2018) describes the scenario of replacing doctors with

intelligent systems.

NOTE: AI in medicine is recognized as a scientific field with national and international annual

conferences. For a comprehensive book on the subject, see Agah (2017).

Other Societal Applications

There are many AI applications in transportation, utilities, education, social services, and other

fields. Some are covered under the topic of smart cities (Chapter 13). AI is used by social media

and others to control content including fake news. Finally, how about using technology to

eradicate child slavery in the Middle East? See Application Case 1.9.

Application Case 1.9 Robots Took the Job of Camel-Racing Jockeys for Societal Benefits

In several Middle Eastern countries, notably Jordan, Abu Dhabi, and other Gulf nations, racing

camels has been a popular activity for generations. The owners of the winning camels can make

a huge bonus (up to $1,000,000$1,000,000 for first place). Also, the events are considered cultural and social.

The Problem For a long time, the racing camels were guided by human jockeys. The lighter the weight of the

rider, the better is the chance to win. So the owners of the camels trained children (as young as

seven) to be jockeys. Young male children were bought (or kidnapped) from poor families in

Sudan, India, Bangladesh, and other poor countries and were trained as child jockeys. In fact,

this practice was using child slave labor to race the camels. This practice was used for

generations until it was banned in all Middle Eastern countries during 2005–2010. A major

factor that resulted in the banning was the utilization of robots.

The Robots’ Solution Racing camels was a tradition for many generations and become a lucrative sport. So, no one

wanted to discontinue it. According to Opfer (2016), there was a humanistic reason for using

robots to race camels—to save the children. Today, all camel race tracks in the Middle East

employ only robots. The robots are tied to the hump of the camels, looking like small jockeys

and are remote controlled from cars that drive parallel to the racing camels. The owners can

command the camels by voice, and they can also operate a mechanical whip to beat the animals

so they will run faster, much like human jockeys do. Note that camels would not run unless they

hear the voice of a human or see something that looks like a human on their humps.

The Technology There is a video camera that shows the people that are in cars driving alongside of the camels,

what is going on in real time. The owner can provide voice commands to the camel from the car.

A mechanical whip attached to the hump of the camel can be remotely operated to induce the

animal.

The Results The results are astonishing. Not only was the child slavery practice eliminated, but also the speed

obtained by the camels increased. After all, the robots used weigh only 6 pounds and do not get

tired. To see how this works watch the video at youtube.com/watch?v=GVeVhWXB7sk (2:47

min.). To view a complete race,

see youtube.com/watch?v=xFCRhk4GYds (9:08 min.)(9:08 min.). You may have a chance to see the royal family when you go to the track. Finally, you can see more details

in youtube.com/watch?v=C1uYAXJIbYg (8:08 min.)(8:08 min.). Sources: Compiled from C. Chung. (2016, April 4). “Dubai Camel Race Ride- Along.” YouTube.com. youtube.com/watch?v=xFCRhk4GYds (accessed September 2018); P. Boddington. (2017, January 3). “Case Study: Robot Camel Jockeys. Yes, really.” Ethics for Artificial Intelligence; and L. Slade. (2017, December 21). “Meet the Jordanian Camel Races Using Robot Jockeys.” Sbs.com.au.

Discussion Questions 1. It is said that the robots eradicated the child slavery. Explain. 2. Why do the owners need to drive by their camels while they are racing? 3. Why not duplicate the technology for horse racing? 4. Summarize ethical aspects of this case (Read Boddington, 2017). Do this exercise

after you have read about ethics in Chapter 14.

Section 1.7 Review Questions

1. What are the major characteristics of AI? 2. List the major benefits of AI. 3. What are the major groups in the ecosystem of AI? List the major contents of each.

4. Why is machine learning so important? 5. Differentiate between narrow and general AI. 6. Some say that no AI application is strong. Why? 7. Define assisted intelligence, augmented intelligence, and autonomous intelligence. 8. What is the difference between traditional AI and augmented intelligence? 9. Relate types of AI to cognitive computing. 10. List five major AI applications for increasing the food supply. 11. List five contributions of AI in medical care.

1.8 Convergence of Analytics and AI

Until now we have presented analytics and AI as two independent entities. But, as

illustrated in the opening vignette, these technologies can be combined in solving

complex problems. In this section, we discuss the convergence of these techniques and

how they complement each other. We also describe the possible addition of other

technologies, especially IoT, that enable the solutions to very complex problems.

Major Differences between Analytics and AI

As you recall from Section 1.4, analytics process historical data using statistical, management science and other computational tools to describe situations (descriptive analytics), to predict results including forecasting (predictive analytics), and to propose recommendations for

solutions to problems (predictive analytics). The emphasis is on the statistical, management

science, and other computational tools that help analyze historical data.

AI, on the other hand, also uses different tools, but its major objective is to mimic the manner in

which people think, learn, reason, make decisions, and solve problems. The emphasis here is

on knowledge and intelligence as major tools for solving problems rather than relying on computation, which we do in analysis. Furthermore, AI also is dealing with cognitive computing.

In reality, this difference is not so clear because in advanced analytic applications, there are

situations of using machine learning (an AI technology), supporting analytics in both prediction

and prescription. In this section, we describe the convergence of intelligent technologies.

Why Combine Intelligent Systems?

Both analytics and AI and their different technologies are making useful contributions to many

organizations when each is applied by itself. But each does have limitations According to a

Gartner study, the chance that business analytics initiatives will not meet the enterprise

objectives is 70–80 percent. Namely, at least 70 percent of corporate needs are not fulfilled. In

other words, there is only a small chance that business intelligence initiatives will result in

organizational excellence. There are several reasons for this situation including:

• Predictive models have unintended effects (see Chapter 14).

• Models must be used ethically, responsibly, and mindfully (Chapter 14). They may not be

used this way.

• The results of analytics may be very good for some applications but not for others.

• Models are as good as their input data and assumptions (garbage-in, garbage-out).

• Data could be incomplete. Changing environments can make data obsolete very quickly.

Models may be unable to adapt.

• Data that come from people may not be accurate.

• Data collected from different sources can vary in format and quality.

Additional reasons for combining intelligent systems are generic to IT projects, and they are

discussed in Section 14.2.

The failure rate of AI initiatives is also high. Some of the reasons are similar to the rate of

analytics. However, a major reason is that some AI technologies need a large amount of data,

sometimes Big Data. For example, many millions of data items are fed to Alexa every day to

increase its knowledge. Without continuous flow of data, there would not be good learning in AI.

The question is whether AI and analytics (and other intelligent systems) can be combined in such

a way that there will be synergy for better results.

How Convergence Can Help?

According to Nadav (2017), business intelligence and its analytics answer most of

the why and what questions regarding the sufficiency of problem solving. Adding prescriptive analytics will add more cost but not necessarily better performance. Therefore, the next

generation of business intelligence platforms will use AI to automatically locate, visualize, and

narrate important things. This can also be used to create automatic alerts and notifications. In

addition, machine learning and deep learning can support analytics by conducting pattern

recognition and more accurate predictions. AI will help to compare actual performance with the

predicted one (see Section 14.6). Machine learning and other AI technologies also provide for

constant improvement strategy. Nadav also suggested adding expert opinions via collective

intelligence, as presented in Chapter 11.

In the remaining part of this section, we present detailed aspects of convergence of some

intelligent systems.

Big Data is Empowering AI Technologies

Big Data is characterized by its volume, variety, and velocity that exceed the reach of commonly

used hardware environments and/or the capabilities of software tools to process data. However,

today there are technologies and methods that enable capturing, cleaning, and analyzing Big

Data. These technologies and methods enable companies to make real-time decisions. The

convergence with AI and machine learning is a major force in this direction. The availability of

new Big Data analytics enables new capabilities in AI technologies that were not possible until

recently. According to Bean (2017), Big Data can empower AI due to:

• The new capabilities of processing Big Data at a much reduced cost.

• The availability of large data sets online.

• The scale up of algorithms, including deep learning, is enabling powerful AI capabilities.

MetLife Example: Convergence of AI and Big Data MetLife is a Canadian-based global insurance company that is known for its use of IT to smooth

its operation and increase customer satisfaction. To get the most from technology, the company

uses AI that has been enabled by Big Data analysis as follows:

• Tracking incidents and their outcomes has been improved by speech recognition.

• Machine learning indicates pending failures. In addition, handwritten reports made by

doctors about people injured or were sick and claims paid by the insurance company are

analyzed in seconds by the system.

• Expediting the execution of underwriting policies in property and casualty insurance is done

by using both AI and analytics.

• The back-office side of claim processing includes many unstructured data that are

incorporated in claims. Part of the analysis includes patients’ health data. Machine learning

is used to recognize anomalies in reports very quickly.

For more about AI and the insurance business, see Chapter 2. For more on the convergence of

Big Data and AI in general and at MetLife, see Bean (2017).

The Convergence of AI and the IoT

The opening vignette illustrated to us how AI technologies when combined with IoT can provide

solutions to complex problems. IoT collects a large amount of data from sensors and other

“things.” These data need to be processed for decision support. Later we will see how

Microsoft’s Cortana does this. Butner (2018) describes how combining AI and IoT can lead to

the “next-level solutions and experiences.” The emphasis in such combination is on learning

more about customers and their needs. This integration also can facilitate competitive analysis

and business operation (see the opening vignette). The combined pair of AI and IoT, especially

when combined with Big Data, can help facilitate the discovery of new products, business

processes, and opportunities. The full potential of IoT can be leveraged with AI technologies. In

addition, the only way to make sense of the data streamed from the “things” via IoT and to

obtain the insight from them is to subject them to AI analysis. Faggela (2017) provides the

following three examples of combining AI and IoT:

1. The smart thermostat of Nest Labs (see smart homes in Chapter 13). 2. Automated vacuum cleaners, like iRobot Roomba (see Chapter 2, intelligent

vacuums). 3. Self-driving vehicles (see Chapter 13).

The IoT can become very intelligent when combined with IBM Watson Analytics that includes

machine learning. Examples are presented in the opening vignette and the opening vignette

to Chapter 13.

The Convergence with Blockchain and Other Technologies

Several experts raise the possibility of the convergence of AI, analytics, and blockchain

(e.g., Corea, 2017; Kranz, 2017). The idea is that such convergence may contribute to design or

redesign of paradigms and technologies. The blockchain technology can add security to data

shared by all parties in a distributed network, where transaction data can be recorded. Kranz

believes that the conversion with blockchain will power new solutions to complex problems.

Such a convergence should include the IoT. Kranz also see a role for fog computing (Chapter 9).

Such a combination can be very useful in complex applications such as autonomous vehicles and

in Amazon’s Go (Application Case 1.10).

Application Case 1.10 Amazon Go Is Open for Business

In early 2018, Amazon.com opened its first fully automated convenience store in downtown

Seattle. The company had had success with this type of store during 2017, experimenting with

only the company’s employees.

Shoppers enter the store, pick up products, and go home. Their accounts are charged later on.

Sounds great! No more waiting in line for the packing of your goods and paying for them – no

cashiers, no hassle.

In some sense, shoppers are going through a process similar to what they do online—find desired

products/services, buy them, and wait for the monthly electronic charge.

The Shopping Process

To participate, you need a special free app on your smartphone. You need to connect it to your

regular Amazon.com account. Here is what you do next:

1. Open your app. 2. Wave your smartphone at a gate to the store. It will work with a QR code there. 3. Enter the store. 4. Start shopping. All products are prepacked. You put them in a shopping bag

(yours or one borrowed at the store). The minute you pick an item from the shelf, it is recorded in a virtual shopping cart. This activity is done by sensors/cameras. Your account is debited. If you change your mind, and return an item, the system will credit your account instantly. The sensors also track your movements in the store. (This is an issue of digital privacy; see Chapter 14, Section 14.3). The sensors are of RFID type (Chapter 13).

5. Finished shopping? Just leave the store (make sure your app is open for the gate to let you leave). The system knows that you have left and what products you took, and your shopping trip is finished. The system will total your cost, which you can check anytime on your smartphone.

6. Amazon.com records your shopping habits (again, a privacy issue), which will help your future shopping experience and will help Amazon to build recommendations for you (Chapter 2). The objective of Go is to guide you to healthy food! (Amazon sells its meal kits of healthy food there.)

NOTE: Today, only few people work in the store! Employees stock shelves and assist you

otherwise. The company plans to open several additional stores in 2018.

The Technology Used

Amazon disclosed some of the technologies used. These are deep learning algorithms, computer

vision, and sensor fusion. Other technologies were not disclosed. See

the videoyoutube.com/watch?v=NrmMk1Myrxc (1:50 min.)(1:50 min.). Sources: Condensed for C. Jarrett. (2018). “Amazon Set to Open Doors on AI-Powered Grocery Store.” Venturebeat.com. venturebeat.com/2018/01/21/amazon-set-to-open-doors-on-ai-powered-grocery-store/ (accessed September 2018); D. Reisinger. (2018, February 22). “Here Are the Next Cities to Get Amazon Go Cashier-Less Stores.” Fortune.

Questions for Case 1.9

1. Watch the video. What did you like in it, and what did you dislike? 2. Compare the process described here to a self-check available today in many

supermarkets and “big box” stores (Home Depot, etc.). 3. The store was opened in downtown Seattle. Why was the downtown location

selected? 4. What are the benefits to customers? To Amazon? 5. Will customers be ready to trade privacy for convenience? Discuss.

For a comprehensive report regarding convergence of intelligent technologies,

see reportbuyer.com/product/5023639/.

In addition to blockchain, one can include IoT and Big Data, as suggested earlier, as well as

more intelligent technologies (e.g., machine vision, voice technologies). These may have

enrichment effects. In general, the more technologies are used (presumably properly), the more

complex problems may be solved, and the more efficient the performance of the convergence

systems (e.g., speed, accuracy) will be. For a discussion, see i-scoop.eu/convergence-ai-iot-big-

data-analytics/.

IBM and Microsoft Support for Intelligent Systems Convergence

Many companies provide tools or platforms for supporting intelligent systems convergence. Two

examples follow.

IBM

IBM is combining two of its platforms to support the convergence of AI and analytics. Power AI

is a distribution platform for AI and machine learning. This is a way to support the IBM analytics

platform called Data Science Experience (cloud enabled). The combination of the two enables

improvements in data analytics process. It also enables data scientists to facilitate the training of

complex AI models and neural networks. Researchers can use the combined system for deep

learning projects. All in all, this combination provides better insight to problem solving. For

details, see FinTech Futures (2017).

As you may recall from the opening vignette, IBM Watson is also combining analytics, AI, and

IoT in cognitive buildings projects.

Microsoft’s Cortana Intelligence Suite

Microsoft offers from its AZURE cloud (Chapter 13) a combination of advanced analytics,

traditional BI, and Big Data analytics. The suite enables users to transform data into intelligent

actions.

Using Cortana, one can transform data from several sources, including from IoT sensors, and

apply both advanced analytics (e.g., data mining) and AI (e.g., machine learning) and extract

insights and actionable recommendations, which are delivered to decision makers, to apps, or to

fully automated systems. For the details of the system and the architecture of Cortana,

see mssqltips.com/sqlservertip/4360/introduction-to-microsoft-cortana-intelligence-suite/.

Section 1.8 Review Questions

1. What are the major benefits of intelligent systems convergences? 2. Why did analytics initiatives fail at such a high rate in the past? 3. What synergy can be created by combining AI and analytics? 4. Why is Big Data preparation essential for AI initiatives? 5. What are the benefits of adding IoT to intelligent technology applications? 6. Why it is recommended to use blockchain in support of intelligent applications?

7. nalytics Ecosystem 8. So you are excited about the potential of analytics, data science, and AI and want

to join this growing industry. Who are the current players, and what to do they

do? Where might you fit in? The objective of this section is to identify various

sectors of the analytics industry, provide a classification of different types of

industry participants, and illustrate the types of opportunities that exist for

analytics professionals. Eleven different types of players are identified in

an analytics ecosystem. An understanding of the ecosystem also gives the

reader a broader view of how the various players come together. A

secondary purpose of understanding the analytics ecosystem for a professional is

also to be aware of organizations and new offerings and opportunities in sectors

allied with analytics.

9. Although some researchers have distinguished business analytics professionals

from data scientists (Davenport and Patil, 2012), as pointed out previously, for

the purpose of understanding the overall analytics ecosystem, we treat them as

one broad profession. Clearly, skill needs can vary for a strong mathematician to

a programmer to a modeler to a communicator, and we believe this issue is

resolved at a more micro/individual level rather than at a macro level of

understanding the opportunity pool. We also take the widest definition of

analytics to include all three types as defined by INFORMS—

descriptive/reporting/visualization, predictive, and prescriptive as described

earlier. We also include AI within this same pool.

10. Figure 1.17 illustrates one view of the analytics ecosystem. The components of

the ecosystem are represented by the petals of an analytics flower. Eleven key

sectors or clusters in the analytics space are identified. The components of the

analytics ecosystem are grouped into three categories represented by the inner

petals, outer petals, and the seed (middle part) of the flower. The outer six petals

can be broadly termed technology providers. Their primary revenue comes from

providing technology, solutions, and training to analytics user organizations so

they can employ these technologies in the most effective and efficient manner.

The inner petals can be generally defined as the analytics accelerators. The

accelerators work with both technology providers and users. Finally, the core of

the ecosystem comprises the analytics user organizations. This is the most

important component as every analytics industry cluster is driven by the user

organizations. 11. Figure 1.17 Analytics Ecosystem.

12. 13. Figure 1.17 Full Alternative Text

14. The metaphor of a flower is well suited for the analytics ecosystem as multiple

components overlap each other. Similar to a living organism like a flower, all

these petals grow and wither together. Many companies play in multiple sectors

within the analytics industry and thus offer opportunities for movement within

the field both horizontally and vertically.

15. More details for the analytics ecosystem are included in our shorter book

(Sharda, Delen, and Turban, 2017) as well as in Sharda and Kalgotra (2018). Matt

Turck, a venture capitalist with FirstMark, has also developed and updates an

analytics ecosystem focused on Big Data. His goal is to keep track of new and

established players in various segments of the Big Data industry. A very nice

visual image of his interpretation of the ecosystem and a comprehensive listing

of companies is available through his Web

site: http://mattturck.com/2016/02/01/big-data-landscape/ (accessed September

2018).

1.10 Plan of the Book

The previous sections have given you an understanding of the need for information

technology in decision making, the evolution of BI, analytics, data science, and artificial

intelligence. In the last several sections, we have seen an overview of various types of

analytics and their applications. Now we are ready for a more detailed managerial

excursion into these topics along with some deep hands-on experience in some of the

technical topics. Figure 1.18 presents a plan on the rest of the book. Figure 1.18 Plan of the Book

In this chapter, we have provided an introduction, definitions, and overview of DSS, BI,

and analytics, including Big Data analytics and data science. We also gave you an

overview of the analytics ecosystem to have you appreciate the breadth and depth of

the industry. Chapters 2 and 3 cover descriptive analytics and data issues. Data clearly

form the foundation for any analytics application. Thus, we cover an introduction to

data warehousing issues, applications, and technologies. This chapter also covers

business reporting and visualization technologies and applications.

We follow the current chapter with a deeper introduction to artificial intelligence

in Chapter 2. Because data are fundamental to any analysis, Chapter 3 introduces data

issues as well as descriptive analytics, including statistical concepts and visualization.

An online chapter covers data warehousing processes and fundamentals for those who

like to dig more deeply into these issues. The next section of the book covers predictive

analytics and machine learning. Chapter 4 provides an introduction to data mining

applications and the data mining process. Chapter 5 introduces many of the common

data mining techniques: classification, clustering, association mining, and so

forth. Chapter 6 includes coverage of deep learning and cognitive

computing. Chapter 7 focuses on text mining applications as well as Web analytics,

including social media analytics, sentiment analysis, and other related topics. The

following section brings the “data science” angle into further depth. Chapter 8 covers

prescriptive analytics. Chapter 9 includes more details of Big Data analytics. It also

includes an introduction to cloud-based analytics as well as location analytics. The next

section covers robotics, social networks, AI, and IoT. Chapter 10 introduces robots in

business and consumer applications and discusses the future impact of such devices on

society. Chapter 11 focuses on collaboration systems, crowdsourcing, and social

networks. Chapter 12 reviews personal assistants, chatbots, and the exciting

developments in this space. Chapter 13 studies IoT and its potential in decision support

and a smarter society. The ubiquity of wireless and GPS devices and other sensors is

resulting in the creation of massive new databases and unique applications. A new

breed of analytics companies is emerging to analyze these new databases and create a

much better and deeper understanding of customers’ behaviors and movements. It is

leading to the automation of analytics and has spanned a new area called the “Internet

of Things.” Finally, Chapter 14 concludes with a brief discussion of security, privacy,

and societal dimensions of analytics/AI.

1.11 Resources, Links, and the Teradata University Network Connection

The use of this chapter and most other chapters in this book can be enhanced by the

tools described in the following sections.

Resources and Links

We recommend the following major organizational resources and links:

• The Data Warehousing Institute (tdwi.org).

• Data Science Central (datasciencecentral.com).

• DSS Resources (dssresources.com).

• Microsoft Enterprise Consortium (enterprise.waltoncollege.uark.edu/mec.asp).

Vendors, Products, and Demos

Most vendors provide software demos of their products and applications. Information about

products, architecture, and software is available at dssresources.com.

Periodicals

We recommend the following periodicals:

• Decision Support Systems (www.journals.elsevier.com/decision-support-systems). • CIO Insight (www.cioinsight.com).

The Teradata University Network Connection

This book is tightly connected with the free resources provided by TUN

(see www.teradatauniversitynetwork.com). The TUN portal is divided into two major parts: one

for students and one for faculty. This book is connected to the TUN portal via a special section at

the end of each chapter. That section includes appropriate links for the specific chapter, pointing

to relevant resources. In addition, we provide hands-on exercises using software and other

materials (e.g., cases) available at TUN.

The Book’s Web Site

This book’s Web site, pearsonhighered.com/sharda, contains supplemental textual material

organized as Web chapters that correspond to the printed book’s chapters. The topics of these

chapters are listed in the online chapter table of contents.

As this book went to press, we verified that all cited Web sites were active and valid. However,

URLs are dynamic. Web sites to which we refer in the text sometimes change or are

discontinued because companies change names, are bought or sold, merge, or fail. Sometimes

Web sites are down for maintenance, repair, or redesign. Many organizations have dropped the

initial “www” designation for their sites, but some still use it. If you have a problem connecting

to a Web site that we mention, please be patient and simply run a Web search to try to identify a

possible new site. Most times, you can quickly find the new site through one of the popular

search engines. We apologize in advance for this inconvenience.

Chapter Highlights

• The business environment is becoming more complex and is rapidly changing,

making decision making more difficult.

• Businesses must respond and adapt to the changing environment rapidly by

making faster and better decisions.

• A model is a simplified representation or abstraction of reality.

• Decision making involves four major phases: intelligence, design, choice, and

implementation.

• In the intelligence phase, the problem (opportunity) is identified, classified, and

decomposed (if needed), and problem ownership is established.

• In the design phase, a model of the system is built, criteria for selection are agreed

on, alternatives are generated, results are predicted, and a decision methodology is

created.

• In the choice phase, alternatives are compared, and a search for the best (or a good-

enough) solution is launched. Many search techniques are available.

• In implementing alternatives, a decision maker should consider multiple goals and

sensitivity-analysis issues.

• The time frame for making decisions is shrinking, whereas the global nature of

decision making is expanding, necessitating the development and use of

computerized DSS.

• An early decision support framework divides decision situations into nine

categories, depending on the degree of structuredness and managerial activities.

Each category is supported differently.

• Structured repetitive decisions are supported by standard quantitative analysis

methods, such as MS, MIS, and rule-based automated decision support.

• DSS use data, models, and sometimes knowledge management to find solutions for

semistructured and some unstructured problems.

• The major components of a DSS are a database and its management, a model base

and its management, and a user-friendly interface. An intelligent (knowledge-

based) component can also be included. The user is also considered to be a

component of a DSS.

• BI methods utilize a central repository called a DW that enables efficient data

mining, OLAP, BPM, and data visualization.

• BI architecture includes a DW, business analytics tools used by end users, and a

user interface (such as a dashboard).

• Many organizations employ descriptive analytics to replace their traditional flat

reporting with interactive reporting that provides insights, trends, and patterns in

the transactional data.

• Predictive analytics enables organizations to establish predictive rules that drive

the business outcomes through historical data analysis of the existing behavior of

the customers.

• Prescriptive analytics helps in building models that involve forecasting and

optimization techniques based on the principles of OR and management science to

help organizations to make better decisions.

• Big Data analytics focuses on unstructured, large data sets that may also include

vastly different types of data for analysis.

• Analytics as a field is also known by industry-specific application names, such as

sports analytics. It is also known by other related names such as data science or

network science.

• Healthcare and retail chains are two areas where analytics applications abound,

with much more to come.

• Image analytics is a rapidly evolving field leading to many applications of deep

learning.

• The analytics ecosystem can be first viewed as a collection of providers, users, and

facilitators. It can be broken into 11 clusters.

Key Terms • analytics • analytics ecosystem • artificial intelligence • augmented intelligence • Big Data analytics • business intelligence (BI) • choice phase • dashboard • data mining • decision or normative analytics • descriptive (or reporting) analytics • design phase • implementation phase • intelligence phase

• intelligent agents

• Internet of Things (IoT)

• narrow (weak) AI • online analytical processing (OLAP)

• online transaction processing (OLTP) • predictive analytics • prescriptive analytics

• strong (general) AI

Questions for Discussion 1. Survey the literature from the past six months to find one application each for

DSS, BI, and analytics. Summarize the applications on one page, and submit it with the exact sources.

2. Your company is considering opening a branch in China. List typical activities in each phase of the decision (intelligence, design, choice, and implementation) regarding whether to open a branch.

3. You are about to buy a car. Using Simon’s (1977) four-phase model, describe your activities at each step in making the decision.

4. Explain, through an example, the support given to decision makers by computers in each phase of the decision process.

5. 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.

6. Review the major characteristics and capabilities of DSS. How does each of them relate to the major components of DSS?

7. List some internal data and external data that could be found in a DSS for a university’s admissions office.

8. Distinguish BI from DSS. 9. Compare and contrast predictive analytics with prescriptive and descriptive

analytics. Use examples. 10. Discuss the major issues in implementing BI.

Exercises

Teradata University Network and Other Hands-On Exercises

1. Go to the TUN site teradatauniversitynetwork.com. Using the site password your instructor provides, register for the site if you have not already previously registered. Log on and learn the content of the site. You will receive assignments related to this site. Prepare a list of 20 items on the site that you think could be beneficial to you.

2. Go to. Explore the Sports Analytics page, and summarize at least two applications of analytics in any sport of your choice.

3. Go to. The TUN site, and select “Cases, Projects, and Assignments.” Then select the case study “Harrah’s High Payoff from Customer Information.” Answer the following questions about this case:

a. What information does the data mining generate?

b. How is this information helpful to management in decision making? (Be specific.)

c. List the types of data that are mined. d. Is this a DSS or BI application? Why?

4. Go to teradatauniversitynetwork.com and find the paper titled “Data Warehousing Supports Corporate Strategy at First American Corporation” (by Watson, Wixom, and Goodhue). Read the paper, and answer the following questions:

a. What were the drivers for the DW/BI project in the company? b. What strategic advantages were realized? c. What operational and tactical advantages were achieved? d. What were the critical success factors for the implementation?

5. Go to http://analytics-magazine.org/issues/digital-editions and find the January/February 2012 edition titled “Special Issue: The Future of Healthcare.” Read the article “Predictive Analytics—Saving Lives and Lowering Medical Bills.” Answer the following questions:

a. What problem is being addressed by applying predictive analytics? b. What is the FICO Medication Adherence Score? c. How is a prediction model trained to predict the FICO Medication

Adherence Score HoH? Did the prediction model classify the FICO Medication Adherence Score?

d. Zoom in on Figure 4, and explain what technique is applied to the generated results.

e. List some of the actionable decisions that were based on the prediction results.

6. Go to http://analytics-magazine.org/issues/digital-editions, and find the January/February 2013 edition titled “Work Social.” Read the article “Big Data, Analytics and Elections,” and answer the following questions:

a. What kinds of Big Data were analyzed in the article’s Coo? Comment on some of the sources of Big Data.

b. Explain the term integrated system. What is the other technical term that suits an integrated system?

c. What data analysis techniques are employed in the project? Comment on some initiatives that resulted from data analysis.

d. What are the different prediction problems answered by the models? e. List some of the actionable decisions taken that were based on the

prediction results. f. Identify two applications of Big Data analytics that are not listed in the

article. 7. Search the Internet for material regarding the work of managers and the role

analytics plays in it. What kinds 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.

8. Explore the public areas of dssresources.com. Prepare a list of its major available resources. You might want to refer to this site as you work through the book.

9. Go to microstrategy.com. Find information on the five styles of BI. Prepare a summary table for each style.

10. Go to oracle.com, and click the Hyperion link under Applications. Determine what the company’s major products are. Relate these to the support technologies cited in this chapter.

11. Go to the TUN questions site. Look for BSI videos. Review the video of “Case of Retail Tweeters.” Prepare a one-page summary of the problem, proposed solution, and the reported results. You can also find associated slides on slideshare.net.

12. Review the Analytics Ecosystem section. Identify at least two additional companies in at least five of the industry clusters noted in the discussion.

13. The discussion for the analytics ecosystem also included several typical job titles for graduates of analytics and data science programs. Research Web sites such as datasciencecentral.com and tdwi.org to locate at least three similar job titles that you may find interesting for your career.

14. Go to Brainspace at MIT lab brainspace.com. View the video about “Augmented Human Intelligence.” Find the activities that deal with the enabling of meaningful combination of people and machines. Write a report.

15. Find information about IBM Watson’s activities in the healthcare field. Write a report.

16. Examine Daniel Power’s DSS Resources site at dssresources.com. Take the Decision Support Systems Web Tour (dssresources.com/tour/index.html). Explore other areas of the Web site. List at least three recent resources related to analytics. What topics do these cover?

ferences • http://canopeoapp.com/ (accessed October 2018).

• http://imazon.org.br/en/imprensa/mapping-change-in-the-amazon-how-satellite-images- are-halting-deforestation/ (accessed October 2018).

• http://www.earthcastdemo.com/2018/07/bloomberg-earthcast-customizing- weather/ (accessed October 2018)

• https://www.worldbank.org/en/news/press-release/2018/02/22/world-bank-supports- sierra-leones-efforts-in-landslide-recovery (accessed October 2018)

• Siemens.com. About Siemens. siemens.com/about/en/ (accessed September 2018). • Silvaris.com. Silvaris overview. silvaris.com (accessed September 2018). • Agah, A. (2017). Medical Applications of Artificial Intelligence. Boca Raton, FL: CRC Press. • Anthony, R. N. (1965). Planning and Control Systems: A Framework for Analysis.

Cambridge, MA: Harvard University Graduate School of Business.

• Baker, J., and M. Cameron. (1996, September). “The Effects of the Service Environment on Affect and Consumer Perception of Waiting Time: An Integrative Review and Research Propositions.” Journal of the Academy of Marketing Science, 24, pp. 338–349.

• Bean, R. (2017, May 8). “How Big Data Is Empowering AI and Machine Learning at Scale.” MIT Sloan Management Review.

• Boddington, P. (2017, January 3). “Case Study: Robot Camel Jockeys. Yes, really.” Ethics for Artificial Intelligence.

• Brainspace. (2016, June 13). “Augmenting Human Intelligence.” MIT Technology Review Insights.

• Butner, K. (2018, January 8). “Combining Artificial Intelligence with the Internet of Things Could Make Your Business Smarter.” IBM Consulting Blog.

• CDC.gov. (2015, September 21). “Important Facts about Falls.” cdc.gov/homeandrecreationalsafety/falls/adultfalls.html (accessed September 2018).

• Charles, T. (2018, May 21). “Influence of the External Environment on Strategic Decision.” Azcentral. yourbusiness.azcentral.com/influence-external-environment- strategic-decisions-17628.html/ (accessed October 2018).

• Chiguluri, V., Guthikonda, K., Slabaugh, S., Havens, E., Peña, J., & Cordier, T. (2015, June). Relationship Between Diabetes Complications and Health Related Quality of Life Among an Elderly Population in the United States. Poster presentation at the American Diabetes Association Seventy-Fifth Annual Scientific Sessions. Boston, MA.

• Chongwatpol, J., & R. Sharda. (2010, December). “SNAP: A DSS to Analyze Network Service Pricing for State Networks.” Decision Support Systems, 50(1), pp. 347–359.

• Chung, C. (2016). “Dubai Camel Race Ride- Along.” YouTube.com. youtube.com/watch?v=xFCRhk4GYds (accessed September 2018).

• Cordier, T., Slabaugh, L., Haugh, G., Gopal, V., Cusano, D., Andrews, G., & Renda, A. (2015, September). Quality of Life Changes with Progressing Congestive Heart Failure. Poster presentation at the Nineteenth Annual Scientific Meeting of the Heart Failure Society of America, Washington, DC.

• Corea, F. (2017, December 1). “The Convergence of AI and Blockchain: What’s the Deal?” Medium.com. medium.com/@Francesco_AI/the-convergence-of-ai-and-blockchain- whats-the-deal-60c618e3accc (accessed September 2018).

• Davenport, T., & SAS Institute Inc. (2014, February). “Analytics in Sports: The New Science of Winning.” sas.com/content/dam/SAS/en_us/doc/whitepaper2/iia-analytics-in-sports- 106993.pdf (accessed September 2018).

• Davenport, T. H., & Patil, D. J. (2012). “Data Scientist.” Harvard Business Review, 90, 70–76. • Deahl, D. (2018, January 7). “This Automatic Feeder Can Tell the Difference Between Your

Pets.” The Verge. • De Smet, A., et al. (2017, June). “Untangling Your Organization’s Decision Making.” McKinsey

Quarterly. • Duncan, A. (2016). “The BICC Is Dead.” https://blogs.gartner.com/alan-

duncan/2016/03/11/the-bicc-is-dead/ (accessed October 2018).

• Dundas.com. “How Siemens Drastically Reduced Cost with Managed BI Applications.” www.dundas.com/Content/pdf/siemens-case-study.pdf (accessed September 2018).

• Eckerson, W. (2003). Smart Companies in the 21st Century: The Secrets of Creating Successful Business Intelligent Solutions. Seattle, WA: The Data Warehousing Institute.

• eMarketer. (2017, May). “Artificial Intelligence: What’s Now, What’s New and What’s Next?” EMarketer Inc.

• Emc.com. (n.d.). “Data Science Revealed: A Data-Driven Glimpse into the Burgeoning New Field.” emc.com/collateral/about/news/emc-data-science-study-wp.pdf (accessed September 2018)

• Emmert, Samantha. (2018, March 19). “Fighting Illegal Fishing.” Global Fishing Watch. globalfishingwatch.org/research/fighting-illegal-fishing/ (accessed October 2018).

• Faggela, D. (2017, August 24). “Artificial Intelligence Plus the Internet of Things (IoT): 3 Examples Worth Learning From.” TechEmergence.

• Faggela, D. (2018, March 29). “Artificial Intelligence Industry: An Overview by Segment.” TechEmergence.

• Fernandez, J. (2017, April). “A Billion People a Day. Millions of Elevators. No Room for Downtime.” IBM developer Works Blog. developer.ibm.com/dwblog/2017/kone-watson- video/ (accessed September 2018).

• FinTech Futures. (2017, October 11). “IBM Combining Data Science and AI for Analytics Advance.” BankingTech.com.

• Gartner, Inc. (2004). Using Business Intelligence to Gain a Competitive Edge. A special report.

• Gates, S., Smith, L. A., Fisher, J. D., et al. (2008). Systematic Review of Accuracy of Screening Instruments for Predicting Fall Risk Among Independently Living Older Adults. Journal of Rehabilitation Research and Development, 45(8), pp. 1105–1116.

• Gill, T. M., Murphy, T. E., Gahbauer, E. A., et al. (2013). “Association of Injurious Falls with Disability Outcomes and Nursing Home Admissions in Community Living Older Persons.” American Journal of Epidemiology, 178(3), pp. 418–425.

• Gorry, G. A., & Scott-Morton, M. S. (1971). “A Framework for Management Information Systems.” Sloan Management Review, 13(1), pp. 55–70.

• Havens, E., Peña, J., Slabaugh, S., Cordier, T., Renda, A., & Gopal, V. (2015, October). Exploring the Relationship Between Health-Related Quality of Life and Health Conditions, Costs, Resource Utilization, and Quality Measures. Podium presentation at the ISOQOL Twenty-Seventh Annual Conference, Vancouver, Canada.

• Havens, E., Slabaugh, L., Peña, J., Haugh, G., & Gopal, V. (2015, February). Are There Differences in Healthy Days Based on Compliance to Preventive Health Screening Measures? Poster presentation at Preventive Medicine 2015, Atlanta, GA.

• Healthcare IT News. (2017, November 9). “How AI Is Transforming Healthcare and Solving Problems in 2017.” Slideshow. healthcareitnews.com/slideshow/how-ai-transforming- healthcare-and-solving-problems-2017?page=4/ (accessed September 2018).

• Hesse, R., & G. Woolsey. (1975). Applied Management Science: A Quick and Dirty Approach. Chicago, IL: SRA Inc.

• Humana. 2016 Progress Report. populationhealth.humana.com/wp- content/uploads/2016/05/BoldGoal2016ProgressReport_1.pdf (accessed September 2018).

• INFORMS. Analytics Section Overview. informs.org/Community/Analytics (accessed September 2018).

• Jacquet, F. (2017, July 4). “Exploring the Artificial Intelligence Ecosystem: AI, Machine Learning, and Deep Learning.” DZone.

• Jarrett, C. (2018, January 21). “Amazon Set to Open Doors on AI-Powered Grocery Store.” Venturebeat.com. venturebeat.com/2018/01/21/amazon-set-to-open-doors-on-ai- powered-grocery-store/ (accessed September 2018).

• Keen, P. G. W., & M. S. Scott-Morton. (1978). Decision Support Systems: An Organizational Perspective. Reading, MA: Addison-Wesley.

• Kemper, C., and C. Breuer. (2016). “How Efficient Is Dynamic Pricing for Sports Events? Designing a Dynamic Pricing Model for Bayern Munich.” International Journal of Sports Finance, 11, pp. 4–25.

• Kranz, M. (2017, December 27). “In 2018, Get Ready for the Convergence of IoT, AI, Fog, and Blockchain.” Insights.

• Liberto, D. (2017, June 29). “Artificial Intelligence Will Add Trillion to the Global Economy: PwC.” Investopedia.

• Lollato, R., Patrignani, A., Ochsner, T. E., Rocatelli, A., Tomlinson, P. & Edwards, J. T. (2015). “Improving Grazing Management Using a Smartphone App.” www.bookstore.ksre.ksu.edu/pubs/MF3304.pdf (accessed October 2018).

• Lopez, J. (2017, August 11). “Smart Farm Equipment Helps Feed the World.” IQintel.com.

• Metz, C. (2018, February 12). “As China Marches Forward on A.I., the White House Is Silent.” The New York Times.

• Nadav, S. (2017, August 9). “Business Intelligence Is Failing; Here Is What Is Coming Next.” Huffington Post.

• Norman, A. (2018, January 31). “Your Future Doctor May Not Be Human. This Is the Rise of AI in Medicine.” Futurism.com.

• Olshansky, C. (2017, August 24). “Coca-Cola Is Bringing Artificial Intelligence to Vending Machines.” Food & Wine.

• Opfer, C. (2016, June 22). “There’s One Terrific Reason to Race Camels Using Robot Jockeys.” Howstuffworks.com.

• Padmanabhan, G. (2018, January 4). “Industry-Specific Augmented Intelligence: A Catalyst for AI in the Enterprise.” Forbes.

• Pajouh Foad, M., Xing, D., Hariharan, S., Zhou, Y., Balasundaram, B., Liu, T., & Sharda, R. (2013). Available-to-Promise in Practice: An Application of Analytics in the Specialty Steel

Bar Products Industry. Interfaces, 43(6), pp. 503– 517. dx.doi.org/10.1287/inte.2013.0693 (accessed September 2018).

• Patrignani, A., & Ochsner, T. E., (2015). Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover. Agronomy Journal, 107(6), pp. 2312–2320;

• PE Report. (2017, July 29). “Satellite-Based Advance Can Help Raise Farm Output by 20 Percent Experts.” Financial Express.

• PricewaterhouseCoopers Report. (2011, December). “Changing the Game: Outlook for the Global Sports Market to 2015.” pwc.com/gx/en/hospitality-leisure/pdf/changing-the- game-outlook-for-the-global-sports-market-to-2015.pdf (accessed September 2018).

• Quain, S. (2018, June 29). “The Decision-Making Process in an Organization.” Small Business Chron.

• Reisinger, D. (2018, February 22). “Here Are the Next Cities to Get Amazon Go Cashier-Less Stores.” Fortune.

• Sharda, R., Asamoah, D., & Ponna, N. (2013). “Research and Pedagogy in Business Analytics: Opportunities and Illustrative Examples.” Journal of Computing and Information Technology, 21(3), pp. 171–182.

• Sharda, R., Delen, D., & Turban, E. (2016). Business Intelligence, Analytics, and Data Science: A Managerial Perspective on Analytics. 4th ed. NJ: Pearson.

• Sharda, R., & P. Kalgotra. (2018). “The Blossoming Analytics Talent Pool: An Overview of the Analytics Ecosystem.” In James J. Cochran (ed.). INFORMS Analytics Body of Knowledge. John Wiley, Hoboken, NJ

• Silk, R. (2017, November). “Biometrics: Facial Recognition Tech Coming to an Airport Near You.” Travel Weekly, 21.

• Simon, H. (1977). The New Science of Management Decision. Englewood Cliffs, NJ: Prentice Hall.

• Slade, L. (2017, December 21). “Meet the Jordanian Camel Races Using Robot Jockeys.” Sbs.com.au.

• Slowey, L. (2017, February 16). “Look Who’s Talking: KONE Makes Elevator Services Truly Intelligent with Watson IoT.” IBM Internet of Things Blog. ibm.com/blogs/internet-of- things/kone/ (accessed September 2018).

• “Sports Analytics Market Worth by 2021.” (2015, June 25). Wintergreen Research Press Release. Covered by PR Newswire at http://www.prnewswire.com/news-releases/sports- analytics-market-worth-47-billion-by-2021-509869871.html.

• Srikanthan, H. . (2018, January 8). “KONE Improves ‘People Flow’ in 1.1 Million Elevators with IBM Watson IoT.” Generis. https://generisgp.com/2018/01/08/ibm-case-study-kone- corp/ (accessed September 2018).

• Staff. “Assisted, Augmented and Autonomous: The 3Flavours of AI Decisions.” (2017, June 28). Software and Technology.

• Tableau.com. Silvaris Augments Proprietary Technology Platform with Tableau’s Real-Time Reporting Capabilities. tableau.com/sites/default/files/case-studies/silvaris-business- dashboards_0.pdf (accessed September 2018).

• Tartar, Andre, et al. (2018, 26 July). “All the Things Satellites Can Now See from Space.” Bloomberg.com. www.bloomberg.com/news/features/2018-07-26/all-the-things- satellites-can-now-see-from-space (accessed October 2018).

• TeradataUniversityNetwork.com. (2015, Fall). “BSI: Sports Analytics—Precision Football” (video). teradatauniversity network.com/About-Us/Whats-New/BSI–Sports-Analytics- Precision-Football/ (accessed September 2018).

• Thibodeaux, W. (2017, June 29). “This Artificial Intelligence Kiosk Is Designed to Spot Liars at Airports.” Inc.com.

• Turck, Matt. “Is Big Data Still a Thing? (The 2016 Big Data Landscape).” http://mattturck.com/2016/02/01/big-data-landscape/ (accessed September 2018).

• Watson, H. (2005, Winter). Sorting Out What’s New in Decision Support. Business Intelligence Journal.

• Weldon, D. (2018, March 6). “Nearly Half of CIOs Now Plan to Deploy Artificial Intelligence.” Information Management.

• Wikipedia.org. On-base Percentage. wikipedia.org/wiki/On_base_percentage. (accessed September 2018).

• Wikipedia.org. Sabermetrics. wikipedia.org/wiki/Sabermetrics (accessed September 2018).

• Wikipedia.org. SIEMENS. wikipedia.org/wiki/Siemens (accessed September 2018). • YouTube.com. (2013, December 17). CenterPoint Energy Talks Real Time Big Data

Analytics. youtube.com/watch?v=s7CzeSlIEfI (accessed September 2018).

• Yue, P. (2017, August 24). “Baidu, Beijing Airport Launch Facial Recognition for Passenger Check-In.” China Money Network. https://www.chinamoneynetwork.com/2017/08/24/baidu-capital-airport- launch-facial-recognition-system-airport (accessed October 2018).

• Zane, E. B. (2016). Effective Decision-Making: How to Make Better Decisions Under Uncertainty And Pressure. Kindle ed. Seattle, WA: Amazon Digital Services.