BI, Analytics & Decision Support – Discussion

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1

LEARNING OBJECTIVES

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

■■ 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

T he business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pres- sures that force them to respond quickly to changing conditions and to be in-

novative 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

C H A P T E R

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 39

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 tech- niques 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 applica- tions. 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 devel- oping 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 experi- ence, 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 experi- enced specific software introduced by the professor.

This introductory chapter provides an introduction to analytics and artificial intel- ligence as well as an overview of the book. The chapter has the following sections:

1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and Escalators Company 39

1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics 41

1.3 Decision-Making Processes and Computer Decision Support Framework 45 1.4 Evolution of Computerized Decision Support to Business Intelligence/

Analytics/Data Science 58 1.5 Analytics Overview 66 1.6 Analytics Examples in Selected Domains 74 1.7 Artificial Intelligence Overview 88 1.8 Convergence of Analytics and AI 95 1.9 Overview of the Analytics Ecosystem 99

1.10 Plan of the Book 101 1.11 Resources, Links, and the Teradata University Network Connection 102

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 eleva- tors 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, can- not get home in time, and may miss important meetings and events. So, KONE’s objective is to minimize the downtime and users’ suffering.

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40 Part I • Introduction to Analytics and AI

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 poten- tial 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 equip- ment 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 immedi- ately respond to emergencies or soon-to-occur failures as quickly as possible, dispatch- ing 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 mak- ers 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).

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 41

u 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 techno- logically 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 dif- ferent locations. This vignette also introduces us to two major types of analytics: predic- tive analytics (Chapters 4–6) and prescriptive analytics (Chapter 8).

Several AI technologies are discussed: machine learning, natural language process- ing, 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 compa- nies, 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 orga- nizations 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 ex- ample, 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, or- ganizational 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.

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42 Part I • Introduction to Analytics and AI

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 manage- rial problems. These styles were often based on creativity, judgment, intuition, and experience rather than on systematic quantitative methods grounded in a scientific ap- proach. 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 interper- sonal 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 alterna- tive 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 re- sult in more and more alternatives from which to choose.

• Government regulations and the need for compliance, political instability and ter- rorism, 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.

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• 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 unpredict- able 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 deci- sions 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 organiza- tion 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, computer- ized 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 eval- uation 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 ac- tivities 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 deci- sions. 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 devel- opments have clearly contributed to facilitating the growth of decision support and ana- lytics 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

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44 Part I • Introduction to Analytics and AI

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 neces- sary 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 orga- nize, 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 pos- sible 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 infor- mation. 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 informa- tion about their own operations, customers, internal procedures, employee interac- tions, 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 manag- ers, 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

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 45

data-driven support for any decision extends not just to managers but also to con- sumers. 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).

u 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 deci- sion 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), mana- gerial 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 imple- mentation 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.

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 owner- ship 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

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46 Part I • Introduction to Analytics and AI

the relationships among all the variables. The model is then validated, and criteria are de- termined 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: imple- mentation 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

Success

Organization objectives Search and scanning procedures Data collection Problem identification Problem ownership Problem classification Problem statement

Solution to the model Sensitivity analysis Selection to the best (good) alternative(s)

Plan for implementation

Formulate a model Set criteria for choice Search for alternatives Predict and measure outcomes

Assumptions

Simplification

Problem Statement

Alternatives

Validation of the Model

Verification, Testing of the Proposed Solution

Implementation of the solution

Failure

Intelligence

Design

Choice

Reality

FIGURE 1.1 The Decision-Making/Modeling Process.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 47

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 clas- sical example in Application Case 1.1.

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 manag- ing 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 manage- ment determined that the real problem was not

about the actual waiting time but rather the per- ceived 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)

Application Case 1.1 Making Elevators Go Faster!

(Continued )

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48 Part I • Introduction to Analytics and AI

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 struc- turedness evident in them. This ranges from totally structured (i.e., programmed) to to- tally 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 sem- istructured problem results when some phases of decision making are structured whereas other phases are unstructured, and when some subproblems of a decision- making prob- lem are structured with others unstructured, the problem itself is semistructured. As a de- cision 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 assign- ment of authority to solve the problem is called problem ownership. For example, a man- ager 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 manag- ers 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.

give several other examples of distractions, includ- ing 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.

Application Case 1.1 (Continued)

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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 irrel- evant 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 mu- tual 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 be- tween 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 activi- ties 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 impor- tance and encourage readers to focus on it independently. Implementation also includes

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50 Part I • Introduction to Analytics and AI

a thorough understanding of project management. The importance of project manage- ment 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 situ- ations. 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 con- cepts 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.

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 struc- tured (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 solu- tion 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 strat- egy, the objectives are clearly defined. Common objectives are cost minimization and profit maximization.

Semistructured problems fall between structured and unstructured problems, hav- ing 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

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 51

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 pur- pose of this matrix was to suggest different types of computerized support to differ- ent 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 his- torically 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.

Monitoring accounts receivable Monitoring accounts payable Placing order entries

Operational Control

Structured

Managerial Control

Strategic Planning

Semistructured

Unstructured

Analyzing budget Forecasting short-term Reporting on personnel Making or buying

Scheduling production Controlling inventory

Evaluating credit Preparing budget Laying out plant Scheduling project Designing reward system Categorizing inventory

Building a new plant Planning mergers and acquisitions Planning new products Planning compensation Providing quality assurance Establishing human resources policies Planning inventory

Buying software Approving loans Operating a help desk Selecting a cover for a magazine

Negotiating Recruiting an executive Buying hardware Lobbying

Planning research and development Developing new technologies Planning social responsibility

Type of Decision

Type of Control

1 2 3

4 5 6

7 8 9

Managing finances Monitoring investment portfolio Locating warehouse Monitoring distribution systems

FIGURE 1.2 Decision Support Frameworks.

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52 Part I • Introduction to Analytics and AI

Structured problems, which are encountered repeatedly, have a high level of struc- ture, 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, pro- curement, 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 automat- ing 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 com- munication and collaboration technologies, as well as cognitive computing (Chapter 6) and deep learning (Chapter 5).

COMPUTER SUPPORT FOR SEMISTRUCTURED PROBLEMS Solving semistructured prob- lems 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 al- gorithms. Not specifically stated but implied in the early definitions was the notion that the system would be computer based, would operate interactively online, and prefer- ably 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 op- portunity. 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 gener- ally 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 mak- ing. 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

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 53

end users) through an interactive and iterative process. It can support all phases of deci- sion 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.

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

Is adaptable and flexible

7

Provides interactivity, ease of use

8

Support variety of decision

processes and styles

6

Improves effectiveness and efficiency

9

Supports intelligence,

design, choice, and implementation

5

Provides complete human control of

the process

10

Supports managers at

all levels

2 Provides support for semistructured or unstructured

problems

1 Can be stand-

alone, integrated, and Web- based tool

14

Provides data access

13

Supports individuals and groups

3

Provides models and analysis

12

Supports interdependent or sequential

decisions

4

Provides ease of development by end users

11 Decision Support System (DSS)

FIGURE 1.3 Key Characteristics and Capabilities of DSS.

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54 Part I • Introduction to Analytics and AI

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 soft- ware 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 dif- ferent configurations.

13. Provides access to a variety of data sources, formats, and types, including GIS, mul- timedia, 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 organiza- tions along the supply chain. It can be integrated with other DSS and/or applica- tions, and it can be distributed internally and externally, using networking and Web technologies.

These key DSS characteristics and capabilities allow decision makers to make bet- ter, 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 manage- ment subsystem, a user interface subsystem, and a knowledge-based management sub- system. We show these in Figure 1.4.

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.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 55

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 cus- tom 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 de- velopment 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 neces- sary 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

Data: internal and/or external

ERP/POS

Legacy

Web, etc.

Other Computer-Based

Systems

Internet, Intranet, Extranet

Data Management

User Interface

Knowledge-Based Subsystems

Manager (user)Organizational Knowledgebase

External Models

Model Management

FIGURE 1.4 Schematic View of DSS.

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56 Part I • Introduction to Analytics and AI

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 ex- amples in this book. Application Case 1.2 describes a spreadsheet-based DSS.

The User Interface Subsystem

The user communicates with and commands the DSS through the user interface subsys- tem. 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 man- agers 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 infor- mation 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

Telecommunications network services to educational institutions and government entities are typically pro- vided 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 net- work 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 institu- tions 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 develop- ment 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 dif- ferent 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.

Application Case 1.2 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 57

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 them- selves 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 al- gorithm 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 com- parisons 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 so- cial 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 aug- ment the decision maker’s own or to help understand a user’s query so as to provide a consis- tent 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 com- ponents. 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.

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58 Part I • Introduction to Analytics and AI

u 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 exam- ples 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 busi- ness. 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 “inter- active 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):

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.

Big Data AutomationAnalyticsBusiness IntelligenceEnterprise/Executive IS

Routine Reporting

AI/Expert Systems

Decision Support Systems

Relational DBM S

On-Demand Static Reporting

Enterprise Resource Planning

Data W arehousing

Dashboards, Scorecards

Executive Information Systems

Software as a Service

Data/Text/W eb M

ining

Business Intelligence, BPM

Cloud, Big Data Analytics

In-M emory/In-Database/M

PP

Social Network/M edia Analytics

Automated Analytics

Al/Deep Learning, loT/Sensors

Robotics, Smart Robo-Assistants

Decision Support Systems

1970s 1980s 1990s 2000s 2010s 2020s

FIGURE 1.5 Evolution of Decision Support, Business Intelligence, Analytics, and AI.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 59

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 dif- ferent 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 ex- perts 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 mod- els had emerged: rule-based expert systems (ESs). These systems promised to capture ex- perts’ 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 en- terprise 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 execu- tive 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 in- dicators. 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

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60 Part I • Introduction to Analytics and AI

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 up- dated 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 pro- cesses 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 ana- lytics business models. Smaller companies, therefore, gained access to analytics capabili- ties 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 gen- eration mediums have emerged. Of all the new data sources (e.g., radio-frequency iden- tification [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 com- putational 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, im- proving customer engagement through targeting and customization, and developing entirely

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 61

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 effec- tiveness 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 meth- odologies 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 deci- sions quickly. These systems, which were generally called EISs, then began to offer addi- tional 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 architec- tures, 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 confu- sion 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 busi- ness managers and analysts the ability to conduct appropriate analyses. By analyzing his- torical 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 sys- tems 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 mul- tidimensional (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 ca- pabilities 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.

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 re- lationship among these components is illustrated in Figure 1.7.

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62 Part I • Introduction to Analytics and AI

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.

Business Intelligence

Spreadsheets (MS Excel)

DSS

ETL

Data Warehouse

Data Marts

Metadata

Querying and Reporting

EIS/ESS

Broadcasting Tools

Portals

OLAP

Scorecards and Dashboards

Alerts and Notifications

Data and Text Mining Predictive

Analytics

Digital Cockpits and Dashboards

Workflow

Financial Reporting

FIGURE 1.6 Evolution of Business Intelligence (BI).

Technical staff

Build the data warehouse

– Organizing – Summarizing – Standardizing

Data warehouse

Business users

Access

Manipulation, results

Managers/executives

BPM strategies

Future component: Intelligent systems

User interface

– Browser – Portal – Dashboard

Data Warehouse Environment

Business Analytics Environment

Performance and Strategy

Data Sources

FIGURE 1.7 A High-Level Architecture of BI. Source: Based on W. Eckerson. (2003). Smart

Companies in the 21st Century: The Secrets of Creating Successful Business Intelligent Solutions.

Seattle, WA: The Data Warehousing Institute, p. 32, Illustration 5.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 63

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 impera- tive. 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 organiza- tions 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, trans- formed, 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 man- agers 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 en- tire 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 in- tegration 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.

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 transac- tion, 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

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64 Part I • Introduction to Analytics and AI

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 per- sonnel 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 sys- tems 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 con- ditions 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 histori- cal 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.

Data Marts

Applications (Visualization)

Data/Text Mining

OLAP, Dashboard, Web

Routine Business Reporting

Custom-Built Applications

Data Sources

POS

Other OLTP/Web

External Data

ETL Process

Select

Extract

Transform

Integrate

Load

Metadata

Enterprise Data

Warehouse

Replication

Data mart (Marketing)

Data mart (Operations)

Data mart (Finance)

Data mart (…)

No data mart options

ERP

Legacy

A P

I/ M

id dl

ew ar

e

FIGURE 1.8 Data Warehouse Framework and Views.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 65

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 run- ning 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 pas- sengers 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 analy- sis 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 re- porting, communication, and collaboration (a form of data-oriented DSS). Another expla- nation (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 con- ducts business by improving its business processes and transforming decision- making processes to be more data driven. Many BI consultants and practitioners involved in suc- cessful 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 busi- ness and organizational levels, strategic and operational objectives must be defined while considering the available organizational skills to achieve those objectives. Issues of orga- nizational culture surrounding BI initiatives and building enthusiasm for those initiatives and procedures for the intra-organizational sharing of BI best practices must be consid- ered 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 as- sessment, 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 through-

out the company. • The IS organization can learn a great deal through interaction with the user communi-

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

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66 Part I • Introduction to Analytics and AI

• 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 pack- ages (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 advan- tage 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.

u 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 comput- erized 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

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 67

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 com- bination of computer technology, management science techniques, and statistics to solve real problems. Of course, many other organizations have proposed their own interpreta- tions 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 technolo- gies 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 pro- posing 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

Descriptive

What happened? What is happening?

Q ue

st io

ns E

na bl

er s

Well-defined business problems and opportunities

O ut

co m

es

Business reporting Dashboards Scorecards Data warehousing

Business Analytics

Predictive

What will happen? Why will it happen?

Prescriptive

What should I do? Why should I do it?

Accurate projections of future events and

outcomes

Best possible business decisions

and actions

Data mining Text mining Web/media mining Forecasting

Optimization Simulation Decision modeling Expert systems

FIGURE 1.9 Three Types of Analytics.

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68 Part I • Introduction to Analytics and AI

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.

Descriptive Analytics

Descriptive (or reporting) analytics refers to knowing what is happening in the or- ganization 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 ap- propriate 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. Two examples of the application of ad- vanced visualization tools in business operations, available to both private and public companies, are analyzed in the Application Cases 1.3 and 1.4.

An Post, the state-owned corporation that manages postal services in the Republic of Ireland, presents an interesting and well-documented case of suc- cessful innovation in the public sector, which is often plagued by inefficiency and lackluster per- formance. Established in 1984, An Post is now one of Ireland’s largest public employers. For most of its history, An Post’s operations did not meet the breakeven point, let alone make a profit. This only changed in 2014, after eight years of losses.

This is why it came as a surprise when An Post made the headlines in 2018 as one of the rare public services to have successfully adopted advanced technological solutions to address customers’ orders and feedback and make them significantly more manageable and streamlined.

Interaction with customers can be particu- larly tricky and time-consuming, and this is where platform innovation and visualization tools can help. For An Post, this service is provided by the Oracle Analytics Cloud platform and An Post’s own Customer Experience Management portal. Oracle Analytics Cloud is one of the most comprehensive cloud analytics platforms, offering self-service visual- ization, powerful inline data preparation to enterprise reporting, and self-learning analytics.

Today, An Post’s portal allows a select few corporate customers, including Amazon, Sky TV, and the Inditex Group (which owns brands such as

Massimo Dutti and Zara), to visualize their parcel traffic on An Post’s cloud service. There are plans to make this data visualization tool available to more of An Post’s customers.

The move to data visualization services was so successful that the state-owned public service was honored with the 2018 Analytics Innovation—Global Excellence Award, celebrated in October 2018 at the Oracle Open World ceremony in San Francisco.

Sources: An Post Media centre. “Oracle Says ‘An Post You’re a Winner'.” https://www.anpost.com/Media-Centre/News/Oracle- says-An-Post-you-re-a-winner. Oracle.com. “Oracle Analytics Cloud Overview.” https://www.oracle.com/technetwork/ middleware/oac/overview/index.html.

Questions for Case 1.3

1. Why was Oracle’s system important for An Post?

2. What additional challenges does a state-owned service face when adopting innovative solutions?

What We Can Learn from This Application Case

While the adoption of innovation technology might at times appear daunting, it is always advantageous to streamline processes. As the case shows, public services can also benefit from adopting them in their operations.

Application Case 1.3 An Post and the Use of Data Visualization in Daily Postal Operations

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 69

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 com- petitor (“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, de- cision 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 how Asian companies have made use of predictive analytics.

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 rev- enue 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 fac- ing the challenge of providing reporting solutions to the entire Siemens organization across different depart- ments while maintaining a balance between gover- nance and self-service capabilities. Siemens needed a platform that could analyze its multiple cases of cus- tomer satisfaction surveys, logistic processes, and finan- cial 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 sys- tems 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 dash- boards 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 dif- ferent 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 (ac- cessed September 2018); Siemens.com. “About Siemens.” http:// www.siemens.com/about/en/ (accessed September 2018).

Application Case 1.4 Siemens Reduces Cost with the Use of Data Visualization

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70 Part I • Introduction to Analytics and AI

Predictive analytics is widely held to be the most actionable form of business intelligence. As IBM famously stated, if business can be considered a “numbers game,” predictive analytics is the way this game is best played and won.

Many companies in China and Hong Kong are increasingly using data mining and predictive analyt- ics to better cater to their customers’ needs. This has led to the growth, in the last ten years, of enterprises specializing in these IT solutions. Among them is the award-winning SagaDigits Group.

Incorporated in 2016 in Hong Kong, the group consists of two sister organizations that work together to offer services to Asian busi- nesses. SagaDigits Limited provides data min- ing, cleansing, extraction, and analytics services based on a series of methods, including natural language processing, big data, and AI technolo- gies. Compathnion Technology Limited special- izes instead in data collection, visual recognition, predictive analytics, and statistical modeling for indoor and outdoor uses.

One of the most interesting scalable solutions that SagaDigits offers its customers is Smart Box, an original, highly configurable AI solution for online and offline retail stores. When a company adopts this product for its retail business, it gets a real-time zone detection service in selected retail stores to predict the number of shoppers in those specific areas and learn their preferences based on the spatial informa- tion collected.

To successfully predict customer behavior, Smart Box employs a mixed set of indicators and informa- tion tools, including advanced visual recognition. Its sensors can detect consumers’ gender, emotion, and approximate age group with a high level of accu- racy. Finally, based on its own behavioral models’

predictions and the historical records of similar cus- tomers’ transaction histories, Smart Box provides auto- matic recommendations for advertisement and prod- uct selection for display.

Smart Box is one among many of SagaDigits’ solutions that use predictive analytics. Another system is Smart User Pro, which uses a variety of public data and internal data to predict various marketing trends in retail and marketing for consumer goods.

Sources: Eric Seigel (2015). “Seven Reasons You Need Predictive Analytics Today.” Prediction Impact, Inc. https://www.ibm. com/downloads/cas/LKMPR8AJ (accessed October 2019). Saga Digits. https://sagadigits.com/about (accessed October 2019). https://sagadigits.com (accessed October 2019).

Questions for Case 1.5

1. Why is predictive analytics becoming increas- ingly common?

2. What is the most interesting feature of Smart Box?

3. To which kind of corporate organization is Smart Box targeted?

4. Describe alternative uses of predictive analytics that Saga Digits has developed solutions for.

What We Can Learn from This Application Case

Innovative IT solutions and sophisticated tools such as predictive analytics are being increasingly used across the world. East Asia was one of the first regions to adopt them, especially places such as Hong Kong, which has many links with North American universi- ties focused on technology and computer science. Saga Digits is one of many companies that offer pre- dictive analytics for consumers’ behaviors and future marketing trends.

Application Case 1.5 SagaDigits and the Use of Predictive Analytics

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 deci- sions 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

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 71

optimizing the performance of a system. The goal here is to provide a decision or a recom- mendation 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 deci- sion 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.

ANALYTICS APPLIED TO DIFFERENT DOMAINS Applications of analytics in various in- dustry 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

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 sup- plies custom configured steel bars to its customers. These bars may be cut into specific shapes or sizes and may have unique material and finishing require- ments. 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 spe- cific material requirements (composition, origin of the metal, quality, shapes, sizes, etc.), the salesper- son usually has just a little bit of time to submit such a quote including the date when the product can be delivered, 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 dur- ing 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 com- mitted to another customer’s order. Thus, the inven- tory 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 con- straints, 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 pro- gramming models that are solved using commercial software. The company has incorporated the DSS into its enterprise resource planning system to seam- lessly 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).

Application Case 1.6 A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates

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72 Part I • Introduction to Analytics and AI

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 ana- lytics, behavioral analytics, and so forth. For example, we will soon see several ap- plications in sports 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 profession- als 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 differ- entiation 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 at- tention 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 sci- ence 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 compila- tion, 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 predic- tive analysis, statistical analysis, and use of more advanced analytical tools and algo- rithms. 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 signifi- cant 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 analyt- ics, 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

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 73

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 ver- satile 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, un- structured, 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 computa- tion 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 ve- locity 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

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74 Part I • Introduction to Analytics and AI

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.

u SECTION 1.5 REVIEW QUESTIONS

1. Define analytics.

2. What is descriptive analytics? What are the various tools that are employed in descrip- tive 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 ap- plication 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 billion in revenues each year plus an additional $100 billion in legal and $300 billion in illegal gambling, accord- ing to Price Waterhouse (“Changing the Game: Outlook for the Global Sports Market to 2015” (2015)). In 2014, only $125 million was spent on analytics (less than 0.1 percent

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 75

of revenues). This is expected to grow at a healthy rate to $4.7 billion by 2021 (“Sports Analytics Market Worth $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 in- dividual 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 im- proved traditional industry decision making.

Example 1: The Business Office

Dave Ward works as a business analyst for a major pro baseball team, focusing on rev- enue. 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 be- havior 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 renew- ing 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

Tier Highly Likely Likely Maybe Probably Not Certainly Not

1 92 88 75 69 45

2 88 81 70 65 38

3 80 76 68 55 36

4 77 72 65 45 25

5 75 70 60 35 25

FIGURE 1.10 Season Ticket Renewals—Survey Scores.

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76 Part I • Introduction to Analytics and AI

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.

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 indi- vidual seats. This is a rich research area for many sports teams and has huge upside po- tential 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.

Which of these factors are important and by how much? Given his extensive sta- tistics 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 custom- ers 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 atten- dance 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!

Seat Location

Team Performance

Time-Related Variables

Game Start Time

Part of the Season

Days before the Game

Home Team Performance in Past 10 Games

Opponent Made Playoffs Previous Year

Individual Player Reputations

Which Pitcher? What’s His Earned Run Average?

Number of All Stars on Opponent’s Roster

Opponent from Same Division

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.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 77

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 play- ers, 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 tenden- cies 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.

New work that Dar is fostering involves building better high school athlete recruit- ing 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

Total # of Plays: 540 Percentage of Run: 46.48% Percentage of Pass: 53.52%

Total # of Plays: 155 Percentage of Run: 79.35% Percentage of Pass: 20.65%

Total # of Plays: 385 Percentage of Run: 33.25% Percentage of Pass: 66.75%

Total # of Plays: 294 Percentage of Run: 38.78% Percentage of Pass: 61.22%

Total # of Plays: 91 Percentage of Run: 15.38% Percentage of Pass: 84.62%

Total # of Plays: 162 Percentage of Run: 50.62% Percentage of Pass: 49.38%

Total # of Plays: 132 Percentage of Run: 24.24% Percentage of Pass: 75.67%

Total # of Plays: 25 Percentage of Run: 44.00% Percentage of Pass: 56.00%

Total # of Plays: 66 Percentage of Run: 4.55%

Percentage of Pass: 95.45%

12, 21, 30, 31, 32 10, 11, 20, 22, or Missing

If it is…

If… If the distance to achieve the next down is

More than 5 yardsLess than 5 yardsWe are behind We are leading

or it is a tie

1st or 2nd Down 3rd or 4th Down

If Off_Pers is

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.

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78 Part I • Introduction to Analytics and AI

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 ana- lysts, 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 condi- tions for PGA tournaments to baseball and basketball referees who are rated on the cor- rect 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 sec- tion 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

A Complete: 35

Total: 46 76.08%

Explosive: 4

1 Complete: 25

Total: 35 71.4%

Explosive: 1

B Complete: 6

Total: 8 75.00%

Explosive: 5

2 Complete: 12

Total: 24 50%

Explosive: 0

3 Complete: 14

Total: 28 50%

Explosive: 0

4 Complete: 8

Total: 14 57.14%

Explosive: 0

6 Complete: 7

Total: 10 70%

Explosive: 2

7 Complete: 13

Total: 21 61.9%

Explosive: 9

8 Complete: 7

Total: 10 70%

Explosive: 6

9 Complete: 15

Total: 27 55.55%

Explosive: 8

5 Complete: 25

Total: 44 56.81%

Explosive: 1

C Complete: 22

Total: 27 81.48%

Explosive: 2

X Complete: 1

Total: 13 7.69%

Explosive: 1

Y Complete: 7

Total: 18 38.88%

Explosive: 7

Z Complete: 5

Total: 15 33.33%

Explosive: 6

Line of Scrimmage

Defense

Offense

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.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 79

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 en- gagement, 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 billion in 2013 alone.1

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ud_d_cov3

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ud_d_cov4 JAM ud_d_cov6

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ud_d_covFIRES

ud_d_covFLAME

ud_d_covHANDS

ud_d_covHARD ud_d_covHERO

ud_d_covHOT

ud_d_covLEVELS

ud_d_covMIX

ud_d_covROBBER

ud_d_covROLL

ud_d_covSKY

ud_d_covSMOKE

ud_d_covSPARK

ud_d_covSQUAT

ud_d_covSTATE

ud_d_covWALL

ud_d_off_pers10

ud_d_off_pers11

ud_d_off_pers12

ud_d_off_pers20

ud_d_off_pers21

ud_d_off_pers22

ud_d_off_pers23

ud_d_off_pers30

ud_d_off_pers31

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FIGURE 1.14 Time-Series Analysis of Explosive Plays.

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80 Part I • Introduction to Analytics and AI

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 com- ponent. However, no rigorous methodology was available to identify individuals most likely to fall, for whom falls prevention efforts would be beneficial. Unlike chronic medi- cal 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 clini- cally 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 prospec- tive 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 con- sumers, 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 con- sumers 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 24/7 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 cap- tures the essence of the Bold Goal. Objectively measured traditional health insurance

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 81

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 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 diabe- tes, UHDs are lower if they obtained an LDL screening (-4.3 UHDs) or a diabetic eye exam (-2.3 UHDs). Likewise, if they have controlled blood sugar levels mea- sured by HbA1C (-1.8 UHDs) or LDL levels (-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) dia- betes (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 80/20 rule generally applies in healthcare; that is, roughly 20 percent of consum- ers 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 con- ditions 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

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82 Part I • Introduction to Analytics and AI

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 man- agement, 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 compil- ing clinical profiles and predicting future health risk. Designed to address the unique chal- lenges 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 min- ing 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 commu- nications from Humana. In the first quarter 2013 earnings release presentation to inves- tors, 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, cus- tomers, 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 ap- plications in multiple domains, but retail analytics is one of its key focus areas.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 83

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 il- lustrates 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 de- rived from fielding such analytics. Some examples are discussed next.

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 com- bining 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 experi- ence 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 observ- ing 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 de- rive 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 informa- tion to plan the workforce allocation for those areas for peak periods.

Retail Value Chain Critical needs at every touch point of the Retail Value Chain

• Shelf-space optimization • Location analysis • Shelf and floor planning • Promotions and markdown optimization

• Supply chain management • Inventory cost optimization • Inventory shortage and excess management • Less unwanted costs

• Targeted promotions • Customized inventory • Promotions and price optimization • Customized shopping experience

• On-time product availability at low costs • Order fulfillment and clubbing • Reduced transportation costs

• Trend analysis • Category management • Predicting trigger events for sales • Better forecasts of demand

• Deliver seamless customer experience • Understand relative performance of channels • Optimize marketing strategies

Vendors Customers Planning Merchandizing Buying

Warehouse & Logistics

Multichannel Operations

• Building retention and satisfaction • Understanding the needs of the customer better • Serving high LTV customers better

FIGURE 1.15 Example of Analytics Applications in a Retail Value Chain. Source: Contributed by Abhishek Rathi,

CEO, vCreaTek.com.

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84 Part I • Introduction to Analytics and AI

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?

3. Should I create a bundle from the same category or a different category line?

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. 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.

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.

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.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 85

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 cur- rent 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 capabili- ties, 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 pat- terns, 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 regard- ing 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 informa- tion 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.

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• 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 govern- ments’ 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.

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 com- mon 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 meth- ods to analyze these images and estimate the per- centage of ground covered by vegetation are being developed. One such method and an app to make it practical through a mobile phone has been devel- oped at Oklahoma State University by researchers in the Department of Plant and Soil Sciences in part- nership 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 ana- lyze 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 device, start walk- ing 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)

Application Case 1.7 Image Analysis Helps Estimate Plant Cover

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 87

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.

u 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 life- styles (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 iden- tified 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?

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 can- opy 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 oth- ers 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 turf- grass 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 applica- tion area for deep learning as well as many other AI techniques. Chapter 9 includes several exam- ples 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.

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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 sev- eral 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 corpora- tions 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 billion to the Chinese economy by 2030 (Metz, 2018). China’s Badu Company today employs over 5,000 AI en- gineers. The Chinese government facilitates research and applications as a national top priority. The accounting firm PricewaterhouseCoopers calculated that AI will add $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 tech- nologies and applications. So, it may be beneficial to look at some of the characteris- tics 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).

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 89

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 (under- standing 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, philoso- phy, 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 protec- tion, 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.)

In Application Case 1.8, we describe how several of these technologies are com- bined in improving security and in expediting the processing of passengers in airports.

Major AI Technologies

Knowledge-Based Technologies

Biometric-Based Technologies

Support Theories, Tools, Platforms,

Mechanisms

AI Applications

FIGURE 1.16 The Landscape (Ecosystem) of AI. Source: Drawn by E. Turban.

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90 Part I • Introduction to Analytics and AI

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 ad- ditional 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

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 mini- mize these possibilities.

1. Facial recognition at airports. Jet Blue is ex- perimenting with facial-recognition technology (a kind of machine vision to match travelers’ faces against prestored photos, such as pass- port, 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 technol- ogies for self-checking of bags. Similar tech- nology 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, us- ing facial recognition for verifying the identity of passengers. The idea is to eliminate board- ing 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 technolo-

gies 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 re- searchers compared the answers to routine questions.

The machine then will flag suspects for fur- ther 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.

Application Case 1.8 AI Increases Passengers’ Comfort and Security in Airports and Borders

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(which is usually based on some AI technology) is not too intelligent. But, it is get- ting “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 ap- plications, 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 aug- mentation). 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.

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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 symp- toms 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 cogni- tive 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 recommenda- tions 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 can- not 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 pilot- ing 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.

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IMPACT ON AGRICULTURE A major impact of AI will be on agriculture. One major an- ticipated 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 per- cent 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 activi- ties 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 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 sys- tems provide a major contribution to our health and medical care. New innovations ar- rive 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 pro-

vided (e.g., speech gesture or inflection). • Many portals provide medical information to patients and even surgeons. Adoptive

spine IT is an example.

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• 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 doc- tors with intelligent systems.

Note: AI in medicine is recognized as a scientific field with national and interna- tional annual conferences. For a comprehensive book on the subject, see Agah (2017).

OTHER SOCIETAL APPLICATIONS There are many AI applications in transportation, utili- ties, 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 in- cluding fake news. Finally, how about using technology to eradicate child slavery in the Middle East? See Application Case 1.9.

In several Middle Eastern countries, notably Jordan, Abu Dhabi, and other Gulf nations, rac- ing camels has been a popular activity for genera- tions. The owners of the winning camels can earn a huge bonus (up to $1,000,000 for first place). The events are also 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 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, etc. and trained as child jockeys. This practice was used for generations until it was banned in all Middle Eastern countries between 2005 and 2010. A major factor that resulted in the banning was the utilization of robots.

The Robots’ Solution

Racing camels was a tradition for many genera- tions and had become a lucrative sport, so no one wanted to abolish it entirely. 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 spur the animals to run faster, much like human jockeys do. Note that camels do not run unless they hear the voice of a human or see  something that looks like a human on their humps.

The Technology

A video camera shows people driving in cars alongside the camels what is going on in real time. The owner can provide voice commands to the camel from the car and control the mechanical whip attached to the hump of the camel.

The Results

The results are astonishing. Not only was the use of child slavery eliminated, but the speed of the camels also 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.). You may have

Application Case 1.9 Robots Took the Job of Camel-Racing Jockeys for Societal Benefits

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u 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 solv- ing complex problems. In this section, we discuss the convergence of these tech- niques 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, man- agement science and other computational tools to describe situations (descriptive ana- lytics), 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

a chance to see the royal family when you go to the track. Finally, you can see more details at youtube. com/watch?v=C1uYAXJIbYg (8:08 min.).

Sources: Compiled from C. Chung. (2016, April 4). “Dubai Camel Race Ride-Along.” YouTube.com. youtube.com/watch?v=xFCRhk4 GYds (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.

Questions for Case 1.9

1. It is said that the robots eradicated the use of child slavery. Explain.

2. Why do the owners need to drive alongside their camels while they are racing?

3. Why not duplicate the technology for horse racing?

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

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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 prescrip- tive 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 ana- lytics 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 pro- cess data. However, today there are technologies and methods that enable capturing,

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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 com- pany are analyzed in seconds by the system.

• Expediting the execution of underwriting policies in property and casualty insur- ance 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 con- vergence 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.

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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 solu- tions to complex problems. Such a convergence should include the IoT. Kranz also sees 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).

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 fu- ture 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.).

Sources: Condensed forC. 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 1.10

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 con- venience? Discuss.

Application Case 1.10 Amazon Go Is Open for Business

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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 perfor- mance 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 conver- gence. Two examples follow.

IBM IBM is combining two of its platforms to support the convergence of AI and ana- lytics. 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 en- ables 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 combi- nation 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 analyt- ics, 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., ma- chine learning) and extract insights and actionable recommendations, which are de- livered 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/.

u 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?

1.9 OVERVIEW OF THE ANALYTICS ECOSYSTEM

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 in- dustry, provide a classification of different types of industry participants, and illustrate the types of opportunities that exist for analytics professionals. Eleven different types of play- ers 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

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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.

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 wid- est 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.

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 clus- ters 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 provid- ers. 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 accelera- tors. 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.

The metaphor of a flower is well suited for the analytics ecosystem as multiple com- ponents 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.

Data Generation

Infrastructure Providers

Analytics- Focused Software

Developers

Data Service Providers

Middleware Providers

Data Warehouse Providers

Analytics User

Organization

Regulators and Policy Makers

Data Management Infrastructure

Providers

Academic Institutions and

Certification Agencies

Analytics Industry

Analysts and InfluencersApplication

Developers: Industry Specific

or General

FIGURE 1.17 Analytics Ecosystem.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 101

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 excur- sion into these topics along with some deep hands-on experience in some of the techni- cal topics. Figure 1.18 presents a plan on the rest of the book.

Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support (11th Edition)

Chapter 1 An Overview of BA, DSS, BI,

DS and AI

Chapter 2 Artificial

Intelligence Concepts, Drivers, Major

Technologies, and Business Applications

Chapter 3 Nature of Data,

Statistical Modeling and Visualization

Chapter 8 Optimization

and Simulation

Chapter 9 Big Data, Data Centers, and

Cloud Computing

Chapter 4 Algorithms rather than Applications in the title

Chapter 5 Machine- Learning

Techniques for Predictive

Analytics

Chapter 7 Text Mining, Sentiment Analysis, and Social Analytics

Chapter 6 Deep Learning and Cognitive Computing

Chapter 10 Robotics;

Industrial and Consumer

Applications

Chapter 11 Group Decision

Making, Collaborative

Systems, and AI Support

Chapter 13 TThe Internet of

Things As A Platform For

Intelligent Applications

Chapter 12 Knowledge Systems:

Expert Systems, Recommenders, Chatbots, Virtual

Personal Assistants, and Robo Advisors

Chapter 14 Implementation

Issues: From Ethics and

Privacy to Organizational and Societal

Impacts

Introduction to Analytics and Al

Predictive Analytics/ Machine Learning

Prescriptive Analytics and Big Data

Robotics, Social Networks, Al and IoT

Caveats of Analytics and Al

FIGURE 1.18 Plan of the Book.

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102 Part I • Introduction to Analytics and AI

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 un- derstanding 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.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 103

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 mate- rial 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 deci- sion making more difficult.

• Businesses must respond and adapt to the chang- ing environment rapidly by making faster and better decisions.

• A model is a simplified representation or abstrac- tion of reality.

• Decision making involves four major phases: in- telligence, design, choice, and implementation.

• In the intelligence phase, the problem (op- portunity) is identified, classified, and decom- posed (if needed), and problem ownership is established.

• In the design phase, a model of the system is built, criteria for selection are agreed on, alterna- tives 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 ex- panding, necessitating the development and use of computerized DSS.

• An early decision support framework divides decision situations into nine categories, de- pending on the degree of structuredness and managerial activities. Each category is sup- ported 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.

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104 Part I • Introduction to Analytics and AI

• The major components of a DSS are a database and its management, a model base and its man- agement, and a user-friendly interface. An intel- ligent (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 analyt- ics 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 in- teractive reporting that provides insights, trends, and patterns in the transactional data.

• Predictive analytics enables organizations to es- tablish 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 tech- niques based on the principles of OR and man- agement 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 analyt- ics. 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

online analytical processing (OLAP)

online transaction processing (OLTP)

predictive analytics prescriptive analytics

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 deci- sion (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 deci- sion 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 prescrip-

tive and descriptive analytics. Use examples. 10. Discuss the major issues in implementing BI.

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Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 105

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 summa- rize 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 follow- ing 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. Big data are collected across a range of sectors, from

public finance to industrial and commercial activities. Some, like climate and transport data, can be particu- larly useful in disaster management and maintenance of the infrastructure essential for business and trade. Others, like health and agricultural data, are of more general interest. Most depositories of such data have been made available to the public and are accessible through Internet. One of them is Big Data Europe (https://www.big-data-europe.eu/). Access the resource and then answer the following questions: a. What are the main links on the landing pages? b. Access the “Food & Agriculture” section. Which kind

of information can be retrieved, and how? c. Go to  the section labelled “Platform.” What kind of

tools are used there? d. Go to the “Health” section. List the contents of the

repository. e. Extreme weather is rapidly becoming a global prob-

lem for businesses. Go to the section related to weather data and discuss the resources.

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 solu- tion, and the reported results. You can also find associ- ated 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 datascience- central.com and tdwi.org to locate at least three similar job titles that you may find interesting for your career.

14. Even the entertainment sector uses Big Data. For instance, the Million Song Dataset (http://million- songdataset.com/) includes 28 datasets containing audio features and metadata for about a million con- temporary popular music tracks. Visit this freely avail- able resource and discuss its intended aims.

15. Visit the Million Song Dataset (http://millionsongda- taset.com/), search the collection, and download a few titles. How are the songs indexed, and how would you download the entire database?

16. Go to the section “Big Data Repository” on Science Direct ( https://www.sciencedirect.com/topics/computer-sci- ence/big-data-repository) and list the articles that relate specifically to cloud computing as discussed in this chapter.

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106 Part I • Introduction to Analytics and AI

References

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)

ht tps : / /www.wor ldbank.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 Univer- sity 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 Market- ing 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 Intelli- gence.” 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 Unit- ed States. Poster presentation at the American Diabe- tes Association Seventy- Fifth Annual Scientific Sessions. Boston, MA.

Chongwatpol, J., & R. Sharda. (2010, December). “SNAP: A DSS to Analyze Network Service Pricing for State Net- works.” 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, Wash- ington, 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/con- tent/dam/SAS/en_us/doc/whitepaper2/iia-analytics- in-sports-106993.pdf (accessed September 2018).

Davenport, T. H., & Patil, D. J. (2012). “Data Scientist.” Har- vard 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 Organiza- tion’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/Con- tent/pdf/siemens-case-study.pdf (accessed September 2018).

Eckerson, W. (2003). Smart Companies in the 21st Century: The Secrets of Creating Successful Business Intelligent Solu- tions. 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/col- lateral/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).

M01_SHAR1552_11_GE_C01.indd 106 10/01/20 8:58 PM

Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence 107

FinTech Futures. (2017, October 11). “IBM Combining Data Sci- ence 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 Re- view of Accuracy of Screening Instruments for Predicting Fall Risk Among Independently Living Older Adults. Jour- nal of Rehabilitation Research and Development, 45(8), pp. 1105–1116.

Gill, T. M., Murphy, T. E., Gahbauer, E. A., et al. (2013). “Associ- ation of Injurious Falls with Disability Outcomes and Nurs- ing 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 Be- tween Health-Related Quality of Life and Health Condi- tions, 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 Trans- forming 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 Sci- ence: 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 Sep- tember 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. venture- beat.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 Sup- port 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 Artifi- cial 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 Augment- ed Intelligence: A Catalyst for AI in the Enterprise.” Forbes.

Pajouh Foad, M., Xing, D., Hariharan, S., Zhou, Y., Balasunda- ram, 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). “Chang- ing 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 Illus- trative 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 Knowl- edge. John Wiley, Hoboken, NJ

M01_SHAR1552_11_GE_C01.indd 107 10/01/20 8:58 PM

108 Part I • Introduction to Analytics and AI

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.” Ge- neris. 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). teradatauniver- sity 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 Deci- sion 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.

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