BI, Analytics and Decision Support - Assignment

153

LEARNING OBJECTIVES

Nature of Data, Statistical Modeling, and Visualization

■■ Understand the nature of data as they relate to business intelligence (BI) and analytics

■■ Learn the methods used to make real-world data analytics ready

■■ Describe statistical modeling and its relationship to business analytics

■■ Learn about descriptive and inferential statistics ■■ Define business reporting and understand its historical evolution

■■ Understand the importance of data/information visualization

■■ Learn different types of visualization techniques ■■ Appreciate the value that visual analytics brings to business analytics

■■ Know the capabilities and limitations of dashboards

I n the age of Big Data and business analytics in which we are living, the importance of data is undeniable. Newly coined phrases such as “data are the oil,” “data are the new bacon,” “data are the new currency,” and “data are the king” are further stress-

ing the renewed importance of data. But the type of data we are talking about is obvi- ously not just any data. The “garbage in garbage out—GIGO” concept/principle applies to today’s Big Data phenomenon more so than any data definition that we have had in the past. To live up to their promise, value proposition, and ability to turn into insight, data have to be carefully created/identified, collected, integrated, cleaned, transformed, and properly contextualized for use in accurate and timely decision making.

Data are the main theme of this chapter. Accordingly, the chapter starts with a de- scription of the nature of data: what they are, what different types and forms they can come in, and how they can be preprocessed and made ready for analytics. The first few sections of the chapter are dedicated to a deep yet necessary understanding and process- ing of data. The next few sections describe the statistical methods used to prepare data as input to produce both descriptive and inferential measures. Following the statistics sections are sections on reporting and visualization. A report is a communication artifact

3 C H A P T E R

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prepared with the specific intention of converting data into information and knowledge and relaying that information in an easily understandable/digestible format. Today, these reports are visually oriented, often using colors and graphical icons that collectively look like a dashboard to enhance the information content. Therefore, the latter part of the chapter is dedicated to subsections that present the design, implementation, and best practices regarding information visualization, storytelling, and information dashboards.

This chapter has the following sections:

3.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing 154

3.2 Nature of Data 157 3.3 Simple Taxonomy of Data 161 3.4 Art and Science of Data Preprocessing 165 3.5 Statistical Modeling for Business Analytics 175 3.6 Regression Modeling for Inferential Statistics 187 3.7 Business Reporting 199 3.8 Data Visualization 202 3.9 Different Types of Charts and Graphs 207

3.10 Emergence of Visual Analytics 212 3.11 Information Dashboards 218

3.1 OPENING VIGNETTE: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing

SiriusXM Radio is a satellite radio powerhouse, the largest radio company in the world with $3.8 billion in annual revenues and a wide range of hugely popular music, sports, news, talk, and entertainment stations. The company, which began broadcasting in 2001 with 50,000 subscribers, had 18.8 million subscribers in 2009, and today has nearly 29 million.

Much of SiriusXM’s growth to date is rooted in creative arrangements with automo- bile manufacturers; today, nearly 70 percent of new cars are SiriusXM enabled. Yet the company’s reach extends far beyond car radios in the United States to a worldwide pres- ence on the Internet, on smartphones, and through other services and distribution chan- nels, including SONOS, JetBlue, and Dish.

BUSINESS CHALLENGE

Despite these remarkable successes, changes in customer demographics, technology, and a competitive landscape over the past few years have posed a new series of business challenges and opportunities for SiriusXM. Here are some notable ones:

• As its market penetration among new cars increased, the demographics of its buy- ers changed, skewing toward younger people with less discretionary income. How could SiriusXM reach this new demographic?

• As new cars become used cars and change hands, how could SiriusXM identify, engage, and convert second owners to paying customers?

• With its acquisition of the connected vehicle business from Agero—the leading pro- vider of telematics in the U.S. car market—SiriusXM gained the ability to deliver its service via satellite and wireless networks. How could it successfully use this acqui- sition to capture new revenue streams?

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 155

PROPOSED SOLUTION: SHIFTING THE VISION TOWARD DATA-DRIVEN MARKETING

SiriusXM recognized that to address these challenges, it would need to become a high- performance, data-driven marketing organization. The company began making that shift by establishing three fundamental tenets. First, personalized interactions—not mass marketing—would rule the day. The company quickly understood that to conduct more personalized marketing, it would have to draw on past history and interactions as well as on a keen understanding of the consumer’s place in the subscription life cycle.

Second, to gain that understanding, information technology (IT) and its external tech- nology partners would need the ability to deliver integrated data, advanced analytics, integrated marketing platforms, and multichannel delivery systems.

And third, the company could not achieve its business goals without an integrated and consistent point of view across the company. Most important, the technology and business sides of SiriusXM would have to become true partners to best address the chal- lenges involved in becoming a high-performance marketing organization that draws on data-driven insights to speak directly with consumers in strikingly relevant ways.

Those data-driven insights, for example, would enable the company to differentiate between consumers, owners, drivers, listeners, and account holders. The insights would help SiriusXM to understand what other vehicles and services are part of each household and cre- ate new opportunities for engagement. In addition, by constructing a coherent and reliable 360-degree view of all its consumers, SiriusXM could ensure that all messaging in all cam- paigns and interactions would be tailored, relevant, and consistent across all channels. The important bonus is that a more tailored and effective marketing is typically more cost-efficient.

IMPLEMENTATION: CREATING AND FOLLOWING THE PATH TO HIGH-PERFORMANCE MARKETING

At the time of its decision to become a high-performance marketing company, SiriusXM was working with a third-party marketing platform that did not have the capacity to support SiriusXM’s ambitions. The company then made an important, forward-thinking decision to bring its marketing capabilities in-house—and then carefully plotted what it would need to do to make the transition successfully.

1. Improve data cleanliness through improved master data management and governance. Although the company was understandably impatient to put ideas into action, data hygiene was a necessary first step to create a reliable window into consumer behavior.

2. Bring marketing analytics in-house and expand the data warehouse to enable scale and fully support integrated marketing analytics.

3. Develop new segmentation and scoring models to run in databases, eliminating la- tency and data duplication.

4. Extend the integrated data warehouse to include marketing data and scoring, lever- aging in-database analytics.

5. Adopt a marketing platform for campaign development. 6. Bring all of its capability together to deliver real-time offer management across all

marketing channels: call center, mobile, Web, and in-app.

Completing those steps meant finding the right technology partner. SiriusXM chose Teradata because its strengths were a powerful match for the project and company. Teradata offered the ability to:

• Consolidate data sources with an integrated data warehouse (IDW), advanced ana- lytics, and powerful marketing applications.

• Solve data-latency issues.

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156 Part I • Introduction to Analytics and AI

• Significantly reduce data movement across multiple databases and applications. • Seamlessly interact with applications and modules for all marketing areas. • Scale and perform at very high levels for running campaigns and analytics in-database. • Conduct real-time communications with customers. • Provide operational support, either via the cloud or on premise.

This partnership has enabled SiriusXM to move smoothly and swiftly along its road map, and the company is now in the midst of a transformational, five-year process. After establishing its strong data governance process, SiriusXM began by implementing its IDW, which allowed the company to quickly and reliably operationalize insights through- out the organization.

Next, the company implemented Customer Interaction Manager—part of the Teradata Integrated Marketing Cloud, which enables real-time, dialog-based customer interaction across the full spectrum of digital and traditional communication channels. SiriusXM also will incorporate the Teradata Digital Messaging Center.

Together, the suite of capabilities allows SiriusXM to handle direct communications across multiple channels. This evolution will enable real-time offers, marketing messages, and recommendations based on previous behavior.

In addition to streamlining the way it executes and optimizes outbound marketing activities, SiriusXM is also taking control of its internal marketing operations with the implementation of Marketing Resource Management, also part of the Teradata Integrated Marketing Cloud. The solution will allow SiriusXM to streamline workflow, optimize mar- keting resources, and drive efficiency through every penny of its marketing budget.

RESULTS: REAPING THE BENEFITS

As SiriusXM continues its evolution into a high-performance marketing organization, it already is benefiting from its thoughtfully executed strategy. Household-level consumer insights and a complete view of marketing touch strategy with each consumer enable SiriusXM to create more targeted offers at the household, consumer, and device levels. By bringing the data and marketing analytics capabilities in-house, SiriusXM achieved the following:

• Campaign results in near real time rather than four days, resulting in massive reduc- tions in cycle times for campaigns and the analysts who support them.

• Closed-loop visibility, allowing the analysts to support multistage dialogs and in-campaign modifications to increase campaign effectiveness.

• Real-time modeling and scoring to increase marketing intelligence and sharpen cam- paign offers and responses at the speed of their business.

Finally, SiriusXM’s experience has reinforced the idea that high-performance market- ing is a constantly evolving concept. The company has implemented both processes and the technology that give it the capacity for continued and flexible growth.

u QUESTIONS FOR THE OPENING VIGNETTE

1. What does SiriusXM do? In what type of market does it conduct its business?

2. What were its challenges? Comment on both technology and data-related challenges.

3. What were the proposed solutions?

4. How did the company implement the proposed solutions? Did it face any implementation challenges?

5. What were the results and benefits? Were they worth the effort/investment?

6. Can you think of other companies facing similar challenges that can potentially benefit from similar data-driven marketing solutions?

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 157

WHAT WE CAN LEARN FROM THIS VIGNETTE

Striving to thrive in a fast-changing competitive industry, SiriusXM realized the need for a new and improved marketing infrastructure (one that relies on data and analytics) to effectively communicate its value proposition to its existing and potential custom- ers. As is the case in any industry, success or mere survival in entertainment depends on intelligently sensing the changing trends (likes and dislikes) and putting together the right messages and policies to win new customers while retaining the existing ones. The key is to create and manage successful marketing campaigns that resonate with the target population of customers and have a close feedback loop to adjust and modify the message to optimize the outcome. At the end, it was all about the preci- sion in the way that SiriusXM conducted business: being proactive about the changing nature of the clientele and creating and transmitting the right products and services in a timely manner using a fact-based/data-driven holistic marketing strategy. Source identification, source creation, access and collection, integration, cleaning, transforma- tion, storage, and processing of relevant data played a critical role in SiriusXM’s suc- cess in designing and implementing a marketing analytics strategy as is the case in any analytically savvy successful company today, regardless of the industry in which they are participating.

Sources: C. Quinn, “Data-Driven Marketing at SiriusXM,” Teradata Articles & News, 2016. http://bigdata. teradata.com/US/Articles-News/Data-Driven-Marketing-At-SiriusXM/ (accessed August 2016); “SiriusXM Attracts and Engages a New Generation of Radio Consumers.” http://assets.teradata.com/resourceCenter/ downloads/CaseStudies/EB8597.pdf?processed=1.

3.2 NATURE OF DATA

Data are the main ingredient for any BI, data science, and business analytics initiative. In fact, they can be viewed as the raw material for what popular decision technolo- gies produce—information, insight, and knowledge. Without data, none of these tech- nologies could exist and be popularized—although traditionally we have built analytics models using expert knowledge and experience coupled with very little or no data at all; however, those were the old days, and now data are of the essence. Once perceived as a big challenge to collect, store, and manage, data today are widely considered among the most valuable assets of an organization with the potential to create invaluable insight to better understand customers, competitors, and the business processes.

Data can be small or very large. They can be structured (nicely organized for computers to process), or they can be unstructured (e.g., text that is created for humans and hence not readily understandable/consumable by computers). Data can come in small batches continuously or can pour in all at once as a large batch. These are some of the characteristics that define the inherent nature of today’s data, which we often call Big Data. Even though these characteristics of data make them more challenging to process and consume, they also make the data more valuable because the character- istics enrich them beyond their conventional limits, allowing for the discovery of new and novel knowledge. Traditional ways to manually collect data (via either surveys or human-entered business transactions) mostly left their places to modern-day data collection mechanisms that use Internet and/or sensor/radio frequency identification (RFID)–based computerized networks. These automated data collection systems are not only enabling us to collect more volumes of data but also enhancing the data quality and integrity. Figure 3.1 illustrates a typical analytics continuum—data to analytics to actionable information.

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158 Part I • Introduction to Analytics and AI

Although their value proposition is undeniable, to live up their promise, data must comply with some basic usability and quality metrics. Not all data are useful for all tasks, obviously. That is, data must match with (have the coverage of the specifics for) the task for which they are intended to be used. Even for a specific task, the relevant data on hand need to comply with the quality and quantity requirements. Essentially, data have to be analytics ready. So what does it mean to make data analytics ready? In addition to its relevancy to the problem at hand and the quality/quantity requirements, it also has to have a certain structure in place with key fields/variables with properly normalized val- ues. Furthermore, there must be an organization-wide agreed-on definition for common variables and subject matters (sometimes also called master data management), such as how to define a customer (what characteristics of customers are used to produce a holis- tic enough representation to analytics) and where in the business process the customer- related information is captured, validated, stored, and updated.

Sometimes the representation of the data depends on the type of analytics being employed. Predictive algorithms generally require a flat file with a target variable, so mak- ing data analytics ready for prediction means that data sets must be transformed into a flat-file format and made ready for ingestion into those predictive algorithms. It is also imperative to match the data to the needs and wants of a specific predictive algorithm and/or a software tool. For instance, neural network algorithms require all input variables

UOB 1.0

X

UOB 2.2

UOB 2.1

UOB 3.0

ERP CRM SCM

Business Process

Facebook

Google+

Linked In

YouTube

Twitter

Tumblr Flicker

Instagram Pinterest

Snapchat

Reddit Foursquare

Internet/Social Media

Machines/Internet of Things

Data Storage Analytics

Data Protection

Cloud Storage and Computing

Patt ern

s

Trends

Knowledge

Applications

End Users

Validate

Built

Test

X

FIGURE 3.1 A Data to Knowledge Continuum.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 159

to be numerically represented (even the nominal variables need to be converted into pseudo binary numeric variables), whereas decision tree algorithms do not require such numerical transformation—they can easily and natively handle a mix of nominal and nu- meric variables.

Analytics projects that overlook data-related tasks (some of the most critical steps) often end up with the wrong answer for the right problem, and these unintentionally cre- ated, seemingly good answers could lead to inaccurate and untimely decisions. Following are some of the characteristics (metrics) that define the readiness level of data for an ana- lytics study (Delen, 2015; Kock, McQueen, & Corner, 1997).

• Data source reliability. This term refers to the originality and appropriateness of the storage medium where the data are obtained—answering the question of “Do we have the right confidence and belief in this data source?” If at all possible, one should always look for the original source/creator of the data to eliminate/mitigate the possibilities of data misrepresentation and data transformation caused by the mishandling of the data as they moved from the source to destination through one or more steps and stops along the way. Every move of the data creates a chance to unintentionally drop or reformat data items, which limits the integrity and perhaps true accuracy of the data set.

• Data content accuracy. This means that data are correct and are a good match for the analytics problem—answering the question of “Do we have the right data for the job?” The data should represent what was intended or defined by the original source of the data. For example, the customer’s contact information recorded within a database should be the same as what the customer said it was. Data accuracy will be covered in more detail in the following subsection.

• Data accessibility. This term means that the data are easily and readily obtainable— answering the question of “Can we easily get to the data when we need to?” Access to data can be tricky, especially if they are stored in more than one location and storage medium and need to be merged/transformed while accessing and obtaining them. As the traditional relational database management systems leave their place (or coexist with a new generation of data storage mediums such as data lakes and Hadoop infra- structure), the importance/criticality of data accessibility is also increasing.

• Data security and data privacy. Data security means that the data are secured to allow only those people who have the authority and the need to access them and to prevent anyone else from reaching them. Increasing popularity in educational degrees and certificate programs for Information Assurance is evidence of the criti- cality and the increasing urgency of this data quality metric. Any organization that maintains health records for individual patients must have systems in place that not only safeguard the data from unauthorized access (which is mandated by federal laws such as the Health Insurance Portability and Accountability Act [HIPAA]) but also accurately identify each patient to allow proper and timely access to records by authorized users (Annas, 2003).

• Data richness. This means that all required data elements are included in the data set. In essence, richness (or comprehensiveness) means that the available variables portray a rich enough dimensionality of the underlying subject matter for an accurate and worthy analytics study. It also means that the information content is complete (or near complete) to build a predictive and/or prescriptive analytics model.

• Data consistency. This means that the data are accurately collected and com- bined/merged. Consistent data represent the dimensional information (variables of interest) coming from potentially disparate sources but pertaining to the same sub- ject. If the data integration/merging is not done properly, some of the variables of different subjects could appear in the same record—having two different patient

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160 Part I • Introduction to Analytics and AI

records mixed up; for instance, this could happen while merging the demographic and clinical test result data records.

• Data currency/data timeliness. This means that the data should be up-to-date (or as recent/new as they need to be) for a given analytics model. It also means that the data are recorded at or near the time of the event or observation so that the time delay–related misrepresentation (incorrectly remembering and encoding) of the data is prevented. Because accurate analytics relies on accurate and timely data, an essential characteristic of analytics-ready data is the timeliness of the creation and access to data elements.

• Data granularity. This requires that the variables and data values be defined at the lowest (or as low as required) level of detail for the intended use of the data. If the data are aggregated, they might not contain the level of detail needed for an analytics algorithm to learn how to discern different records/cases from one another. For example, in a medical setting, numerical values for laboratory results should be recorded to the appropriate decimal place as required for the meaning- ful interpretation of test results and proper use of those values within an analytics algorithm. Similarly, in the collection of demographic data, data elements should be defined at a granular level to determine the differences in outcomes of care among various subpopulations. One thing to remember is that the data that are aggregated cannot be disaggregated (without access to the original source), but they can easily be aggregated from its granular representation.

• Data validity. This is the term used to describe a match/mismatch between the actual and expected data values of a given variable. As part of data definition, the acceptable values or value ranges for each data element must be defined. For example, a valid data definition related to gender would include three values: male, female, and unknown.

• Data relevancy. This means that the variables in the data set are all relevant to the study being conducted. Relevancy is not a dichotomous measure (whether a variable is relevant or not); rather, it has a spectrum of relevancy from least relevant to most relevant. Based on the analytics algorithms being used, one can choose to include only the most relevant information (i.e., variables) or, if the algorithm is capable enough to sort them out, can choose to include all the relevant ones regard- less of their levels. One thing that analytics studies should avoid is including totally irrelevant data into the model building because this could contaminate the informa- tion for the algorithm, resulting in inaccurate and misleading results.

The above-listed characteristics are perhaps the most prevailing metrics to keep up with; the true data quality and excellent analytics readiness for a specific application do- main would require different levels of emphasis to be placed on these metric dimensions and perhaps add more specific ones to this collection. The following section will delve into the nature of data from a taxonomical perspective to list and define different data types as they relate to different analytics projects.

u SECTION 3.2 REVIEW QUESTIONS

1. How do you describe the importance of data in analytics? Can we think of analytics without data?

2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?

3. Where do the data for business analytics come from?

4. In your opinion, what are the top three data-related challenges for better analytics?

5. What are the most common metrics that make for analytics-ready data?

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 161

3.3 SIMPLE TAXONOMY OF DATA

The term data (datum in singular form) refers to a collection of facts usually obtained as the result of experiments, observations, transactions, or experiences. Data can consist of numbers, letters, words, images, voice recordings, and so on, as measurements of a set of variables (characteristics of the subject or event that we are interested in studying). Data are often viewed as the lowest level of abstraction from which information and then knowledge is derived.

At the highest level of abstraction, one can classify data as structured and unstruc- tured (or semistructured). Unstructured data/semistructured data are composed of any combination of textual, imagery, voice, and Web content. Unstructured/semistruc- tured data will be covered in more detail in the text mining and Web mining chapter. Structured data are what data mining algorithms use and can be classified as categori- cal or numeric. The categorical data can be subdivided into nominal or ordinal data, whereas numeric data can be subdivided into intervals or ratios. Figure 3.2 shows a simple data taxonomy.

• Categorical data. These represent the labels of multiple classes used to divide a variable into specific groups. Examples of categorical variables include race, sex, age group, and educational level. Although the latter two variables can also be considered in a numerical manner by using exact values for age and highest grade completed, for example, it is often more informative to categorize such variables into a relatively small number of ordered classes. The categorical data can also be called discrete data, implying that they represent a finite number of values with no continuum between them. Even if the values used for the categorical (or discrete) variables are numeric, these numbers are nothing more than symbols and do not imply the possibility of calculating fractional values.

• Nominal data. These contain measurements of simple codes assigned to objects as labels, which are not measurements. For example, the variable marital status can be generally categorized as (1) single, (2) married, and (3) divorced. Nominal

Data in Analytics

Structured Data Unstructured or Semi-Structured Data

Nominal

Ordinal

Textual

Multimedia

XML/JSON

Categorical Numerical

Interval

Ratio

Image

Audio

Video

FIGURE 3.2 A Simple Taxonomy of Data.

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162 Part I • Introduction to Analytics and AI

data can be represented with binomial values having two possible values (e.g., yes/no, true/false, good/bad) or multinomial values having three or more pos- sible values (e.g., brown/green/blue, white/black/Latino/Asian, single/married/ divorced).

• Ordinal data. These contain codes assigned to objects or events as labels that also represent the rank order among them. For example, the variable credit score can be generally categorized as (1) low, (2) medium, or (3) high. Similar ordered relationships can be seen in variables such as age group (i.e., child, young, middle-aged, elderly) and educational level (i.e., high school, college, graduate school). Some predictive analytic algorithms, such as ordinal multiple logistic regression, take into account this additional rank-order information to build a better classification model.

• Numeric data. These represent the numeric values of specific variables. Examples of numerically valued variables include age, number of children, total household income (in U.S. dollars), travel distance (in miles), and temperature (in Fahrenheit degrees). Numeric values representing a variable can be integers (only whole numbers) or real (also fractional numbers). The numeric data can also be called continuous data, implying that the variable contains continuous measures on a specific scale that allows insertion of interim values. Unlike a discrete vari- able, which represents finite, countable data, a continuous variable represents scal- able measurements, and it is possible for the data to contain an infinite number of fractional values.

• Interval data. These are variables that can be measured on interval scales. A common example of interval scale measurement is temperature on the Celsius scale. In this particular scale, the unit of measurement is 1/100 of the difference between the melting temperature and the boiling temperature of water in atmospheric pres- sure; that is, there is not an absolute zero value.

• Ratio data. These include measurement variables commonly found in the physical sciences and engineering. Mass, length, time, plane angle, energy, and electric charge are examples of physical measures that are ratio scales. The scale type takes its name from the fact that measurement is the estimation of the ratio between a magnitude of a continuous quantity and a unit magnitude of the same kind. Informally, the dis- tinguishing feature of a ratio scale is the possession of a nonarbitrary zero value. For example, the Kelvin temperature scale has a nonarbitrary zero point of absolute zero, which is equal to –273.15 degrees Celsius. This zero point is nonarbitrary because the particles that comprise matter at this temperature have zero kinetic energy.

Other data types, including textual, spatial, imagery, video, and voice, need to be converted into some form of categorical or numeric representation before they can be pro- cessed by analytics methods (data mining algorithms; Delen, 2015). Data can also be classi- fied as static or dynamic (i.e., temporal or time series).

Some predictive analytics (i.e., data mining) methods and machine-learning algorithms are very selective about the type of data that they can handle. Providing them with incompatible data types can lead to incorrect models or (more often) halt the model development process. For example, some data mining methods need all the variables (both input and output) represented as numerically valued variables (e.g., neural net- works, support vector machines, logistic regression). The nominal or ordinal variables are converted into numeric representations using some type of 1-of-N pseudo variables (e.g., a categorical variable with three unique values can be transformed into three pseudo variables with binary values—1 or 0). Because this process could increase the number of variables, one should be cautious about the effect of such representations, especially for the categorical variables that have large numbers of unique values.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 163

Similarly, some predictive analytics methods, such as ID3 (a classic decision tree algorithm) and rough sets (a relatively new rule induction algorithm), need all the vari- ables represented as categorically valued variables. Early versions of these methods re- quired the user to discretize numeric variables into categorical representations before they could be processed by the algorithm. The good news is that most implementa- tions of these algorithms in widely available software tools accept a mix of numeric and nominal variables and internally make the necessary conversions before process- ing the data.

Data come in many different variable types and representation schemas. Business analytics tools are continuously improving in their ability to help data scientists in the daunting task of data transformation and data representation so that the data require- ments of specific predictive models and algorithms can be properly executed. Application Case 3.1 illustrates a business scenario in which one of the largest telecommunication companies streamlined and used a wide variety of rich data sources to generate customers insight to prevent churn and to create new revenue sources.

The Problem

In the ultra-competitive telecommunications indus- try, staying relevant to consumers while finding new sources of revenue is critical, especially since cur- rent revenue sources are in decline.

For Fortune 13 powerhouse Verizon, the secret weapon that catapulted the company into the nation’s largest and most reliable network provider is also guiding the business toward future success (see the following figure for some numbers about Verizon). The secret weapon? Data and analytics. Because telecommunication companies are typically rich in data, having the right analytics solution and personnel in place can uncover critical insights that benefit every area of the business.

The Backbone of the Company

Since its inception in 2000, Verizon has partnered with Teradata to create a data and analytics archi- tecture that drives innovation and science-based decision making. The goal is to stay relevant to cus- tomers while also identifying new business oppor- tunities and making adjustments that result in more cost-effective operations.

“With business intelligence, we help the business identify new business opportunities or

make course corrections to operate the business in a more cost-effective way,” said Grace Hwang, executive director of Financial Performance & Analytics, BI, for Verizon. “We support decision makers with the most relevant information to improve the competitive advantage of Verizon.”

By leveraging data and analytics, Verizon is able to offer a reliable network, ensure customer satisfaction, and develop products and services that consumers want to buy.

“Our incubator of new products and services will help bring the future to our customers,” Hwang said. “We’re using our network to make breakthroughs in

Application Case 3.1 Verizon Answers the Call for Innovation: The Nation’s Largest Network Provider Uses Advanced Analytics to Bring the Future to Its Customers

Verizon by the Numbers

The top ranked wireless carrier in the U.S. has:

$131.6B

177K

1,700

112.1M

106.5M

13M

retail locations

retail connections

postpaid customers

TV and Internet subscribers

in revenue

employees

$

(Continued )

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164 Part I • Introduction to Analytics and AI

interactive entertainment, digital media, the Internet of Things, and broadband services.”

Data Insights across Three Business Units

Verizon relies on advanced analytics that are exe- cuted on the Teradata® Unified Data Architecture™ to support its business units. The analytics enable Verizon to deliver on its promise to help customers innovate their lifestyles and provide key insights to support these three areas:

• Identify new revenue sources. Research and development teams use data, analytics, and strategic partnerships to test and develop with the Internet of Things (IoT). The new frontier in data is IoT, which will lead to new revenues that in turn generate opportunities for top-line growth. Smart cars, smart agricul- ture, and smart IoT will all be part of this new growth.

• Predict churn in the core mobile business. Verizon has multiple use cases that demonstrate how its advanced analytics enable laser-accurate churn prediction—within a one to two percent margin—in the mobile space. For a $131 billion company, predicting churn with such precision is significant. By recognizing specific patterns in tablet data usage, Verizon can identify which customers most often access their tablets, then engage those who do not.

• Forecast mobile phone plans. Customer behav- ioral analytics allow finance to better predict earnings in fast-changing market conditions. The U.S. wireless industry is moving from monthly payments for both the phone and the service to paying for the phone independently. This opens up a new opportunity for Verizon to gain busi- ness. The analytic environment helps Verizon better predict churn with new plans and forecast the impact of changes to pricing plans.

The analytics deliver what Verizon refers to as “hon- est data” that inform various business units. “Our mission is to be the honest voice and the indepen- dent third-party opinion on the success or oppor- tunities for improvement to the business,” Hwang

explains. “So my unit is viewed as the golden source of information, and we come across with the honest voice, and a lot of the business decisions are through various rungs of course correction.”

Hwang adds that oftentimes, what forces a company to react is competitors affecting change in the marketplace, rather than the company making the wrong decisions. “So we try to guide the business through the best course of correc- tion, wherever applicable, timely, so that we can continue to deliver record-breaking results year after year,” she said. “I have no doubt that the business intelligence had led to such success in the past.”

Disrupt and Innovate

Verizon leverages advanced analytics to optimize marketing by sending the most relevant offers to customers. At the same time, the company relies on analytics to ensure they have the financial acumen to stay number one in the U.S. mobile market. By continuing to disrupt the industry with innovative products and solutions, Verizon is positioned to remain the wireless standard for the industry.

“We need the marketing vision and the sales rigor to produce the most relevant offer to our customers, and then at the same time we need to have the finance rigor to ensure that whatever we offer to the customer is also profitable to the business so that we’re responsible to our share- holders,” Hwang says.

In Summary—Executing the Seven Ps of Modern Marketing

Telecommunications giant Verizon uses seven Ps to drive its modern-day marketing efforts. The Ps, when used in unison, help Verizon penetrate the market in the way it predicted.

1. People: Understanding customers and their needs to create the product.

2. Place: Where customers shop.

3. Product: The item that’s been manufactured and is for sale.

Application Case 3.1 (Continued)

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 165

u SECTION 3.3 REVIEW QUESTIONS

1. What are data? How do data differ from information and knowledge?

2. What are the main categories of data? What types of data can we use for BI and analytics?

3. Can we use the same data representation for all analytics models? Why, or why not?

4. What is a 1-of-N data representation? Why and where is it used in analytics?

3.4 ART AND SCIENCE OF DATA PREPROCESSING

Data in their original form (i.e., the real-world data) are not usually ready to be used in analytics tasks. They are often dirty, misaligned, overly complex, and inaccurate. A te- dious and time-demanding process (so-called data preprocessing) is necessary to con- vert the raw real-world data into a well-refined form for analytics algorithms (Kotsiantis, Kanellopoulos, & Pintelas, 2006). Many analytics professionals would testify that the time spent on data preprocessing (which is perhaps the least enjoyable phase in the whole process) is significantly longer than the time spent on the rest of the analytics tasks (the fun of analytics model building and assessment). Figure 3.3 shows the main steps in the data preprocessing endeavor.

In the first step of data preprocessing, the relevant data are collected from the iden- tified sources, the necessary records and variables are selected (based on an intimate understanding of the data, the unnecessary information is filtered out), and the records coming from multiple data sources are integrated/merged (again, using the intimate un- derstanding of the data, the synonyms and homonyms are able to be handled properly).

In the second step of data preprocessing, the data are cleaned (this step is also known as data scrubbing). Data in their original/raw/real-world form are usually dirty (Hernández & Stolfo, 1998; Kim et al., 2003). In this phase, the values in the data set are identified and dealt with. In some cases, missing values are an anomaly in the data set, in which case they need to be imputed (filled with a most probable value) or ignored; in other cases, the missing values are a natural part of the data set

4. Process: How customers get to the shop or place to buy the product.

5. Pricing: Working with promotions to get cus- tomers’ attention.

6. Promo: Working with pricing to get customers’ attention.

7. Physical evidence: The business intelligence that gives insights.

“The Aster and Hadoop environment allows us to explore things we suspect could be the rea- sons for breakdown in the seven Ps,” says Grace Hwang, executive director of Financial Performance & Analytics, BI, for Verizon. “This goes back to

providing the business value to our decision- makers. With each step in the seven Ps, we ought to be able to tell them where there are opportunities for improvement.”

Questions for Case 3.1

1. What was the challenge Verizon was facing?

2. What was the data-driven solution proposed for Verizon’s business units?

3. What were the results?

Source: Teradata Case Study “Verizon Answers the Call for Innovation” https://www.teradata.com/Resources/Case-Studies/ Verizon-answers-the-call-for-innovation (accessed July 2018).

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166 Part I • Introduction to Analytics and AI

(e.g., the household income field is often left unanswered by people who are in the top income tier). In this step, the analyst should also identify noisy values in the data (i.e., the outliers) and smooth them out. In addition, inconsistencies (unusual values within a variable) in the data should be handled using domain knowledge and/or expert opinion.

In the third step of data preprocessing, the data are transformed for better process- ing. For instance, in many cases, the data are normalized between a certain minimum and maximum for all variables to mitigate the potential bias of one variable having

DW

Well-Formed Data

Social Data

Legacy DBWeb Data

Data Consolidation Collect data Select data Integrate data

Data Cleaning Impute values Reduce noise Eliminate duplicates

Data Transformation Normalize data Discretize data Create attributes

Data Reduction Reduce dimension Reduce volume Balance data

OLTP

Raw Data Sources

F ee

db ac

k

FIGURE 3.3 Data Preprocessing Steps.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 167

large numeric values (such as household income) dominating other variables (such as number of dependents or years in service, which could be more important) having smaller values. Another transformation that takes place is discretization and/or aggrega- tion. In some cases, the numeric variables are converted to categorical values (e.g., low, medium, high); in other cases, a nominal variable’s unique value range is reduced to a smaller set using concept hierarchies (e.g., as opposed to using the individual states with 50 different values, one could choose to use several regions for a variable that shows location) to have a data set that is more amenable to computer processing. Still, in other cases, one might choose to create new variables based on the existing ones to magnify the information found in a collection of variables in the data set. For instance, in an organ transplantation data set, one might choose to use a single variable show- ing the blood-type match (1: match, 0: no match) as opposed to separate multinominal values for the blood type of both the donor and the recipient. Such simplification could increase the information content while reducing the complexity of the relationships in the data.

The final phase of data preprocessing is data reduction. Even though data scientists (i.e., analytics professionals) like to have large data sets, too much data can also be a problem. In the simplest sense, one can visualize the data commonly used in predictive analytics projects as a flat file consisting of two dimensions: variables (the number of columns) and cases/records (the number of rows). In some cases (e.g., image process- ing and genome projects with complex microarray data), the number of variables can be rather large, and the analyst must reduce the number to a manageable size. Because the variables are treated as different dimensions that describe the phenomenon from differ- ent perspectives, in predictive analytics and data mining, this process is commonly called dimensional reduction (or variable selection). Even though there is not a single best way to accomplish this task, one can use the findings from previously published litera- ture; consult domain experts; run appropriate statistical tests (e.g., principal component analysis or independent component analysis); and, more preferably, use a combination of these techniques to successfully reduce the dimensions in the data into a more manage- able and most relevant subset.

With respect to the other dimension (i.e., the number of cases), some data sets can include millions or billions of records. Even though computing power is increasing ex- ponentially, processing such a large number of records cannot be practical or feasible. In such cases, one might need to sample a subset of the data for analysis. The underlying assumption of sampling is that the subset of the data will contain all relevant patterns of the complete data set. In a homogeneous data set, such an assumption could hold well, but real-world data are hardly ever homogeneous. The analyst should be extremely careful in selecting a subset of the data that reflects the essence of the complete data set and is not specific to a subgroup or subcategory. The data are usually sorted on some variable, and taking a section of the data from the top or bottom could lead to a biased data set on specific values of the indexed variable; therefore, always try to randomly select the records on the sample set. For skewed data, straightforward random sampling might not be sufficient, and stratified sampling (a proportional representation of different subgroups in the data is represented in the sample data set) might be required. Speaking of skewed data, it is a good practice to balance the highly skewed data by either oversampling the less represented or undersampling the more represented classes. Research has shown that balanced data sets tend to produce better prediction models than unbalanced ones (Thammasiri et al., 2014).

The essence of data preprocessing is summarized in Table 3.1, which maps the main phases (along with their problem descriptions) to a representative list of tasks and algorithms.

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168 Part I • Introduction to Analytics and AI

TABLE 3.1 A Summary of Data Preprocessing Tasks and Potential Methods

Main Task Subtasks Popular Methods

Data consolidation Access and collect the data Select and filter the data Integrate and unify the data

SQL queries, software agents, Web services. Domain expertise, SQL queries, statistical tests. SQL queries, domain expertise, ontology-driven data mapping.

Data cleaning Handle missing values in the data

Fill in missing values (imputations) with most appropriate val- ues (mean, median, min/max, mode, etc.); recode the missing values with a constant such as “ML”; remove the record of the missing value; do nothing.

Identify and reduce noise in the data

Identify the outliers in data with simple statistical techniques (such as averages and standard deviations) or with cluster analysis; once identified, either remove the outliers or smooth them by using binning, regression, or simple averages.

Find and eliminate erroneous data

Identify the erroneous values in data (other than outliers), such as odd values, inconsistent class labels, odd distributions; once identified, use domain expertise to correct the values or remove the records holding the erroneous values.

Data transformation Normalize the data Reduce the range of values in each numerically valued variable to a standard range (e.g., 0 to 1 or -1 to +1) by using a vari- ety of normalization or scaling techniques.

Discretize or aggregate the data

If needed, convert the numeric variables into discrete represen- tations using range- or frequency-based binning techniques; for categorical variables, reduce the number of values by applying proper concept hierarchies.

Construct new attributes Derive new and more informative variables from the existing ones using a wide range of mathematical functions (as simple as addition and multiplication or as complex as a hybrid combi- nation of log transformations).

Data reduction Reduce number of attributes Use principal component analysis, independent component analysis, chi-square testing, correlation analysis, and decision tree induction.

Reduce number of records Perform random sampling, stratified sampling, expert- knowledge-driven purposeful sampling.

Balance skewed data Oversample the less represented or undersample the more represented classes.

It is almost impossible to underestimate the value proposition of data preprocess- ing. It is one of those time-demanding activities in which investment of time and effort pays off without a perceivable limit for diminishing returns. That is, the more resources you invest in it, the more you will gain at the end. Application Case 3.2 illustrates an interesting study that used raw, readily available academic data within an educational organization to develop predictive models to better understand attrition and improve freshman student retention in a large higher education institution. As the application case clearly states, each and every data preprocessing task described in Table 3.1 was criti- cal to a successful execution of the underlying analytics project, especially the task that related to the balancing of the data set.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 169

Student attrition has become one of the most chal- lenging problems for decision makers in academic institutions. Despite all the programs and services that are put in place to help retain students, accord- ing to the U.S. Department of Education’s Center for Educational Statistics (nces.ed.gov), only about half of those who enter higher education actually earn a bachelor’s degree. Enrollment management and the retention of students have become a top priority for administrators of colleges and universities in the United States and other countries around the world. High dropout of students usually results in overall financial loss, lower graduation rates, and an inferior school reputation in the eyes of all stakeholders. The legislators and policy makers who oversee higher education and allocate funds, the parents who pay for their children’s education to prepare them for a better future, and the students who make college choices look for evidence of institutional quality and reputation to guide their decision-making processes.

The Proposed Solution

To improve student retention, one should try to understand the nontrivial reasons behind the attrition. To be successful, one should also be able to accu- rately identify those students who are at risk of drop- ping out. So far, the vast majority of student attrition research has been devoted to understanding this com- plex, yet crucial, social phenomenon. Even though these qualitative, behavioral, and survey-based stud- ies revealed invaluable insight by developing and testing a wide range of theories, they do not pro- vide the much-needed instruments to accurately pre- dict (and potentially improve) student attrition. The project summarized in this case study proposed a quantitative research approach in which the histori- cal institutional data from student databases could be used to develop models that are capable of pre- dicting as well as explaining the institution-specific nature of the attrition problem. The proposed analyt- ics approach is shown in Figure 3.4.

Although the concept is relatively new to higher education, for more than a decade now, similar problems in the field of marketing man- agement have been studied using predictive data

analytics techniques under the name of “churn analysis” where the purpose has been to identify a sample among current customers to answer the question, “Who among our current customers are more likely to stop buying our products or services?” so that some kind of mediation or intervention pro- cess can be executed to retain them. Retaining exist- ing customers is crucial because, as we all know and as the related research has shown time and time again, acquiring a new customer costs on an order of magnitude more effort, time, and money than try- ing to keep the one that you already have.

Data Are of the Essence

The data for this research project came from a sin- gle institution (a comprehensive public university located in the Midwest region of the United States) with an average enrollment of 23,000 students, of which roughly 80 percent are the residents of the same state and roughly 19 percent of the students are listed under some minority classification. There is no significant difference between the two genders in the enrollment numbers. The average freshman student retention rate for the institution was about 80 percent, and the average six-year graduation rate was about 60 percent.

The study used five years of institutional data, which entailed 16,000+ students enrolled as fresh- men, consolidated from various and diverse univer- sity student databases. The data contained variables related to students’ academic, financial, and demo- graphic characteristics. After merging and convert- ing the multidimensional student data into a single flat file (a file with columns representing the vari- ables and rows representing the student records), the resultant file was assessed and preprocessed to identify and remedy anomalies and unusable val- ues. As an example, the study removed all inter- national student records from the data set because they did not contain information about some of the most reputed predictors (e.g., high school GPA, SAT scores). In the data transformation phase, some of the variables were aggregated (e.g., “Major” and “Concentration” variables aggregated to binary vari- ables MajorDeclared and ConcentrationSpecified)

Application Case 3.2 Improving Student Retention with Data-Driven Analytics

(Continued )

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170 Part I • Introduction to Analytics and AI

FIGURE 3.4 An Analytics Approach to Predicting Student Attrition.

Application Case 3.2 (Continued)

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 171

for better interpretation for the predictive model- ing. In addition, some of the variables were used to derive new variables (e.g., Earned/Registered ratio and YearsAfterHighSchool).

Earned>Registered = EarnedHours> RegisteredHours

YearsAfterHigh = FreshmenEnrollmentYear - School HighSchoolGraduationYear

The Earned/Registered ratio was created to have a better representation of the students’ resiliency and determination in their first semester of the freshman year. Intuitively, one would expect greater values for this variable to have a positive impact on retention/per- sistence. The YearsAfterHighSchool was created to mea- sure the impact of the time taken between high school graduation and initial college enrollment. Intuitively, one would expect this variable to be a contributor to the prediction of attrition. These aggregations and derived variables are determined based on a number of experiments conducted for a number of logical hypoth- eses. The ones that made more common sense and the ones that led to better prediction accuracy were kept in the final variable set. Reflecting the true nature of the subpopulation (i.e., the freshmen students), the dependent variable (i.e., “Second Fall Registered”) con- tained many more yes records (~80 percent) than no records (~20 percent; see Figure 3.5).

Research shows that having such imbalanced data has a negative impact on model performance.

Therefore, the study experimented with the options of using and comparing the results of the same type of models built with the original imbalanced data (biased for the yes records) and the well-balanced data.

Modeling and Assessment

The study employed four popular classification meth- ods (i.e., artificial neural networks, decision trees, sup- port vector machines, and logistic regression) along with three model ensemble techniques (i.e., bagging, busting, and information fusion). The results obtained from all model types were then compared to each other using regular classification model assessment methods (e.g., overall predictive accuracy, sensitivity, specificity) on the holdout samples.

In machine-learning algorithms (some of which will be covered in Chapter 4), sensitivity analysis is a method for identifying the “cause-and-effect” relationship between the inputs and outputs of a given prediction model. The fundamental idea behind sensitivity analysis is that it measures the importance of predictor variables based on the change in modeling performance that occurs if a predictor variable is not included in the model. This modeling and experimentation practice is also called a leave-one-out assessment. Hence, the mea- sure of sensitivity of a specific predictor variable is the ratio of the error of the trained model without the predictor variable to the error of the model that includes this predictor variable. The more sensitive

50% No

50% No

B al

an ce

d D

at a

Model Building, Testing, and Validating

Model Assessment

TP FP

FN TN

Yes No

Yes

No

(80%, 80%, 80%)

(90%, 100%, 50%)

Which one is better?

Input Data

80% No

20% Yes

*Yes: dropped out, No: persisted.

(accuracy, precision+, precision–)

(accuracy, precision+, precision–)

Validate

Built

Test

Im ba

la nc

ed D

at a

FIGURE 3.5 A Graphical Depiction of the Class Imbalance Problem.

(Continued )

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172 Part I • Introduction to Analytics and AI

the network is to a particular variable, the greater the performance decrease would be in the absence of that variable and therefore the greater the ratio of importance. In addition to the predictive power of the models, the study also conducted sensitivity analyses to determine the relative importance of the input variables.

The Results

In the first set of experiments, the study used the original imbalanced data set. Based on the 10-fold cross-validation assessment results, the support vector machines produced the best accuracy with an overall prediction rate of 87.23 percent, and the decision tree was the runner-up with an overall prediction rate of 87.16 percent, followed by artificial neural networks and logistic regression with overall prediction rates of 86.45 percent and 86.12 percent, respectively (see Table 3.2). A careful examination of these results reveals that the prediction accuracy for the “Yes” class is significantly higher than the prediction accuracy of the “No” class. In fact, all four model types predicted the students who are likely to return for the second year with better than 90 percent accuracy, but the types did poorly on predicting the students who are likely to drop out after the freshman year with less than 50 percent accuracy. Because the prediction of the “No” class is the main purpose of this study, less than 50 percent accuracy for this class was deemed not acceptable. Such a difference in prediction accu- racy of the two classes can (and should) be attributed to the imbalanced nature of the training data set (i.e., ~80 percent “Yes” and ~20 percent “No” samples).

The next round of experiments used a well- balanced data set in which the two classes are represented nearly equally in counts. In realizing this approach, the study took all samples from the minority class (i.e., the “No” class herein), randomly selected an equal number of samples from the major- ity class (i.e., the “Yes” class herein), and repeated this process 10 times to reduce potential bias of random sampling. Each of these sampling processes resulted in a data set of 7,000+ records, of which both class labels (“Yes” and “No”) were equally represented. Again, using a 10-fold cross-validation methodology, the study developed and tested prediction models for all four model types. The results of these experi- ments are shown in Table 3.3. Based on the hold- out sample results, support vector machines once again generated the best overall prediction accuracy with 81.18 percent followed by decision trees, artifi- cial neural networks, and logistic regression with an overall prediction accuracy of 80.65 percent, 79.85 percent, and 74.26 percent, respectively. As can be seen in the per-class accuracy figures, the prediction models did significantly better on predicting the “No” class with the well-balanced data than they did with the unbalanced data. Overall, the three machine- learning techniques performed significantly better than their statistical counterpart, logistic regression.

Next, another set of experiments was con- ducted to assess the predictive ability of the three ensemble models. Based on the 10-fold cross- validation methodology, the information fusion– type ensemble model produced the best results with an overall prediction rate of 82.10 percent, followed by the bagging-type ensembles and boosting-type

TABLE 3.2 Prediction Results for the Original/Unbalanced Data Set

ANN(MLP) DT(C5) SVM LR

No Yes No Yes No Yes No Yes

No 1,494 384 1,518 304 1,478 255 1,438 376

Yes 1,596 11,142 1,572 11,222 1,612 11,271 1,652 11,150

SUM 3,090 11,526 3,090 11,526 3,090 11,526 3,090 11,526

Per-class accuracy 48.35% 96.67% 49.13% 97.36% 47.83% 97.79% 46.54% 96.74%

Overall accuracy 86.45% 87.16% 87.23% 86.12%

*ANN: Artificial Neural Network; MLP: Multi-Layer Perceptron; DT: Decision Tree; SVM: Support Vector Machine; LR: Logistic Regression

Application Case 3.2 (Continued)

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 173

ensembles with overall prediction rates of 81.80 per- cent and 80.21 percent, respectively (see Table 3.4). Even though the prediction results are slightly better than those of the individual models, ensembles are known to produce more robust prediction systems compared to a single-best prediction model (more on this can be found in Chapter 4).

In addition to assessing the prediction accu- racy for each model type, a sensitivity analysis was also conducted using the developed prediction models to identify the relative importance of the independent variables (i.e., the predictors). In real- izing the overall sensitivity analysis results, each of the four individual model types generated its own sensitivity measures, ranking all independent vari- ables in a prioritized list. As expected, each model type generated slightly different sensitivity rank- ings of the independent variables. After collecting all four sets of sensitivity numbers, the sensitivity numbers are normalized and aggregated and plot- ted in a horizontal bar chart (see Figure 3.6).

The Conclusions

The study showed that, given sufficient data with the proper variables, data mining methods are capable of predicting freshmen student attrition with approximately 80 percent accuracy. Results also showed that, regardless of the prediction model employed, the balanced data set (compared to unbalanced/original data set) produced better prediction models for identifying the students who are likely to drop out of the college prior to their sophomore year. Among the four individual pre- diction models used in this study, support vector machines performed the best, followed by deci- sion trees, neural networks, and logistic regres- sion. From the usability standpoint, despite the fact that support vector machines showed better prediction results, one might choose to use deci- sion trees, because compared to support vector machines and neural networks, they portray a more transparent model structure. Decision trees

TABLE 3.3 Prediction Results for the Balanced Data Set

Confusion Matrix

ANN(MLP) DT(C5) SVM LR

No Yes No Yes No Yes No Yes

No 2,309 464 2311 417 2,313 386 2,125 626

Yes 781 2,626 779 2,673 777 2,704 965 2,464

SUM 3,090 3,090 3,090 3,090 3,090 3,090 3,090 3,090

Per-class accuracy 74.72% 84.98% 74.79% 86.50% 74.85% 87.51% 68.77% 79.74%

Overall accuracy 79.85% 80.65% 81.18% 74.26%

TABLE 3.4 Prediction Results for the Three Ensemble Models

Boosting Bagging Information Fusion (boosted trees) (random forest) (weighted average)

No Yes No Yes No Yes

No 2,242 375 2,327 362 2,335 351

Yes 848 2,715 763 2,728 755 2,739

SUM 3,090 3,090 3,090 3,090 3,090 3,090

Per-class accuracy 72.56% 87.86% 75.31% 88.28% 75.57% 88.64%

Overall accuracy 80.21% 81.80% 82.10%

(Continued )

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174 Part I • Introduction to Analytics and AI

explicitly show the reasoning process of different predictions, providing a justification for a specific outcome, whereas support vector machines and artificial neural networks are mathematical models that do not provide such a transparent view of “how they do what they do.”

Questions for Case 3.2

1. What is student attrition, and why is it an impor- tant problem in higher education?

2. What were the traditional methods to deal with the attrition problem?

3. List and discuss the data-related challenges within the context of this case study.

4. What was the proposed solution? What were the results?

Sources: D. Thammasiri, D. Delen, P. Meesad, & N. Kasap, “A Critical Assessment of Imbalanced Class Distribution Problem: The Case of Predicting Freshmen Student Attrition,” Expert Systems with Applications, 41(2), 2014, pp. 321–330; D. Delen, “A Comparative Analysis of Machine Learning Techniques for Student Retention Management,” Decision Support Systems, 49(4), 2010, pp. 498–506, and “Predicting Student Attrition with Data Mining Methods,” Journal of College Student Retention 13(1), 2011, pp. 17–35.

EarnedByRegistered

SpringStudentLoan

FallGPA

SpringGrantTuitionWaiverScholarship

FallRegisteredHours

FallStudentLoan

MaritalStatus

AdmissionType

Ethnicity

SATHighMath

SATHighEnglish

FallFederalWorkStudy

SpringFederalWorkStudy

FallGrantTuitionWaiverScholarship

PermanentAddressState

SATHighScience

SATHighComprehensive

SATHighReading

TransferredHours

ReceivedFallAid

MajorDeclared

ConcentrationSpecified

StartingTerm

HighSchoolGraduationMonth

HighSchoolGPA

YearsAfterHS

Age

0.00 0.20 0.40 0.60 1.000.80 1.20

Sex

CLEPHours

FIGURE 3.6 Sensitivity-Analysis-Based Variable Importance Results.

Application Case 3.2 (Continued)

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 175

u SECTION 3.4 REVIEW QUESTIONS

1. Why are the original/raw data not readily usable by analytics tasks?

2. What are the main data preprocessing steps?

3. What does it mean to clean/scrub the data? What activities are performed in this phase?

4. Why do we need data transformation? What are the commonly used data transforma- tion tasks?

5. Data reduction can be applied to rows (sampling) and/or columns (variable selec- tion). Which is more challenging?

3.5 STATISTICAL MODELING FOR BUSINESS ANALYTICS

Because of the increasing popularity of business analytics, the traditional statistical meth- ods and underlying techniques are also regaining their attractiveness as enabling tools to support evidence-based managerial decision making. Not only are they regaining atten- tion and admiration, but this time, they are attracting business users in addition to statisti- cians and analytics professionals.

Statistics (statistical methods and underlying techniques) is usually considered as part of descriptive analytics (see Figure 3.7). Some of the statistical methods can also be considered as part of predictive analytics, such as discriminant analysis, multiple regres- sion, logistic regression, and k-means clustering. As shown in Figure 3.7, descriptive ana- lytics has two main branches: statistics and online analytics processing (OLAP). OLAP is the term used for analyzing, characterizing, and summarizing structured data stored in organizational databases (often stored in a data warehouse or in a data mart) using cubes (i.e., multidimensional data structures that are created to extract a subset of data values to answer a specific business question). The OLAP branch of descriptive analytics has also been called business intelligence. Statistics, on the other hand, helps to characterize the data, either one variable at a time or multivariable, all together using either descriptive or inferential methods.

Statistics—a collection of mathematical techniques to characterize and interpret data—has been around for a very long time. Many methods and techniques have been developed to address the needs of the end users and the unique characteristics of the data being analyzed. Generally speaking, at the highest level, statistical methods can be

Descriptive Inferential

OLAP Statistics

Business Analytics

Descriptive Predictive Prescriptive

FIGURE 3.7 Relationship between Statistics and Descriptive Analytics.

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176 Part I • Introduction to Analytics and AI

classified as either descriptive or inferential. The main difference between descriptive and inferential statistics is the data used in these methods—whereas descriptive statistics is all about describing the sample data on hand, inferential statistics is about drawing inferences or conclusions about the characteristics of the population. In this section, we briefly describe descriptive statistics (because of the fact that it lays the foundation for, and is the integral part of, descriptive analytics), and in the following section we cover regression (both linear and logistic regression) as part of inferential statistics.

Descriptive Statistics for Descriptive Analytics

Descriptive statistics, as the name implies, describes the basic characteristics of the data at hand, often one variable at a time. Using formulas and numerical aggregations, descrip- tive statistics summarizes the data in such a way that often meaningful and easily under- standable patterns emerge from the study. Although it is very useful in data analytics and very popular among the statistical methods, descriptive statistics does not allow making conclusions (or inferences) beyond the sample of the data being analyzed. That is, it is simply a nice way to characterize and describe the data on hand without making conclu- sions (inferences or extrapolations) regarding the population of related hypotheses we might have in mind.

In business analytics, descriptive statistics plays a critical role—it allows us to un- derstand and explain/present our data in a meaningful manner using aggregated num- bers, data tables, or charts/graphs. In essence, descriptive statistics helps us convert our numbers and symbols into meaningful representations for anyone to understand and use. Such an understanding helps not only business users in their decision-making processes but also analytics professionals and data scientists to characterize and validate the data for other more sophisticated analytics tasks. Descriptive statistics allows analysts to identify data concertation, unusually large or small values (i.e., outliers), and unexpectedly dis- tributed data values for numeric variables. Therefore, the methods in descriptive statistics can be classified as either measures for central tendency or measures of dispersion. In the following section, we use a simple description and mathematical formulation/repre- sentation of these measures. In mathematical representation, we will use x1, x2, . . . , xn to represent individual values (observations) of the variable (measure) that we are interested in characterizing.

Measures of Centrality Tendency (Also Called Measures of Location or Centrality)

Measures of centrality are the mathematical methods by which we estimate or describe central positioning of a given variable of interest. A measure of central tendency is a single numerical value that aims to describe a set of data by simply identifying or estimat- ing the central position within the data. The mean (often called the arithmetic mean or the simple average) is the most commonly used measure of central tendency. In addition to mean, you could also see median or mode being used to describe the centrality of a given variable. Although, the mean, median, and mode are all valid measures of central tendency, under different circumstances, one of these measures of centrality becomes more appropriate than the others. What follows are short descriptions of these measures, including how to calculate them mathematically and pointers on the circumstances in which they are the most appropriate measure to use.

Arithmetic Mean

The arithmetic mean (or simply mean or average) is the sum of all the values/observa- tions divided by the number of observations in the data set. It is by far the most popular

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and most commonly used measure of central tendency. It is used with continuous or discrete numeric data. For a given variable x, if we happen to have n values/observations 1x1, x2, . . ., xn2 , we can write the arithmetic mean of the data sample (x, pronounced as x-bar) as follows:

x = x1 + x2 + g + xn

n

or

x = a i=1 n

xi

n

The mean has several unique characteristics. For instance, the sum of the absolute devia- tions (differences between the mean and the observations) above the mean is the same as the sum of the deviations below the mean, balancing the values on either side of it. That said, it does not suggest, however, that half the observations are above and the other half are below the mean (a common misconception among those who do not know basic statistics). Also, the mean is unique for every data set and is meaningful and calculable for both interval- and ratio-type numeric data. One major downside is that the mean can be affected by outliers (observations that are considerably larger or smaller than the rest of the data points). Outliers can pull the mean toward their direction and, hence, bias the centrality representation. Therefore, if there are outliers or if the data are erratically dis- persed and skewed, one should either avoid using the mean as the measure of centrality or augment it with other central tendency measures, such as median and mode.

Median

The median is the measure of center value in a given data set. It is the number in the middle of a given set of data that has been arranged/sorted in order of magnitude (either ascending or descending). If the number of observations is an odd number, identifying the median is very easy—just sort the observations based on their values and pick the value right in the middle. If the number of observations is an even number, identify the two middle values, and then take the simple average of these two values. The median is meaningful and calculable for ratio, interval, and ordinal data types. Once determined, one-half of the data points in the data is above and the other half is below the median. In contrary to the mean, the median is not affected by outliers or skewed data.

Mode

The mode is the observation that occurs most frequently (the most frequent value in our data set). On a histogram, it represents the highest bar in a bar chart, and, hence, it can be considered as the most popular option/value. The mode is most useful for data sets that contain a relatively small number of unique values. That is, it could be useless if the data have too many unique values (as is the case in many engineering measurements that capture high precision with a large number of decimal places), rendering each value having either one or a very small number representing its frequency. Although it is a useful measure (especially for nominal data), mode is not a very good representation of centrality, and therefore, it should not be used as the only measure of central tendency for a given data set.

In summary, which central tendency measure is the best? Although there is not a clear answer to this question, here are a few hints—use the mean when the data are not prone to outliers and there is no significant level of skewness; use the median when the data have outliers and/or it is ordinal in nature; use the mode when the data are nominal.

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Perhaps the best practice is to use all three together so that the central tendency of the data set can be captured and represented from three perspectives. Mostly because “av- erage” is a very familiar and highly used concept to everyone in regular daily activities, managers (as well as some scientists and journalists) often use the centrality measures (especially mean) inappropriately when other statistical information should be consid- ered along with the centrality. It is a better practice to present descriptive statistics as a package—a combination of centrality and dispersion measures—as opposed to a single measure such as mean.

Measures of Dispersion (Also Called Measures of Spread or Decentrality)

Measures of dispersion are the mathematical methods used to estimate or describe the degree of variation in a given variable of interest. They represent the numerical spread (compactness or lack thereof) of a given data set. To describe this dispersion, a number of statistical measures are developed; the most notable ones are range, variance, and standard deviation (and also quartiles and absolute deviation). One of the main reasons why the measures of dispersion/spread of data values are important is the fact that they give us a framework within which we can judge the central tendency—give us the indica- tion of how well the mean (or other centrality measures) represents the sample data. If the dispersion of values in the data set is large, the mean is not deemed to be a very good representation of the data. This is because a large dispersion measure indicates large dif- ferences between individual scores. Also, in research, it is often perceived as a positive sign to see a small variation within each data sample, as it may indicate homogeneity, similarity, and robustness within the collected data.

Range

The range is perhaps the simplest measure of dispersion. It is the difference between the largest and the smallest values in a given data set (i.e., variables). So we calculate range by simply identifying the smallest value in the data set (minimum), identifying the largest value in the data set (maximum), and calculating the difference between them (range = maximum - minimum).

Variance

A more comprehensive and sophisticated measure of dispersion is the variance. It is a method used to calculate the deviation of all data points in a given data set from the mean. The larger the variance, the more the data are spread out from the mean and the more variability one can observe in the data sample. To prevent the offsetting of negative and positive differences, the variance takes into account the square of the distances from the mean. The formula for a data sample can be written as

s2 = a n i = 1(xi - x )2

n - 1

where n is the number of samples, x is the mean of the sample, and xi is the ith value in the data set. The larger values of variance indicate more dispersion, whereas smaller values indicate compression in the overall data set. Because the differences are squared, larger deviations from the mean contribute significantly to the value of variance. Again, because the differences are squared, the numbers that represent de- viation/variance become somewhat meaningless (as opposed to a dollar difference, here you are given a squared dollar difference). Therefore, instead of variance, in

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many business applications, we use a more meaningful dispersion measure, called standard deviation.

Standard Deviation

The standard deviation is also a measure of the spread of values within a set of data. The standard deviation is calculated by simply taking the square root of the variations. The following formula shows the calculation of standard deviation from a given sample of data points.

s = A a n i = 1(xi - x)2

n - 1

Mean Absolute Deviation

In addition to variance and standard deviation, sometimes we also use mean absolute deviation to measure dispersion in a data set. It is a simpler way to calculate the overall deviation from the mean. Specifically, the mean absolute deviation is calculated by mea- suring the absolute values of the differences between each data point and the mean and then summing them. This process provides a measure of spread without being specific about the data point being lower or higher than the mean. The following formula shows the calculation of the mean absolute deviation:

MAD = a n i =1 �xi - x �

n

Quartiles and Interquartile Range

Quartiles help us identify spread within a subset of the data. A quartile is a quarter of the number of data points given in a data set. Quartiles are determined by first sorting the data and then splitting the sorted data into four disjoint smaller data sets. Quartiles are a useful measure of dispersion because they are much less affected by outliers or a skewness in the data set than the equivalent measures in the whole data set. Quartiles are often reported along with the median as the best choice of measure of dispersion and central tendency, respectively, when dealing with skewed and/or data with outliers. A common way of expressing quartiles is as an interquartile range, which describes the difference between the third quartile (Q3) and the first quartile (Q1), telling us about the range of the middle half of the scores in the distribution. The quartile-driven descriptive measures (both centrality and dispersion) are best explained with a popular plot called a box-and-whiskers plot (or box plot).

Box-and-Whiskers Plot

The box-and-whiskers plot (or simply a box plot) is a graphical illustration of several descriptive statistics about a given data set. They can be either horizontal or vertical, but vertical is the most common representation, especially in modern-day analytics software products. It is known to be first created and presented by John W. Tukey in 1969. Box plot is often used to illustrate both centrality and dispersion of a given data set (i.e., the distribution of the sample data) in an easy-to-understand graphical notation. Figure 3.8 shows two box plots side by side, sharing the same y-axis. As shown therein, a single chart can have one or more box plots for visual comparison purposes. In such cases, the y-axis would be the common measure of magnitude (the numerical value of the

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variable), with the x-axis showing different classes/subsets such as different time dimen- sions (e.g., descriptive statistics for annual Medicare expenses in 2015 versus 2016) or different categories (e.g., descriptive statistics for marketing expenses versus total sales).

Although historically speaking, the box plot has not been used widely and often enough (especially in areas outside of statistics), with the emerging popularity of business analytics, it is gaining fame in less technical areas of the business world. Its information richness and ease of understanding are largely to credit for its recent popularity.

The box plot shows the centrality (median and sometimes also mean) as well as the dispersion (the density of the data within the middle half—drawn as a box between the first and third quartiles), the minimum and maximum ranges (shown as extended lines from the box, looking like whiskers, that are calculated as 1.5 times the upper or lower end of the quartile box), and the outliers that are larger than the limits of the whis- kers. A box plot also shows whether the data are symmetrically distributed with respect to the mean or sway one way or another. The relative position of the median versus mean and the lengths of the whiskers on both side of the box give a good indication of the potential skewness in the data.

x

Max

Upper Quartile

Median

Lower Quartile

Min

Outliers

Outliers Larger than 1.5 times the upper quartile

Largest value, excluding larger outliers

25% of data is larger than this value

25% of data is smaller than this value

Smallest value, excluding smaller outliers

Smaller than 1.5 times the lower quartile

Mean

50% of data is larger than this value—middle of dataset

Simple average of the dataset

x

Variable 1 Variable 2

FIGURE 3.8 Understanding the Specifics about Box-and-Whiskers Plots.

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Shape of a Distribution

Although not as common as the centrality and dispersion, the shape of the data distribu- tion is also a useful measure for the descriptive statistics. Before delving into the shape of the distribution, we first need to define the distribution itself. Simply put, distribution is the frequency of data points counted and plotted over a small number of class labels or numerical ranges (i.e., bins). In a graphical illustration of distribution, the y-axis shows the frequency (count or percentage), and the x-axis shows the individual classes or bins in a rank-ordered fashion. A very well-known distribution is called normal distribution, which is perfectly symmetric on both sides of the mean and has numerous well-founded mathematical properties that make it a very useful tool for research and practice. As the dispersion of a data set increases, so does the standard deviation, and the shape of the distribution looks wider. A graphic illustration of the relationship between dispersion and distribution shape (in the context of normal distribution) is shown in Figure 3.9.

There are two commonly used measures to calculate the shape characteristics of a distribution: skewness and kurtosis. A histogram (frequency plot) is often used to visually illustrate both skewness and kurtosis.

Skewness is a measure of asymmetry (sway) in a distribution of the data that por- trays a unimodal structure—only one peak exists in the distribution of the data. Because normal distribution is a perfectly symmetric unimodal distribution, it does not have

0 1 2 3212223

(a)

(c)

(d)

(b)

FIGURE 3.9 Relationship between Dispersion and Distribution Shape Properties.

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skewness; that is, its skewness measure (i.e., the value of the coefficient of skewness) is equal to zero. The skewness measure/value can be either positive or negative. If the dis- tribution sways left (i.e., the tail is on the right side and the mean is smaller than median), then it produces a positive skewness measure; if the distribution sways right (i.e., the tail is on the left side and the mean is larger than median), then it produces a negative skew- ness measure. In Figure 3.9, (c) represents a positively skewed distribution whereas (d) represents a negatively skewed distribution. In the same figure, both (a) and (b) represent perfect symmetry and hence zero measure for skewness.

Skewness = S = a n i = 1(xi - x)3

(n - 1)s3

where s is the standard deviation and n is the number of samples. Kurtosis is another measure to use in characterizing the shape of a unimodal dis-

tribution. As opposed to the sway in shape, kurtosis focuses more on characterizing the peak/tall/skinny nature of the distribution. Specifically, kurtosis measures the degree to which a distribution is more or less peaked than a normal distribution. Whereas a posi- tive kurtosis indicates a relatively peaked/tall distribution, a negative kurtosis indicates a relatively flat/short distribution. As a reference point, a normal distribution has a kurtosis of 3. The formula for kurtosis can be written as

Kurtosis = K = a n i = 1(xi - x)4

ns4 -3

Descriptive statistics (as well as inferential statistics) can easily be calculated using com- mercially viable statistical software packages (e.g., SAS, SPSS, Minitab, JMP, Statistica) or free/open source tools (e.g., R). Perhaps the most convenient way to calculate descriptive and some of the inferential statistics is to use Excel. Technology Insights 3.1 describes in detail how to use Microsoft Excel to calculate descriptive statistics.

TECHNOLOGY INSIGHTS 3.1 How to Calculate Descriptive Statistics in Microsoft Excel

Excel, arguably the most popular data analysis tool in the world, can easily be used for descriptive statistics. Although the base configuration of Excel does not seem to have the statistics function readily available for end users, those functions come with the Excel installation and can be acti- vated (turned on) with only a few mouse clicks. Figure 3.10 shows how these statistics functions (as part of the Analysis ToolPak) can be activated in Microsoft Excel 2016.

Once activated, the Analysis ToolPak will appear in the Data menu option under the name of Data Analysis. When you click on Data Analysis in the Analysis group under the Data tab in the Excel menu bar, you will see Descriptive Statistics as one of the options within the list of data analysis tools (see Figure 3.11, steps 1, 2); click on OK, and the Descriptive Statistics dialog box will appear (see the middle of Figure 3.11). In this dialog box, you need to enter the range of the data, which can be one or more numerical columns, along with the preference check boxes, and click OK (see Figure 3.11, steps 3, 4). If the selection includes more than one numeric column, the tool treats each column as a separate data set and provides descriptive statistics for each column separately.

As a simple example, we selected two columns (labeled as Expense and Demand) and executed the Descriptive Statistics option. The bottom section of Figure 3.11 shows the output created by Excel. As can be seen, Excel produced all descriptive statistics that are covered in the previous section and added a few more to the list. In Excel 2016, it is also very easy (a few

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1

2

3

4

FIGURE 3.10 Activating Statistics Function in Excel 2016.

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3

1

4

2

FIGURE 3.11 Obtaining Descriptive Statistics in Excel.

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1

2 3

FIGURE 3.12 Creating a Box-and-Whiskers Plot in Excel 2016.

mouse clicks) to create a box-and-whiskers plot. Figure 3.12 shows the simple three-step pro- cess of creating a box-and-whiskers plot in Excel.

Although Analysis ToolPak is a very useful tool in Excel, one should be aware of an im- portant point related to the results that it generates, which have a different behavior than other ordinary Excel functions: Although Excel functions dynamically change as the underlying data in the spreadsheet are changed, the results generated by the Analysis ToolPak do not. For example, if you change the values in either or both of these columns, the Descriptive Statistics results produced by the Analysis ToolPak will stay the same. However, the same is not true for ordinary Excel func- tions. If you were to calculate the mean value of a given column (using “=AVERAGE(A1:A121)”) and then change the values within the data range, the mean value would automatically change. In summary, the results produced by Analysis ToolPak do not have a dynamic link to the underlying data, and if the data change, the analysis needs to be redone using the dialog box.

Successful applications of data analytics cover a wide range of business and organizational settings, addressing problems once thought unsolvable. Application Case 3.3 is an excellent il- lustration of those success stories in which a small municipality administration adopted a data analytics approach to intelligently detect and solve problems by continuously analyzing demand and consumption patterns.

u SECTION 3.5 REVIEW QUESTIONS

1. What is the relationship between statistics and business analytics?

2. What are the main differences between descriptive and inferential statistics?

3. List and briefly define the central tendency measures of descriptive statistics.

4. List and briefly define the dispersion measures of descriptive statistics.

5. What is a box-and-whiskers plot? What types of statistical information does it represent?

6. What are the two most commonly used shape characteristics to describe a data distribution?

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A leaky faucet. A malfunctioning dishwasher. A cracked sprinkler head. These are more than just a headache for a home owner or business to fix. They can be costly, unpredictable, and, unfortunately, hard to pin- point. Through a combination of wireless water meters and a data-analytics-driven, customer-accessible portal, the Town of Cary, North Carolina, is making it much easier to find and fix water loss issues. In the process, the town has gained a big-picture view of water usage critical to planning future water plant expansions and promoting targeted conservation efforts.

When the town of Cary installed wireless meters for 60,000 customers in 2010, it knew the new technology wouldn’t just save money by eliminating manual monthly readings; the town also realized it would get more accurate and timely information about water consumption. The Aquastar wireless system reads meters once an hour—that is 8,760 data points per customer each year instead of 12 monthly readings. The data had tremendous potential if they could be easily consumed.

“Monthly readings are like having a gallon of water’s worth of data. Hourly meter readings are more like an Olympic-size pool of data,” says Karen Mills, finance director for Cary. “SAS helps us man- age the volume of that data nicely.” In fact, the solu- tion enables the town to analyze half a billion data points on water usage and make them available to and easily consumable by all customers.

The ability to visually look at data by house- hold or commercial customer by the hour has led to some very practical applications:

• The town can notify customers of potential leaks within days.

• Customers can set alerts that notify them with- in hours if there is a spike in water usage.

• Customers can track their water usage online, helping them to be more proactive in conserv- ing water.

Through the online portal, one business in the town saw a spike in water consumption on weekends when employees are away. This seemed odd, and the unusual reading helped the company learn that a commercial dishwasher was malfunctioning, running continuously over weekends. Without the wireless

water-meter data and the customer-accessible portal, this problem could have gone unnoticed, continuing to waste water and money.

The town has a much more accurate picture of daily water usage per person, critical for planning future water plant expansions. Perhaps the most interesting perk is that the town was able to verify a hunch that has far-reaching cost ramifications: Cary residents are very economical in their use of water. “We calculate that with modern high-efficiency appli- ances, indoor water use could be as low as 35 gal- lons per person per day. Cary residents average 45 gallons, which is still phenomenally low,” explains town Water Resource Manager Leila Goodwin. Why is this important? The town was spending money to encourage water efficiency—rebates on low-flow toilets or discounts on rain barrels. Now it can take a more targeted approach, helping specific consum- ers understand and manage both their indoor and outdoor water use.

SAS was critical not just for enabling residents to understand their water use but also working behind the scenes to link two disparate databases. “We have a billing database and the meter-reading database. We needed to bring that together and make it presentable,” Mills says.

The town estimates that by just removing the need for manual readings, the Aquastar system will save more than $10 million above the cost of the project. But the analytics component could provide even bigger savings. Already, both the town and individual citizens have saved money by catch- ing water leaks early. As Cary continues to plan its future infrastructure needs, having accurate infor- mation on water usage will help it invest in the right amount of infrastructure at the right time. In addition, understanding water usage will help the town if it experiences something detrimental like a drought.

“We went through a drought in 2007,” says Goodwin. “If we go through another, we have a plan in place to use Aquastar data to see exactly how much water we are using on a day-by-day basis and communicate with customers. We can show ‘here’s what’s happening, and here is how much you can use because our supply is low.’ Hopefully, we’ll never have to use it, but we’re prepared.”

Application Case 3.3 Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems

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Questions for Case 3.3

1. What were the challenges the Town of Cary was facing?

2. What was the proposed solution? 3. What were the results? 4. What other problems and data analytics solutions

do you foresee for towns like Cary?

Source: “Municipality Puts Wireless Water Meter-Reading Data To Work (SAS' Analytics)—The Town of Cary, North Carolina Uses SAS Analytics to Analyze Data from Wireless Water Meters, Assess Demand, Detect Problems and Engage Customers.” Copyright © 2016 SAS Institute Inc., Cary, NC, USA. Reprinted with permis- sion. All rights reserved.

3.6 REGRESSION MODELING FOR INFERENTIAL STATISTICS

Regression, especially linear regression, is perhaps the most widely known and used analytics technique in statistics. Historically speaking, the roots of regression date back to the 1920s and 1930s, to the earlier work on inherited characteristics of sweet peas by Sir Francis Galton and subsequently by Karl Pearson. Since then, regression has become the statistical technique for characterization of relationships between explanatory (input) variable(s) and response (output) variable(s).

As popular as it is, regression essentially is a relatively simple statistical technique to model the dependence of a variable (response or output variable) on one (or more) explanatory (input) variables. Once identified, this relationship between the variables can be formally represented as a linear/additive function/equation. As is the case with many other modeling techniques, regression aims to capture the functional relationship be- tween and among the characteristics of the real world and describe this relationship with a mathematical model, which can then be used to discover and understand the complexi- ties of reality—explore and explain relationships or forecast future occurrences.

Regression can be used for one of two purposes: hypothesis testing—investigating potential relationships between different variables—and prediction/forecasting— estimating values of a response variable based on one or more explanatory variables. These two uses are not mutually exclusive. The explanatory power of regression is also the foundation of its predictive ability. In hypothesis testing (theory building), regression analysis can reveal the existence/strength and the directions of relationships between a number of explanatory variables (often represented with xi) and the response variable (often represented with y). In prediction, regression identifies additive mathematical relationships (in the form of an equation) between one or more explanatory variables and a response variable. Once deter- mined, this equation can be used to forecast the values of the response variable for a given set of values of the explanatory variables.

CORRELATION VERSUS REGRESSION Because regression analysis originated from cor- relation studies, and because both methods attempt to describe the association between two (or more) variables, these two terms are often confused by professionals and even by scientists. Correlation makes no a priori assumption of whether one variable is de- pendent on the other(s) and is not concerned with the relationship between variables; instead it gives an estimate on the degree of association between the variables. On the other hand, regression attempts to describe the dependence of a response variable on one (or more) explanatory variables where it implicitly assumes that there is a one- way causal effect from the explanatory variable(s) to the response variable, regardless of whether the path of effect is direct or indirect. Also, although correlation is interested in the low-level relationships between two variables, regression is concerned with the rela- tionships between all explanatory variables and the response variable.

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SIMPLE VERSUS MULTIPLE REGRESSION If the regression equation is built between one response variable and one explanatory variable, then it is called simple regression. For instance, the regression equation built to predict/explain the relationship between the height of a person (explanatory variable) and the weight of a person (response variable) is a good example of simple regression. Multiple regression is the extension of simple regression when the explanatory variables are more than one. For instance, in the pre- vious example, if we were to include not only the height of the person but also other personal characteristics (e.g., BMI, gender, ethnicity) to predict the person’s weight, then we would be performing multiple regression analysis. In both cases, the relationship between the response variable and the explanatory variable(s) is linear and additive in nature. If the relationships are not linear, then we might want to use one of many other nonlinear regression methods to better capture the relationships between the input and output variables.

How Do We Develop the Linear Regression Model?

To understand the relationship between two variables, the simplest thing that one can do is to draw a scatter plot where the y-axis represents the values of the response variable and the x-axis represents the values of the explanatory variable (see Figure 3.13). A scat- ter plot would show the changes in the response variable as a function of the changes in the explanatory variable. In the case shown in Figure 3.13, there seems to be a positive relationship between the two; as the explanatory variable values increase, so does the response variable.

Simple regression analysis aims to find a mathematical representation of this rela- tionship. In reality, it tries to find the signature of a straight line passing through right between the plotted dots (representing the observation/historical data) in such a way that it minimizes the distance between the dots and the line (the predicted values on the

R es

po ns

e V

ar ia

bl e:

y

Explanatory Variable: x

b0

b1

(xi, yi)

(xi, yi)

(xi, yi)

Regression Line

FIGURE 3.13 A Scatter Plot and a Linear Regression Line.

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theoretical regression line). Even though there are several methods/algorithms proposed to identify the regression line, the one that is most commonly used is called the ordinary least squares (OLS) method. The OLS method aims to minimize the sum of squared residuals (squared vertical distances between the observation and the regression point) and leads to a mathematical expression for the estimated value of the regression line (which are known as b parameters). For simple linear regression, the aforementioned relationship between the response variable 1y2 and the explanatory variable(s) 1x2 can be shown as a simple equation as follows:

y = b0 + b1x

In this equation, b0 is called the intercept, and b1 is called the slope. Once OLS deter- mines the values of these two coefficients, the simple equation can be used to forecast the values of y for given values of x. The sign and the value of b1 also reveal the direc- tion and the strengths of relationship between the two variables.

If the model is of a multiple linear regression type, then there would be more coef- ficients to be determined, one for each additional explanatory variable. As the following formula shows, the additional explanatory variable would be multiplied with the new bi coefficients and summed together to establish a linear additive representation of the response variable.

y = b0 + b1x1 + b2x2 + b3x3 + # + bnxn

How Do We Know If the Model Is Good Enough?

Because of a variety of reasons, sometimes models as representations of the reality do not prove to be good. Regardless of the number of explanatory variables included, there is always a possibility of not having a good model, and therefore the linear regression model needs to be assessed for its fit (the degree to which it represents the response variable). In the simplest sense, a well-fitting regression model results in predicted values close to the observed data values. For the numerical assessment, three statistical measures are often used in evaluating the fit of a regression model: R2(R - squared), the overall F-test, and the root mean square error (RMSE). All three of these measures are based on the sums of the square errors (how far the data are from the mean and how far the data are from the model’s predicted values). Different combinations of these two values pro- vide different information about how the regression model compares to the mean model.

Of the three, R2 has the most useful and understandable meaning because of its intuitive scale. The value of R2 ranges from 0 to 1 (corresponding to the amount of vari- ability explained in percentage) with 0 indicating that the relationship and the prediction power of the proposed model is not good, and 1 indicating that the proposed model is a perfect fit that produces exact predictions (which is almost never the case). The good R2 values would usually come close to one, and the closeness is a matter of the phe- nomenon being modeled—whereas an R2 value of 0.3 for a linear regression model in social sciences can be considered good enough, an R2 value of 0.7 in engineering might be considered as not a good enough fit. The improvement in the regression model can be achieved by adding more explanatory variables or using different data transforma- tion techniques, which would result in comparative increases in an R2 value. Figure 3.14 shows the process flow of developing regression models. As can be seen in the process flow, the model development task is followed by the model assessment task in which not only is the fit of the model assessed, but because of restrictive assumptions with which the linear models have to comply, the validity of the model also needs to be put under the microscope.

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190 Part I • Introduction to Analytics and AI

What Are the Most Important Assumptions in Linear Regression?

Even though they are still the choice of many for data analyses (both for explanatory and for predictive modeling purposes), linear regression models suffer from several highly restrictive assumptions. The validity of the linear model built depends on its ability to comply with these assumptions. Here are the most commonly pronounced assumptions:

1. Linearity. This assumption states that the relationship between the response variable and the explanatory variables is linear. That is, the expected value of the response variable is a straight-line function of each explanatory variable while holding all other explanatory variables fixed. Also, the slope of the line does not depend on the values of the other variables. It also implies that the effects of dif- ferent explanatory variables on the expected value of the response variable are additive in nature.

2. Independence (of errors). This assumption states that the errors of the response variable are uncorrelated with each other. This independence of the errors is weaker

Tabulated Data

Data Assessment

Scatter plot

Correlations

Model Fitting

Transform data

Estimate parameters

Model Assessment

Test assumptions

Assess model fit

Deployment

One-time use

Recurrent use

FIGURE 3.14 A Process Flow for Developing Regression Models.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 191

than actual statistical independence, which is a stronger condition and is often not needed for linear regression analysis.

3. Normality (of errors). This assumption states that the errors of the response vari- able are normally distributed. That is, they are supposed to be totally random and should not represent any nonrandom patterns.

4. Constant variance (of errors). This assumption, also called homoscedasticity, states that the response variables have the same variance in their error regardless of the values of the explanatory variables. In practice, this assumption is invalid if the response variable varies over a wide enough range/scale.

5. Multicollinearity. This assumption states that the explanatory variables are not correlated (i.e., do not replicate the same but provide a different perspective of the information needed for the model). Multicollinearity can be triggered by having two or more perfectly correlated explanatory variables presented to the model (e.g., if the same explanatory variable is mistakenly included in the model twice, one with a slight transformation of the same variable). A correlation-based data assessment usually catches this error.

There are statistical techniques developed to identify the violation of these assump- tions and techniques to mitigate them. The most important part for a modeler is to be aware of their existence and to put in place the means to assess the models to make sure that they are compliant with the assumptions they are built on.

Logistic Regression

Logistic regression is a very popular, statistically sound, probability-based classifica- tion algorithm that employs supervised learning. It was developed in the 1940s as a complement to linear regression and linear discriminant analysis methods. It has been used extensively in numerous disciplines, including the medical and social sciences fields. Logistic regression is similar to linear regression in that it also aims to regress to a mathematical function that explains the relationship between the response vari- able and the explanatory variables using a sample of past observations (training data). Logistic regression differs from linear regression with one major point: its output (re- sponse variable) is a class as opposed to a numerical variable. That is, whereas linear regression is used to estimate a continuous numerical variable, logistic regression is used to classify a categorical variable. Even though the original form of logistic regres- sion was developed for a binary output variable (e.g., 1/0, yes/no, pass/fail, accept/ reject), the present-day modified version is capable of predicting multiclass output variables (i.e., multinomial logistic regression). If there is only one predictor variable and one predicted variable, the method is called simple logistic regression (similar to calling linear regression models with only one independent variable simple linear regression).

In predictive analytics, logistic regression models are used to develop probabilis- tic models between one or more explanatory/predictor variables (which can be a mix of both continuous and categorical in nature) and a class/response variable (which can be binomial/binary or multinomial/multiclass). Unlike ordinary linear regression, logis- tic regression is used for predicting categorical (often binary) outcomes of the response variable—treating the response variable as the outcome of a Bernoulli trial. Therefore, logistic regression takes the natural logarithm of the odds of the response variable to create a continuous criterion as a transformed version of the response variable. Thus, the logit transformation is referred to as the link function in logistic regression—even though the response variable in logistic regression is categorical or binomial, the logit is the con- tinuous criterion on which linear regression is conducted. Figure 3.15 shows a logistic

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192 Part I • Introduction to Analytics and AI

regression function where the odds are represented in the x-axis (a linear function of the independent variables), whereas the probabilistic outcome is shown in the y-axis (i.e., response variable values change between 0 and 1).

The logistic function, f1y2 in Figure 3.15 is the core of logistic regression, which can take values only between 0 and 1. The following equation is a simple mathematical representation of this function:

f1y2 = 1

1 + e-1b0 + b1x 2

The logistic regression coefficients (the bs) are usually estimated using the maximum likelihood estimation method. Unlike linear regression with normally distributed residu- als, it is not possible to find a closed-form expression for the coefficient values that maxi- mizes the likelihood function, so an iterative process must be used instead. This process begins with a tentative starting solution, then revises the parameters slightly to see if the solution can be improved, and repeats this iterative revision until no improvement can be achieved or is very minimal, at which point the process is said to have completed/ converged.

Sports analytics—use of data and statistical/analytics techniques to better manage sports teams/organizations—has been gaining tremendous popularity. Use of data-driven analytics techniques has become mainstream for not only professional teams but also col- lege and amateur sports. Application Case 3.4 is an example of how existing and readily available public data sources can be used to predict college football bowl game outcomes using both classification and regression-type prediction models.

Time-Series Forecasting

Sometimes the variable that we are interested in (i.e., the response variable) might not have distinctly identifiable explanatory variables, or there might be too many of them in a highly complex relationship. In such cases, if the data are available in a desired format, a prediction model, the so-called time series, can be developed. A time series is a sequence of data points of the variable of interest, measured and represented at successive points in time spaced at uniform time intervals. Examples of time series include monthly rain volumes in a geographic area, the daily closing value of the stock market indexes, and

f (y) 1

26 24 22 0 2 4 6

b0 1 b1x

0.5

FIGURE 3.15 The Logistic Function.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 193

Predicting the outcome of a college football game (or any sports game, for that matter) is an interesting and challenging problem. Therefore, challenge-seeking researchers from both academics and industry have spent a great deal of effort on forecasting the out- come of sporting events. Large amounts of historic data exist in different media outlets (often publicly available) regarding the structure and outcomes of sporting events in the form of a variety of numeri- cally or symbolically represented factors that are assumed to contribute to those outcomes.

The end-of-season bowl games are very impor- tant to colleges in terms of both finance (bring- ing in millions of dollars of additional revenue) and reputation—for recruiting quality students and highly regarded high school athletes for their athletic pro- grams (Freeman & Brewer, 2016). Teams that are selected to compete in a given bowl game split a purse, the size of which depends on the specific bowl (some bowls are more prestigious and have higher payouts for the two teams), and therefore securing an invitation to a bowl game is the main goal of any division I-A college football program. The decision makers of the bowl games are given the authority to select and invite bowl-eligible (a team that has six

wins against its Division I-A opponents in that season) successful teams (as per the ratings and rankings) that will play in an exciting and competitive game, attract fans of both schools, and keep the remaining fans tuned in via a variety of media outlets for advertising.

In a recent data mining study, Delen et al. (2012) used eight years of bowl game data along with three popular data mining techniques (decision trees, neural networks, and support vector machines) to predict both the classification-type outcome of a game (win versus loss) and the regression-type out- come (projected point difference between the scores of the two opponents). What follows is a shorthand description of their study.

The Methodology

In this research, Delen and his colleagues followed a popular data mining methodology, CRISP-DM (Cross-Industry Standard Process for Data Mining), which is a six-step process. This popular meth- odology, which is covered in detail in Chapter 4, provided them with a systematic and structured way to conduct the underlying data mining study and hence improved the likelihood of obtaining accurate

Application Case 3.4 Predicting NCAA Bowl Game Outcomes

(Continued )

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194 Part I • Introduction to Analytics and AI

and reliable results. To objectively assess the pre- diction power of the different model types, they used a cross-validation methodology k-fold cross- validation. Details on k-fold cross-validation can be found in Chapter 4. Figure 3.16 graphically illustrates the methodology employed by the researchers.

Data Acquisition and Data Preprocessing

The sample data for this study are collected from a variety of sports databases available on the Web,

including jhowel.net, ESPN.com, Covers.com, ncaa.org, and rauzulusstreet.com. The data set included 244 bowl games representing a com- plete set of eight seasons of college football bowl games played between 2002 and 2009. Delen et al. also included an out-of-sample data set (2010– 2011 bowl games) for additional validation pur- poses. Exercising one of the popular data mining rules of thumb, they included as much relevant information in the model as possible. Therefore, after an in-depth variable identification and

Classification & Regression Trees

Neural Networks

X1

X2

Support Vector Machines

M ax

im um

-m ar

gin h yp

er pla

ne

M argin

Data Collection, Organization, Cleaning, and Transformation

Raw Data Sources

Built Classification

Models

Test Model

Tabulate the Results

Built Regression

Models

Transform and Tabulate Results

Compare the Prediction Results

Test Model

Classification Modeling

Regression Modeling

DBs

Output: Binary (win/loss) Output: Integer (point difference)

Win Loss

Win

Loss

...

......

...

10 %

10 %

10 %

10 % 10 %

10 %

10 %

10 %

10 % 10 % 10 %

10 %

10 %

10 % 10 %

10 %

10 %

10 %

10 % 10 %

FIGURE 3.16 The Graphical Illustration of the Methodology Employed in the Study.

Application Case 3.4 (Continued)

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 195

collection process, they ended up with a data set that included 36 variables, of which the first 6 were the identifying variables (i.e., name and the year of the bowl game, home and away team names, and their athletic conferences—see variables 1–6 in

Table 3.5), followed by 28 input variables (which included variables delineating a team’s seasonal sta- tistics on offense and defense, game outcomes, team composition characteristics, athletic conference char- acteristics, and how they fared against the odds—see

TABLE 3.5 Description of Variables Used in the Study

No Cat Variable Name Description

1 ID1 YEAR Year of the bowl game

2 ID BOWLGAME Name of the bowl game

3 ID HOMETEAM Home team (as listed by the bowl organizers)

4 ID AWAYTEAM Away team (as listed by the bowl organizers)

5 ID HOMECONFERENCE Conference of the home team

6 ID AWAYCONFERENCE Conference of the away team

7 I12 DEFPTPGM Defensive points per game

8 I1 DEFRYDPGM Defensive rush yards per game

9 I1 DEFYDPGM Defensive yards per game

10 I1 PPG Average number of points a given team scored per game

11 I1 PYDPGM Average total pass yards per game

12 I1 RYDPGM Team’s average total rush yards per game

13 I1 YRDPGM Average total offensive yards per game

14 I2 HMWIN% Home winning percentage

15 I2 LAST7 How many games the team won out of their last 7 games

16 I2 MARGOVIC Average margin of victory

17 I2 NCTW Nonconference team winning percentage

18 I2 PREVAPP Did the team appear in a bowl game previous year

19 I2 RDWIN% Road winning percentage

20 I2 SEASTW Winning percentage for the year

21 I2 TOP25 Winning percentage against AP top 25 teams for the year

22 I3 TSOS Strength of schedule for the year

23 I3 FR% Percentage of games played by freshmen class players for the year

24 I3 SO% Percentage of games played by sophomore class players for the year

25 I3 JR% Percentage of games played by junior class players for the year

26 I3 SR% Percentage of games played by senior class players for the year

27 I4 SEASOvUn% Percentage of times a team went over the O/U3 in the current season

28 I4 ATSCOV% Against the spread cover percentage of the team in previous bowl games

(Continued )

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196 Part I • Introduction to Analytics and AI

TABLE 3.5 (Continued)

No Cat Variable Name Description

29 I4 UNDER% Percentage of times a team went under in previous bowl games

30 I4 OVER% Percentage of times a team went over in previous bowl games

31 I4 SEASATS% Percentage of covering against the spread for the current season

32 I5 CONCH Did the team win their respective conference championship game

33 I5 CONFSOS Conference strength of schedule

34 I5 CONFWIN% Conference winning percentage

35 O1 ScoreDiff4 Score difference (HomeTeamScore – AwayTeamScore)

36 O2 WinLoss4 Whether the home team wins or loses the game

1ID: Identifier variables; O1: output variable for regression models; O2: output variable for classification models. 2Offense/defense; I2: game outcome; I3: team configuration; I4: against the odds; I5: conference stats. 3Over/Under—Whether or not a team will go over or under the expected score difference. 4Output variables—ScoreDiff for regression models and WinLoss for binary classification models.

variables 7–34 in Table 3.5), and finally the last two were the output variables (i.e., ScoreDiff—the score difference between the home team and the away team represented with an integer number—and WinLoss—whether the home team won or lost the bowl game represented with a nominal label).

In the formulation of the data set, each row (a.k.a. tuple, case, sample, example, etc.) represented a bowl game, and each column stood for a variable (i.e., identifier/input or output type). To represent the game-related comparative characteristics of the two opponent teams in the input variables, Delen et al. calculated and used the differences between the measures of the home and away teams. All these variable values are calculated from the home team’s perspective. For instance, the variable PPG (average number of points a team scored per game) repre- sents the difference between the home team’s PPG and away team’s PPG. The output variables repre- sent whether the home team wins or loses the bowl game. That is, if the ScoreDiff variable takes a posi- tive integer number, then the home team is expected to win the game by that margin; otherwise (if the ScoreDiff variable takes a negative integer number), the home team is expected to lose the game by that margin. In the case of WinLoss, the value of the out- put variable is a binary label, “Win” or “Loss,” indi- cating the outcome of the game for the home team.

The Results and Evaluation

In this study, three popular prediction techniques are used to build models (and to compare them to each other): artificial neural networks, decision trees, and support vector machines. These prediction techniques are selected based on their capability of modeling both classification and regression-type prediction problems and their popularity in recently published data mining literature. More details about these popular data min- ing methods can be found in Chapter 4.

To compare predictive accuracy of all models to one another, the researchers used a stratified k-fold cross-validation methodology. In a stratified version of k-fold cross-validation, the folds are created in a way that they contain approximately the same proportion of predictor labels (i.e., classes) as the original data set. In this study, the value of k is set to 10 (i.e., the com- plete set of 244 samples are split into 10 subsets, each having about 25 samples), which is a common prac- tice in predictive data mining applications. A graphical depiction of the 10-fold cross-validations was shown earlier in this chapter. To compare the prediction mod- els that were developed using the aforementioned three data mining techniques, the researchers chose to use three common performance criteria: accuracy, sen- sitivity, and specificity. The simple formulas for these metrics were also explained earlier in this chapter.

Application Case 3.4 (Continued)

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 197

The prediction results of the three model- ing techniques are presented in Tables 3.6 and 3.7. Table 3.6 presents the 10-fold cross-validation results of the classification methodology in which the three data mining techniques are formulated to have a binary-nominal output variable (i.e., WinLoss). Table 3.7 presents the 10-fold cross- validation results of the regression-based classifica- tion methodology in which the three data mining techniques are formulated to have a numerical out- put variable (i.e., ScoreDiff). In the regression-based classification prediction, the numerical output of the models is converted to a classification type by label- ing the positive WinLoss numbers with a “Win” and

negative WinLoss numbers with a “Loss” and then tabulating them in the confusion matrixes. Using the confusion matrices, the overall prediction accuracy, sensitivity, and specificity of each model type are calculated and presented in Tables 3.6 and 3.7. As the results indicate, the classification-type prediction methods performed better than regression-based classification-type prediction methodology. Among the three data mining technologies, classification and regression trees produced better prediction accuracy in both prediction methodologies. Overall, classification and regression tree classification mod- els produced a 10-fold cross-validation accuracy of 86.48 percent followed by support vector machines

TABLE 3.6 Prediction Results for the Direct Classification Methodology

Prediction Method (classification1)

Confusion Matrix

Accuracy2 (in %)

Sensitivity (in %)

Specificity (in %)

Win Loss

ANN (MLP) Win 92 42 75.00 68.66 82.73

Loss 19 91

SVM (RBF) Win 105 29 79.51 78.36 80.91

Loss 21 89

DT (C&RT) Win 113 21 86.48 84.33 89.09

Loss 12 98

1The output variable is a binary categorical variable (Win or Loss). 2Differences were significant.

TABLE 3.7 Prediction Results for the Regression-Based Classification Methodology

Prediction Method (regression based1)

Confusion Matrix

Accuracy2

Sensitivity

Specificity

Win Loss

ANN (MLP) Win 94 40 72.54 70.15 75.45

Loss 27 83

SVM (RBF) Win 100 34 74.59 74.63 74.55

Loss 28 82

DT (C&RT) Win 106 28 77.87 76.36 79.10

Loss 26 84

1The output variable is a numerical/integer variable (point-diff). 2Differences were sig p 6 0.01.

(Continued )

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198 Part I • Introduction to Analytics and AI

daily sales totals for a grocery store. Often, time series are visualized using a line chart. Figure 3.17 shows an example time series of sales volumes for the years 2008 through 2012 on a quarterly basis.

Time-series forecasting is the use of mathematical modeling to predict future values of the variable of interest based on previously observed values. The time-series plots/charts look and feel very similar to simple linear regression in that, as was the case in simple linear regression, in time series there are two variables: the response variable and the time variable presented in a scatter plot. Beyond this appearance similarity, there is hardly any other commonality between the two. Although regression analysis is often employed in testing theories to see if current values of one or more explanatory variables explain (and hence predict) the response variable, the time-series models are focused on extrapolating on their time-varying behavior to estimate the future values.

Time-series-forecasting assumes that all of the explanatory variables are aggregated into the response variable as a time-variant behavior. Therefore, capturing the time- variant behavior is the way to predict the future values of the response variable. To do that, the pattern is analyzed and decomposed into its main components: random variations, time trends, and seasonal cycles. The time-series example shown in Figure 3.17 illustrates all of these distinct patterns.

The techniques used to develop time-series forecasts range from very simple (the naïve forecast that suggests today’s forecast is the same as yesterday’s actual) to very complex like ARIMA (a method that combines autoregressive and moving average pat- terns in data). Most popular techniques are perhaps the averaging methods that include simple average, moving average, weighted moving average, and exponential smoothing. Many of these techniques also have advanced versions when seasonality and trend can also be taken into account for better and more accurate forecasting. The accuracy of a method is usually assessed by computing its error (calculated deviation between actuals and forecasts for the past observations) via mean absolute error (MAE), mean squared error (MSE), or mean absolute percent error (MAPE). Even though they all use the same

(with a 10-fold cross-validation accuracy of 79.51 percent) and neural networks (with a 10-fold cross- validation accuracy of 75.00 percent). Using a t-test, researchers found that these accuracy values were significantly different at 0.05 alpha level; that is, the decision tree is a significantly better predictor of this domain than the neural network and support vec- tor machine, and the support vector machine is a significantly better predictor than neural networks.

The results of the study showed that the classification-type models predict the game out- comes better than regression-based classification models. Even though these results are specific to the application domain and the data used in this study and therefore should not be generalized beyond the scope of the study, they are exciting because deci- sion trees are not only the best predictors but also the best in understanding and deployment, com- pared to the other two machine-learning techniques

employed in this study. More details about this study can be found in Delen et al. (2012).

Questions for Case 3.4

1. What are the foreseeable challenges in predicting sporting event outcomes (e.g., college bowl games)?

2. How did the researchers formulate/design the prediction problem (i.e., what were the inputs and output, and what was the representation of a single sample—row of data)?

3. How successful were the prediction results? What else can they do to improve the accuracy?

Sources: D. Delen, D. Cogdell, and N. Kasap, “A Comparative Analysis of Data Mining Methods in Predicting NCAA Bowl Outcomes,” International Journal of Forecasting, 28, 2012, pp. 543–552; K. M. Freeman, and R. M. Brewer, “The Politics of American College Football,” Journal of Applied Business and Economics, 18(2), 2016, pp. 97–101.

Application Case 3.4 (Continued)

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 199

core error measure, these three assessment methods emphasize different aspects of the error, some penalizing larger errors more so than the others.

u SECTION 3.6 REVIEW QUESTIONS

1. What is regression, and what statistical purpose does it serve?

2. What are the commonalities and differences between regression and correlation?

3. What is OLS? How does OLS determine the linear regression line?

4. List and describe the main steps to follow in developing a linear repression model.

5. What are the most commonly pronounced assumptions for linear regression?

6. What is logistics regression? How does it differ from linear regression?

7. What is time series? What are the main forecasting techniques for time-series data?

3.7 BUSINESS REPORTING

Decision makers need information to make accurate and timely decisions. Information is essentially the contextualization of data. In addition to statistical means that were ex- plained in the previous section, information (descriptive analytics) can also be obtained using OLTP systems (see the simple taxonomy of descriptive analytics in Figure 3.7). The information is usually provided to decision makers in the form of a written report (digital or on paper), although it can also be provided orally. Simply put, a report is any com- munication artifact prepared with the specific intention of conveying information in a di- gestible form to whoever needs it whenever and wherever. It is typically a document that contains information (usually driven from data) organized in a narrative, graphic, and/or tabular form, prepared periodically (recurring) or on an as-needed (ad hoc) basis, refer- ring to specific time periods, events, occurrences, or subjects. Business reports can fulfill many different (but often related) functions. Here are a few of the most prevailing ones:

• To ensure that all departments are functioning properly. • To provide information.

0

1

2

3

4

5

6

7

8

9

10

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

2008 2009 2010 2011 2012

Quarterly Product Sales (in millions)

FIGURE 3.17 A Sample Time Series of Data on Quarterly Sales Volumes.

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200 Part I • Introduction to Analytics and AI

• To provide the results of an analysis. • To persuade others to act. • To create an organizational memory (as part of a knowledge management

system).

Business reporting (also called OLAP or BI) is an essential part of the larger drive to- ward improved, evidence-based, optimal managerial decision making. The foundation of these business reports is various sources of data coming from both inside and outside the organization (OLTP systems). Creation of these reports involves extract, transform, and load (ETL) procedures in coordination with a data warehouse and then using one or more reporting tools.

Due to the rapid expansion of IT coupled with the need for improved competitive- ness in business, there has been an increase in the use of computing power to produce unified reports that join different views of the enterprise in one place. Usually, this report- ing process involves querying structured data sources, most of which were created using different logical data models and data dictionaries, to produce a human-readable, easily digestible report. These types of business reports allow managers and coworkers to stay informed and involved, review options and alternatives, and make informed decisions. Figure 3.18 shows the continuous cycle of data acquisition S information generation S decision-making S business process management. Perhaps the most critical task in this cyclical process is the reporting (i.e., information generation)—converting data from dif- ferent sources into actionable information.

Key to any successful report are clarity, brevity, completeness, and correctness. The nature of the report and the level of importance of these success factors changes significantly based on for whom the report is created. Most of the research in effective reporting is dedicated to internal reports that inform stakeholders and decision makers within the organization. There are also external reports between businesses and the government (e.g., for tax purposes or for regular filings to the Securities and Exchange Commission). Even though there is a wide variety of business reports, the ones that are often used for managerial purposes can be grouped into three major categories (Hill, 2016).

Data Repositories

Business Functions

UOB 1.0 X

UOB 2.2

UOB 2.1 X UOB 3.0

Symbol Count Description

1 Machine Failure

Exception Event

Transactional Records

Information (reporting)

Decision Maker

Action (decision)

Data

1 2 3 4 5

FIGURE 3.18 The Role of Information Reporting in Managerial Decision Making.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 201

METRIC MANAGEMENT REPORTS In many organizations, business performance is man- aged through outcome-oriented metrics. For external groups, these are service-level agreements. For internal management, they are key performance indicators (KPIs). Typically, there are enterprise-wide agreed upon targets to be tracked against over a pe- riod of time. They can be used as part of other management strategies such as Six Sigma or total quality management.

DASHBOARD-TYPE REPORTS A popular idea in business reporting in recent years has been to present a range of different performance indicators on one page like a dashboard in a car. Typically, dashboard vendors would provide a set of predefined reports with static elements and fixed structure but also allow for customization of the dashboard wid- gets, views, and set targets for various metrics. It is common to have color-coded traffic lights defined for performance (red, orange, green) to draw management’s attention to particular areas. A more detailed description of dashboards can be found in a later section of this chapter.

BALANCED SCORECARD–TYPE REPORTS This is a method developed by Kaplan and Norton that attempts to present an integrated view of success in an organization. In addition to financial performance, balanced scorecard–type reports also include cus- tomer, business process, and learning and growth perspectives. More details on balanced scorecards are provided in a later section in this chapter.

Application Case 3.5 is an example to illustrate the power and the utility of auto- mated report generation for a large (and, at a time of natural crisis, somewhat chaotic) organization such as the Federal Emergency Management Agency.

Staff at the Federal Emergency Management Agency (FEMA), the U.S. federal agency that coordinates disaster response when the president declares a national disaster, always got two floods at once. First, water covered the land. Next, a flood of paper required to administer the National Flood Insurance Program (NFIP) covered their desks— pallets and pallets of green-striped reports poured off a mainframe printer and into their offices. Individual reports were sometimes 18 inches thick with a nugget of information about insurance claims, premiums, or payments buried in them somewhere.

Bill Barton and Mike Miles do not claim to be able to do anything about the weather, but the project manager and computer scientist, respec- tively, from Computer Sciences Corporation (CSC) have used WebFOCUS software from Information Builders to turn back the flood of paper generated

by the NFIP. The program allows the government to work with national insurance companies to col- lect flood insurance premiums and pay claims for flooding in communities that adopt flood control measures. As a result of CSC’s work, FEMA staffs no longer leaf through paper reports to find the data they need. Instead, they browse insurance data posted on NFIP’s BureauNet intranet site, select just the information they want to see, and get an on-screen report or download the data as a spread- sheet. And that is only the start of the savings that WebFOCUS has provided. The number of times that NFIP staff ask CSC for special reports has dropped in half because NFIP staff can generate many of the special reports they need without calling on a pro- grammer to develop them. Then there is the cost of creating BureauNet in the first place. Barton esti- mates that using conventional Web and database software to export data from FEMA’s mainframe,

Application Case 3.5 Flood of Paper Ends at FEMA

(Continued )

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202 Part I • Introduction to Analytics and AI

u SECTION 3.7 REVIEW QUESTIONS

1. What is a report? What are reports used for?

2. What is a business report? What are the main characteristics of a good business report?

3. Describe the cyclic process of management, and comment on the role of business reports.

4. List and describe the three major categories of business reports.

5. What are the main components of a business reporting system?

3.8 DATA VISUALIZATION

Data visualization (or more appropriately, information visualization) has been defined as “the use of visual representations to explore, make sense of, and communicate data” (Few, 2007). Although the name that is commonly used is data visualization, usually what this means is information visualization. Because information is the aggregation, summarization, and contextualization of data (raw facts), what is portrayed in visualiza- tions is the information, not the data. However, because the two terms data visualiza- tion and information visualization are used interchangeably and synonymously, in this chapter we will follow suit.

Data visualization is closely related to the fields of information graphics, information visualization, scientific visualization, and statistical graphics. Until recently, the major forms of data visualization available in both BI applications have included charts and graphs as well as the other types of visual elements used to create scorecards and dashboards.

To better understand the current and future trends in the field of data visualization, it helps to begin with some historical context.

store it in a new database, and link that to a Web server would have cost about 100 times as much— more than $500,000—and taken about two years to complete compared with the few months Miles spent on the WebFOCUS solution.

When Tropical Storm Allison, a huge slug of sodden, swirling cloud, moved out of the Gulf of Mexico onto the Texas and Louisiana coastline in June 2001, it killed 34 people, most from drowning; damaged or destroyed 16,000 homes and businesses; and displaced more than 10,000 families. President George W. Bush declared 28 Texas counties disaster areas, and FEMA moved in to help. This was the first serious test for BureauNet, and it delivered. This first comprehensive use of BureauNet resulted in FEMA field staff readily accessing what they needed when they needed it and asking for many new types of reports. Fortunately, Miles and WebFOCUS were up to the task. In some cases, Barton says, “FEMA would ask for a new type of report one day, and Miles would have it on BureauNet the next day,

thanks to the speed with which he could create new reports in WebFOCUS.”

The sudden demand on the system had little impact on its performance, noted Barton. “It han- dled the demand just fine,” he says. “We had no problems with it at all. And it made a huge differ- ence to FEMA and the job they had to do. They had never had that level of access before, never had been able to just click on their desktop and generate such detailed and specific reports.”

Questions for Case 3.5

1. What is FEMA, and what does it do?

2. What are the main challenges that FEMA faces?

3. How did FEMA improve its inefficient reporting practices?

Source: Used with permission from Information Builders. Useful information flows at disaster response agency. informationbuild- ers.com/applications/fema (accessed July 2018); and fema.gov.

Application Case 3.5 (Continued)

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 203

Brief History of Data Visualization

Despite the fact that predecessors to data visualization date back to the second century AD, most developments have occurred in the last two and a half centuries, predominantly during the last 30 years (Few, 2007). Although visualization has not been widely recognized as a discipline until fairly recently, today’s most popular visual forms date back a few centuries. Geographical exploration, mathematics, and popularized history spurred the creation of early maps, graphs, and timelines as far back as the 1600s, but William Playfair is widely credited as the inventor of the modern chart, having created the first widely distributed line and bar charts in his Commercial and Political Atlas of 1786 and what is generally considered to be the first time-series line chart in his Statistical Breviary published in 1801 (see Figure 3.19).

Perhaps the most notable innovator of information graphics during this period was Charles Joseph Minard, who graphically portrayed the losses suffered by Napoleon’s army in the Russian campaign of 1812 (see Figure 3.20). Beginning at the Polish–Russian border, the thick band shows the size of the army at each position. The path of Napoleon’s retreat from Moscow in the bitterly cold winter is depicted by the dark lower band, which is tied to tem- perature and time scales. Popular visualization expert, author, and critic Edward Tufte says that this “may well be the best statistical graphic ever drawn.” In this graphic, Minard man- aged to simultaneously represent several data dimensions (the size of the army, direction of movement, geographic locations, outside temperature, etc.) in an artistic and informative

FIGURE 3.19 The First Time-Series Line Chart Created by William PlayFair in 1801.

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204 Part I • Introduction to Analytics and AI

manner. Many more excellent visualizations were created in the 1800s, and most of them are chronicled on Tufte’s Web site (edwardtufte.com) and his visualization books.

The 1900s saw the rise of a more formal, empirical attitude toward visualization, which tended to focus on aspects such as color, value scales, and labeling. In the mid- 1900s, cartographer and theorist Jacques Bertin published his Semiologie Graphique, which some say serves as the theoretical foundation of modern information visualization. Although most of his patterns are either outdated by more recent research or completely inapplicable to digital media, many are still very relevant.

In the 2000s, the Internet emerged as a new medium for visualization and brought with it many new tricks and capabilities. Not only has the worldwide, digital distribution of both data and visualization made them more accessible to a broader audience (raising visual literacy along the way), but also it has spurred the design of new forms that incorporate interaction, animation, and graphics-rendering technology unique to screen media and real- time data feeds to create immersive environments for communicating and consuming data.

Companies and individuals are, seemingly all of a sudden, interested in data; that in- terest has in turn sparked a need for visual tools that help them understand it. Cheap hard- ware sensors and do-it-yourself frameworks for building your own system are driving down the costs of collecting and processing data. Countless other applications, software tools, and low-level code libraries are springing up to help people collect, organize, manipulate, visualize, and understand data from practically any source. The Internet has also served as a fantastic distribution channel for visualizations; a diverse community of designers, program- mers, cartographers, tinkerers, and data wonks has assembled to disseminate all sorts of new ideas and tools for working with data in both visual and nonvisual forms.

Google Maps has also single-handedly democratized both the interface conven- tions (click to pan, double-click to zoom) and the technology (256-pixel square map tiles with predictable file names) for displaying interactive geography online to the extent that most people just know what to do when they are presented with a map online. Flash has served well as a cross-browser platform on which to design and develop rich, beautiful Internet applications incorporating interactive data visualization and maps; now, new

FIGURE 3.20 Decimation of Napoleon’s Army during the 1812 Russian Campaign.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 205

browser-native technologies such as canvas and SVG (sometimes collectively included under the umbrella of HTML5) are emerging to challenge Flash’s supremacy and extend the reach of dynamic visualization interfaces to mobile devices.

The future of data/information visualization is very hard to predict. We can only extrapolate from what has already been invented: more three-dimensional visualization, immersive experience with multidimensional data in a virtual reality environment, and holographic visualization of information. There is a pretty good chance that we will see something that we have never seen in the information visualization realm invented be- fore the end of this decade. Application Case 3.6 shows how visual analytics/reporting tools such as Tableau can help facilitate effective and efficient decision making through information/insight creation and sharing.

The Background

Macfarlan Smith has earned its place in medical history. The company held a royal appointment to provide medicine to Her Majesty Queen Victoria and supplied groundbreaking obstetrician Sir James Simpson with chloroform for his experiments in pain relief during labor and delivery. Today, Macfarlan Smith is a sub- sidiary of the Fine Chemical and Catalysts division of Johnson Matthey plc. The pharmaceutical manufac- turer is the world’s leading manufacturer of opiate narcotics such as codeine and morphine.

Every day, Macfarlan Smith is making decisions based on its data. The company collects and ana- lyzes manufacturing operational data, for example, to allow it to meet continuous improvement goals. Sales, marketing, and finance rely on data to identify new pharmaceutical business opportunities, grow revenues, and satisfy customer needs. Additionally, the company’s manufacturing facility in Edinburgh needs to monitor, trend, and report quality data to ensure the identity, quality, and purity of its phar- maceutical ingredients for customers and regulatory authorities such as the U.S. FDA and others as part of current good manufacturing practice (CGMP).

Challenges: Multiple Sources of Truth and Slow, Onerous Reporting Processes

The process of gathering that data, making decisions, and reporting was not easy, though. The data were

scattered across the business including in the compa- ny’s bespoke enterprise resource planning (ERP) plat- form, inside legacy departmental databases such as SQL, Access databases, and stand-alone spreadsheets. When those data were needed for decision mak- ing, excessive time and resources were devoted to extracting the data, integrating them, and presenting them in a spreadsheet or other presentation outlet.

Data quality was another concern. Because teams relied on their own individual sources of data, there were multiple versions of the truth and con- flicts between the data. And it was sometimes hard to tell which version of the data was correct and which was not.

It didn’t stop there. Even once the data had been gathered and presented, making changes “on the fly” was slow and difficult. In fact, whenever a member of the Macfarlan Smith team wanted to per- form trend or other analysis, the changes to the data needed to be approved. The end result was that the data were frequently out of date by the time they were used for decision making.

Liam Mills, Head of Continuous Improvement at Macfarlan Smith highlights a typical reporting scenario:

One of our main reporting processes is the “Corrective Action and Preventive Action,” or CAPA, which is an analysis of Macfarlan Smith’s manufacturing processes taken to eliminate causes of non-conformities or other unde- sirable situations. Hundreds of hours every month were devoted to pulling data together for CAPA—and it took days to produce each

Application Case 3.6 Macfarlan Smith Improves Operational Performance Insight with Tableau Online

(Continued )

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206 Part I • Introduction to Analytics and AI

report. Trend analysis was tricky too, because the data was static. In other reporting scenar- ios, we often had to wait for spreadsheet pivot table analysis; which was then presented on a graph, printed out, and pinned to a wall for everyone to review.

Slow, labor-intensive reporting processes, dif- ferent versions of the truth, and static data were all catalysts for change. “Many people were frustrated because they believed they didn’t have a complete picture of the business,” says Mills. “We were having more and more discussions about issues we faced— when we should have been talking about business intelligence reporting.”

The Solution: Interactive Data Visualizations

One of the Macfarlan Smith team had previous expe- rience in using Tableau and recommended Mills explore the solution further. A free trial of Tableau Online quickly convinced Mills that the hosted inter- active data visualization solution could conquer the data battles the company was facing.

“I was won over almost immediately,” he says. “The ease of use, the functionality and the breadth of data visualizations are all very impressive. And of course being a software-as-a-service (SaaS)-based solution, there’s no technology infrastructure invest- ment, we can be live almost immediately, and we have the flexibility to add users whenever we need.”

One of the key questions that needed to be answered concerned the security of the online data. “Our parent company Johnson Matthey has a cloud- first strategy, but has to be certain that any hosted solution is completely secure. Tableau Online fea- tures like single sign-on and allowing only autho- rized users to interact with the data provide that watertight security and confidence.”

The other security question that Macfarlan Smith and Johnson Matthey wanted answered was this: Where are the data physically stored? Mills again: “We are satisfied Tableau Online meets our criteria for data security and privacy. The data and workbooks are all hosted in Tableau’s new Dublin data center, so it never leaves Europe.”

Following a six-week trial, the Tableau sales manager worked with Mills and his team to build a

business case for Tableau Online. The management team approved it almost straight away, and a pilot program involving 10 users began. The pilot involved a manufacturing quality improvement initiative: look- ing at deviations from the norm, such as when a heat- ing device used in the opiate narcotics manufacturing process exceeds a temperature threshold. From this, a “quality operations” dashboard was created to track and measure deviations and put in place measures to improve operational quality and performance.

“That dashboard immediately signaled where deviations might be. We weren’t ploughing through rows of data—we reached answers straight away,” says Mills.

Throughout this initial trial and pilot, the team used Tableau training aids, such as the free training videos, product walk-throughs, and live online train- ing. They also participated in a two-day “fundamen- tals training” event in London. According to Mills, “The training was expert, precise and pitched just at the right level. It demonstrated to everyone just how intuitive Tableau Online is. We can visualize 10 years’ worth of data in just a few clicks.” The com- pany now has five Tableau Desktop users and up to 200 Tableau Online licensed users.

Mills and his team particularly like the Tableau Union feature in Version 9.3, which allows them to piece together data that have been split into little files. “It’s sometimes hard to bring together the data we use for analysis. The Union feature lets us work with data spread across multiple tabs or files, reducing the time we spend on prepping the data,” he says.

The Results: Cloud Analytics Transform Decision Making and Reporting

By standardizing on Tableau Online, Macfarlan Smith has transformed the speed and accuracy of its deci- sion making and business reporting. This includes:

• New interactive dashboards can be produced within one hour. Previously, it used to take days to integrate and present data in a static spreadsheet.

• The CAPA manufacturing process report, which used to absorb hundreds of man-hours every month and days to produce, can now be produced in minutes—with insights shared in the cloud.

Application Case 3.6 (Continued)

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 207

u SECTION 3.8 REVIEW QUESTIONS

1. What is data visualization? Why is it needed?

2. What are the historical roots of data visualization?

3. Carefully analyze Charles Joseph Minard’s graphical portrayal of Napoleon’s march. Identify and comment on all the information dimensions captured in this ancient diagram.

4. Who is Edward Tufte? Why do you think we should know about his work?

5. What do you think is the “next big thing” in data visualization?

3.9 DIFFERENT TYPES OF CHARTS AND GRAPHS

Often end users of business analytics systems are not sure what type of chart or graph to use for a specific purpose. Some charts or graphs are better at answering certain types of questions. Some look better than others. Some are simple; some are rather complex and crowded. What follows is a short description of the types of charts and/or graphs com- monly found in most business analytics tools and the types of questions they are better at answering/analyzing. This material is compiled from several published articles and other literature (Abela, 2008; Hardin et al., 2012; SAS, 2014).

Basic Charts and Graphs

What follows are the basic charts and graphs that are commonly used for information visualization.

LINE CHART The line chart is perhaps the most frequently used graphical visuals for time-series data. Line charts (or line graphs) show the relationship between two variables; they are most often used to track changes or trends over time (having one of the vari- ables set to time on the x-axis). Line charts sequentially connect individual data points to help infer changing trends over a period of time. Line charts are often used to show time-dependent changes in the values of some measure, such as changes in a specific

• Reports can be changed and interrogated “on the fly” quickly and easily, without technical intervention. Macfarlan Smith has the flexibility to publish dashboards with Tableau Desktop and share them with colleagues, partners, or customers.

• The company has one, single, trusted version of the truth.

• Macfarlan Smith is now having discussions about its data—not about the issues surround- ing data integration and data quality.

• New users can be brought online almost instantly—and there’s no technical infrastruc- ture to manage.

Following this initial success, Macfarlan Smith is now extending Tableau Online to financial report- ing, supply chain analytics, and sales forecasting. Mills

concludes, “Our business strategy is now based on data-driven decisions, not opinions. The interactive visualizations enable us to spot trends instantly, identify process improvements and take business intelligence to the next level. I’ll define my career by Tableau.”

Questions for Case 3.6

1. What were the data and reporting related chal- lenges that Macfarlan Smith faced?

2. What were the solution and the obtained results/ benefits?

Source: Tableau Customer Case Study, “Macfarlan Smith improves operational performance insight with Tableau Online,” http://www. tableau.com/stories/customer/macfarlan-smith- improves- operational-performance-insight-tableau-online (accessed June 2018). Used with permission from Tableau Software, Inc.

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stock price over a five-year period or changes in the number of daily customer service calls over a month.

BAR CHART The bar chart is among the most basic visuals used for data representation. They are effective when you have nominal data or numerical data that split nicely into different categories so you can quickly see comparative results and trends within your data. Bar charts are often used to compare data across multiple categories such as the percentage of advertising spending by departments or by product categories. Bar charts can be vertically or horizontally oriented. They can also be stacked on top of each other to show multiple dimensions in a single chart.

PIE CHART The pie chart is visually appealing, as the name implies, pie-looking charts. Because they are so visually attractive, they are often incorrectly used. Pie charts should be used only to illustrate relative proportions of a specific measure. For instance, they can be used to show the relative percentage of an advertising budget spent on differ- ent product lines, or they can show relative proportions of majors declared by college students in their sophomore year. If the number of categories to show is more than just a few (say more than four), one should seriously consider using a bar chart instead of a pie chart.

SCATTER PLOT The scatter plot is often used to explore the relationship between two or three variables (in 2D or 3D visuals). Because scatter plots are visual exploration tools, translating more than three variables into more than three dimensions is not easily achiev- able. Scatter plots are an effective way to explore the existence of trends, concentrations, and outliers. For instance, in a two-variable (two-axis) graph, a scatter plot can be used to illustrate the co-relationship between age and weight of heart disease patients, or it can illustrate the relationship between the number of customer care representatives and the number of open customer service claims. Often, a trend line is superimposed on a two- dimensional scatter plot to illustrate the nature of the relationship.

BUBBLE CHART The bubble chart is often an enhanced version of scatter plots. Bubble charts, though, are not a new visualization type; instead, they should be viewed as a tech- nique to enrich data illustrated in scatter plots (or even geographic maps). By varying the size and/or color of the circles, one can add additional data dimensions, offering more enriched meaning about the data. For instance, a bubble chart can be used to show a competitive view of college-level class attendance by major and by time of the day, and it can be used to show profit margin by product type and by geographic region.

Specialized Charts and Graphs

The graphs and charts that we review in this section are either derived from the basic charts as special cases or they are relatively new and are specific to a problem type and/ or an application area.

HISTOGRAM Graphically speaking, a histogram looks just like a bar chart. The dif- ference between histograms and generic bar charts is the information that is portrayed. Histograms are used to show the frequency distribution of one variable or several vari- ables. In a histogram, the x-axis is often used to show the categories or ranges, and the y-axis is used to show the measures/values/frequencies. Histograms show the distribu- tional shape of the data. That way, one can visually examine whether the data are nor- mally or exponentially distributed. For instance, one can use a histogram to illustrate the

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 209

exam performance of a class, to show distribution of the grades as well as comparative analysis of individual results, or to show the age distribution of the customer base.

GANTT CHART A Gantt chart is a special case of horizontal bar charts used to portray project timelines, project tasks/activity durations, and overlap among the tasks/activities. By showing start and end dates/times of tasks/activities and the overlapping relation- ships, Gantt charts provide an invaluable aid for management and control of projects. For instance, Gantt charts are often used to show project timelines, task overlaps, relative task completions (a partial bar illustrating the completion percentage inside a bar that shows the actual task duration), resources assigned to each task, milestones, and deliverables.

PERT CHART The PERT chart (also called a network diagram) is developed primarily to simplify the planning and scheduling of large and complex projects. A PERT chart shows precedence relationships among project activities/tasks. It is composed of nodes (rep- resented as circles or rectangles) and edges (represented with directed arrows). Based on the selected PERT chart convention, either nodes or the edges can be used to repre- sent the project activities/tasks (activity-on-node versus activity-on-arrow representation schema).

GEOGRAPHIC MAP When the data set includes any kind of location data (e.g., physical addresses, postal codes, state names or abbreviations, country names, latitude/longitude, or some type of custom geographic encoding), it is better and more informative to see the data on a map. Maps usually are used in conjunction with other charts and graphs rather than by themselves. For instance, one can use maps to show the distribution of customer service requests by product type (depicted in pie charts) by geographic locations. Often a large variety of information (e.g., age distribution, income distribution, education, eco- nomic growth, population changes) can be portrayed in a geographic map to help decide where to open a new restaurant or a new service station. These types of systems are often called geographic information systems (GIS).

BULLET A bullet graph is often used to show progress toward a goal. This graph is essentially a variation of a bar chart. Often bullet graphs are used in place of gauges, meters, and thermometers in a dashboard to more intuitively convey the meaning within a much smaller space. Bullet graphs compare a primary measure (e.g., year-to-date rev- enue) to one or more other measures (e.g., annual revenue target) and present this in the context of defined performance metrics (e.g., sales quotas). A bullet graph can intuitively illustrate how the primary measure is performing against overall goals (e.g., how close a sales representative is to achieving his or her annual quota).

HEAT MAP The heat map is a great visual to illustrate the comparison of continuous values across two categories using color. The goal is to help the user quickly see where the intersection of the categories is strongest and weakest in terms of numerical values of the measure being analyzed. For instance, one can use a heat map to show segmenta- tion analysis of target markets where the measure (color gradient would be the purchase amount) and the dimensions would be age and income distribution.

HIGHLIGHT TABLE The highlight table is intended to take heat maps one step further. In addition to showing how data intersect by using color, highlight tables add a number on top to provide additional detail. That is, they are two-dimensional tables with cells popu- lated with numerical values and gradients of colors. For instance, one can show sales representatives’ performance by product type and by sales volume.

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210 Part I • Introduction to Analytics and AI

TREE MAP A tree map displays hierarchical (tree-structured) data as a set of nested rectangles. Each branch of the tree is given a rectangle, which is then tiled with smaller rectangles representing subbranches. A leaf node’s rectangle has an area proportional to a specified dimension on the data. Often the leaf nodes are colored to show a separate dimension of the data. When the color and size dimensions are correlated in some way with the tree structure, one can often easily see patterns that would be difficult to spot in other ways, such as a certain color that is particularly relevant. A second advantage of tree maps is that, by construction, they make efficient use of space. As a result, they can legibly display thousands of items on the screen simultaneously.

Which Chart or Graph Should You Use?

Which chart or graph that we explained in the previous section is the best? The answer is rather easy: There is not one best chart or graph because if there were, we would not have so many chart and graph types. They all have somewhat different data representa- tion “skills.” Therefore, the right question should be, “Which chart or graph is the best for a given task?” The capabilities of the charts given in the previous section can help in selecting and using the proper chart/graph for a specific task, but doing so still is not easy to sort out. Several different chart/graph types can be used for the same visualiza- tion task. One rule of thumb is to select and use the simplest one from the alternatives to make it easy for the intended audience to understand and digest.

Although there is not a widely accepted, all-encompassing chart selection algorithm or chart/graph taxonomy, Figure 3.21 presents a rather comprehensive and highly logical

Single Variable

What would you like to show in your chart or graph?

Composition

DistributionRelationship

Two Variables

Three Variables

Changing over Time

Static Few Periods Many Periods

Only Relative Difference Matters

Relative and Absolute Difference

Matter

Only Relative Difference Matters

Relative and Absolute Difference

Matter

Simple Share

of Total

Accumulation or Subtraction

to Total

Components of

Components

Two Variables

Three Variables

Among Items Over Time

Two Variables per Item

One Variable per Item

Many Categories Few Categories

Few ItemsMany Items

Many Periods Few Periods

Cyclic Data Non-Cyclic Data Single or Few Categories

Many Categories

Many Data Points

Few Data Points

Comparison

FIGURE 3.21 A Taxonomy of Charts and Graphs. Source: Adapted from Abela, A. (2008). Advanced

Presentations by Design: Creating Communication That Drives Action. New York: Wiley.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 211

organization of chart/graph types in a taxonomy-like structure (the original version was published in Abela, 2008). The taxonomic structure is organized around the questions of “What would you like to show in your chart or graph?”—that is, what the purpose of the chart or graph will be. At that level, the taxonomy divides the purpose into four different types—relationship, comparison, distribution, and composition—and further divides the branches into subcategories based on the number of variables involved and time depen- dency of the visualization.

Even though these charts and graphs cover a major part of what is commonly used in information visualization, they by no means cover all. Today, one can find many other specialized graphs and charts that serve a specific purpose. Furthermore, the cur- rent trend is to combine/hybridize and animate these charts for better-looking and more intuitive visualization of today’s complex and volatile data sources. For instance, the interactive, animated, bubble charts available at the Gapminder Web site (gapminder. org) provide an intriguing way of exploring world health, wealth, and population data from a multidimensional perspective. Figure 3.22 depicts the types of displays available at that site. In this graph, population size, life expectancy, and per capita income at the continent level are shown; also given is a time-varying animation that shows how these variables change over time.

FIGURE 3.22 A Gapminder Chart That Shows the Wealth and Health of Nations. Source: gapminder.org.

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212 Part I • Introduction to Analytics and AI

u SECTION 3.9 REVIEW QUESTIONS

1. Why do you think there are many different types of charts and graphs?

2. What are the main differences among line, bar, and pie charts? When should you use one over the others?

3. Why would you use a geographic map? What other types of charts can be combined with it?

4. Find and explain the role of two types of charts that are not covered in this section.

3.10 EMERGENCE OF VISUAL ANALYTICS

As Seth Grimes (2009a, b) has noted, there is a “growing palate” of data visualization tech- niques and tools that enable the users of business analytics and BI systems to better “commu- nicate relationships, add historical context, uncover hidden correlations, and tell persuasive stories that clarify and call to action.” The latest Magic Quadrant for Business Intelligence and Analytics Platforms released by Gartner in February 2016 further emphasizes the impor- tance of data visualization in BI and analytics. As the chart in Figure 3.23 shows, all solution

FIGURE 3.23 Magic Quadrant for Business Intelligence and Analytics Platforms. Source: Used with

permission from Gartner Inc.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 213

providers in the Leaders and Visionaries quadrants are either relatively recently founded information visualization companies (e.g., Tableau Software, QlikTech) or well-established large analytics companies (e.g., Microsoft, SAS, IBM, SAP, MicroStrategy, Alteryx) that are increasingly focusing their efforts on information visualization and visual analytics. More de- tails on Gartner’s latest Magic Quadrant are given in Technology Insights 3.2.

In BI and analytics, the key challenges for visualization have revolved around the intuitive representation of large, complex data sets with multiple dimensions and mea- sures. For the most part, the typical charts, graphs, and other visual elements used in these applications usually involve two dimensions, sometimes three, and fairly small sub- sets of data sets. In contrast, the data in these systems reside in a data warehouse. At a

TECHNOLOGY INSIGHTS 3.2 Gartner Magic Quadrant for Business Intelligence and Analytics Platforms

Gartner, Inc., the creator of Magic Quadrants, is the leading IT research and advisory company publically traded in the United States with over $2 billion annual revenues in 2015. Founded in 1979, Gartner has 7,600 associates, including 1,600 research analysts and consultants and numer- ous clients in 90 countries.

Magic Quadrant is a research method designed and implemented by Gartner to monitor and evaluate the progress and positions of companies in a specific, technology-based market. By applying a graphical treatment and a uniform set of evaluation criteria, Magic Quadrant helps users to understand how technology providers are positioned within a market.

Gartner changed the name of this Magic Quadrant from “Business Intelligence Platforms” to “Business Intelligence and Analytics Platforms” to emphasize the growing importance of ana- lytics capabilities to the information systems that organizations are now building. Gartner defines the BI and analytics platform market as a software platform that delivers 15 capabilities across three categories: integration, information delivery, and analysis. These capabilities enable orga- nizations to build precise systems of classification and measurement to support decision making and improve performance.

Figure 3.23 illustrates the latest Magic Quadrant for Business Intelligence and Analytics Platforms. Magic Quadrant places providers in four groups (niche players, challengers, visionar- ies, and leaders) along two dimensions: completeness of vision (x-axis) and ability to execute (y-axis). As the quadrant clearly shows, most of the well-known BI/BA (business analytics) providers are positioned in the “leaders” category while many of the less known, relatively new, emerging providers are positioned in the “niche players” category.

The BI and analytics platform market’s multiyear shift from IT-led enterprise reporting to business-led self-service analytics seems to have passed the tipping point. Most new buying is of modern, business-user-centric visual analytics platforms forcing a new market perspective, sig- nificantly reordering the vendor landscape. Most of the activity in the BI and analytics platform market is from organizations that are trying to mature their visualization capabilities and to move from descriptive to predictive and prescriptive analytics echelons. The vendors in the market have overwhelmingly concentrated on meeting this user demand. If there were a single market theme in 2015, it would be that data discovery/visualization became a mainstream architec- ture. While data discovery/visualization vendors such as Tableau, Qlik, and Microsoft are solidi- fying their position in the Leaders quadrant, others (both emerging and large, well-established tool/solution providers) are trying to move out of Visionaries into the Leaders quadrant.

This emphasis on data discovery/visualization from most of the leaders and visionar- ies in the market—which are now promoting tools with business-user-friendly data integration coupled with embedded storage and computing layers and unfettered drilling—continues to accelerate the trend toward decentralization and user empowerment of BI and analytics and greatly enables organizations’ ability to perform diagnostic analytics.

Source: Gartner Magic Quadrant, released on February 4, 2016, gartner.com (accessed August 2016). Used with permission from Gartner Inc.

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214 Part I • Introduction to Analytics and AI

minimum, these warehouses involve a range of dimensions (e.g., product, location, orga- nizational structure, time), a range of measures, and millions of cells of data. In an effort to address these challenges, a number of researchers have developed a variety of new visualization techniques.

Visual Analytics

Visual analytics is a recently coined term that is often used loosely to mean nothing more than information visualization. What is meant by visual analytics is the combi- nation of visualization and predictive analytics. Whereas information visualization is aimed at answering “What happened?” and “What is happening?” and is closely associ- ated with BI (routine reports, scorecards, and dashboards), visual analytics is aimed at answering “Why is it happening?” and “What is more likely to happen?” and is usu- ally associated with business analytics (forecasting, segmentation, correlation analysis). Many of the information visualization vendors are adding the capabilities to call them- selves visual analytics solution providers. One of the top, long-time analytics solution providers, SAS Institute, is approaching it from another direction. It is embedding its analytics capabilities into a high-performance data visualization environment that it calls visual analytics.

Visual or not visual, automated or manual, online or paper based, business report- ing is not much different than telling a story. Technology Insights 3.3 provides a different, unorthodox viewpoint on better business reporting.

TECHNOLOGY INSIGHTS 3.3 Telling Great Stories with Data and Visualization

Everyone who has data to analyze has stories to tell, whether it’s diagnosing the reasons for manufacturing defects, selling a new idea in a way that captures the imagination of your target audience, or informing colleagues about a particular customer service improvement program. And when it’s telling the story behind a big strategic choice so that you and your senior management team can make a solid decision, providing a fact-based story can be es- pecially challenging. In all cases, it’s a big job. You want to be interesting and memorable; you know you need to keep it simple for your busy executives and colleagues. Yet you also know you have to be factual, detail oriented, and data driven, especially in today’s metric- centric world.

It’s tempting to present just the data and facts, but when colleagues and senior manage- ment are overwhelmed by data and facts without context, you lose. We have all experienced presentations with large slide decks only to find that the audience is so overwhelmed with data that they don’t know what to think, or they are so completely tuned out that they take away only a fraction of the key points.

Start engaging your executive team and explaining your strategies and results more powerfully by approaching your assignment as a story. You will need the “what” of your story (the facts and data), but you also need the “Who?” “How?” “Why?” and the often-missed “So what?” It’s these story elements that will make your data relevant and tangible for your audience. Creating a good story can aid you and senior management in focusing on what is important.

Why Story? Stories bring life to data and facts. They can help you make sense and order out of a disparate collection of facts. They make it easier to remember key points and can paint a vivid picture of what the future can look like. Stories also create interactivity—people put themselves into stories and can relate to the situation.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 215

Cultures have long used storytelling to pass on knowledge and content. In some cultures, storytelling is critical to their identity. For example, in New Zealand, some of the Maori people tattoo their faces with mokus. A moku is a facial tattoo containing a story about ancestors—the family tribe. A man may have a tattoo design on his face that shows features of a hammerhead to highlight unique qualities about his lineage. The design he chooses signifies what is part of his “true self” and his ancestral home.

Likewise, when we are trying to understand a story, the storyteller navigates to finding the “true north.” If senior management is looking to discuss how they will respond to a competitive change, a good story can make sense and order out of a lot of noise. For example, you may have facts and data from two studies, one including results from an advertising study and one from a product satisfaction study. Developing a story for what you measured across both studies can help people see the whole where there were disparate parts. For rallying your distributors around a new product, you can employ a story to give vision to what the future can look like. Most important, storytelling is interactive—typically, the presenter uses words and pictures that audience members can put themselves into. As a result, they become more engaged and better understand the information.

So What Is a Good Story? Most people can easily rattle off their favorite film or book. Or they remember a funny story that a colleague recently shared. Why do people remember these stories? Because they contain cer- tain characteristics. First, a good story has great characters. In some cases, the reader or viewer has a vicarious experience where they become involved with the character. The character then has to be faced with a challenge that is difficult but believable. There must be hurdles that the character overcomes. And finally, the outcome or prognosis is clear by the end of the story. The situation may not be resolved—but the story has a clear endpoint.

Think of Your Analysis as a Story—Use a Story Structure When crafting a data-rich story, the first objective is to find the story. Who are the characters? What is the drama or challenge? What hurdles have to be overcome? And at the end of your story, what do you want your audience to do as a result?

Once you know the core story, craft your other story elements: define your characters, understand the challenge, identify the hurdles, and crystallize the outcome or decision question. Make sure you are clear with what you want people to do as a result. This will shape how your audience will recall your story. With the story elements in place, write out the storyboard, which represents the structure and form of your story. Although it’s tempting to skip this step, it is bet- ter first to understand the story you are telling and then to focus on the presentation structure and form. Once the storyboard is in place, the other elements will fall into place. The storyboard will help you think about the best analogies or metaphors, clearly set up challenge or oppor- tunity, and finally see the flow and transitions needed. The storyboard also helps you focus on key visuals (graphs, charts, and graphics) that you need your executives to recall. Figure 3.24 shows a storyline for the impact of small loans in a worldwide view within the Tableau visual analytics environment.

In summary, do not be afraid to use data to tell great stories. Being factual, detail oriented, and data driven is critical in today’s metric-centric world, but it does not have to mean being boring and lengthy. In fact, by finding the real stories in your data and following the best prac- tices, you can get people to focus on your message—and thus on what’s important. Here are those best practices:

1. Think of your analysis as a story—use a story structure. 2. Be authentic—your story will flow. 3. Be visual—think of yourself as a film editor. 4. Make it easy for your audience and you. 5. Invite and direct discussion.

Source: Fink, E., & Moore, S. J. (2012). “Five Best Practices for Telling Great Stories with Data.” White paper by Tableau Software, Inc., www.tableau.com/whitepapers/telling-data-stories (accessed May 2016).

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216 Part I • Introduction to Analytics and AI

High-Powered Visual Analytics Environments

Due to the increasing demand for visual analytics coupled with fast-growing data volumes, there is an exponential movement toward investing in highly efficient visualization sys- tems. With its latest move into visual analytics, the statistical software giant SAS Institute is now among those who are leading this wave. Its new product, SAS Visual Analytics, is a very high-performance computing, in-memory solution for exploring massive amounts of data in a very short time (almost instantaneously). It empowers users to spot patterns, identify opportunities for further analysis, and convey visual results via Web reports or mobile platforms such as tablets and smartphones. Figure 3.24 shows the high-level ar- chitecture of the SAS Visual Analytics platform. On one end of the architecture, there are universal data builder and administrator capabilities, leading into explorer, report designer, and mobile BI modules, collectively providing an end-to-end visual analytics solution.

Some of the key benefits proposed by the SAS analytics platform (see Figure 3.25) are the following:

• Empowers all users with data exploration techniques and approachable analytics to drive improved decision making. SAS Visual Analytics enables different types of users to conduct fast, thorough explorations on all available data. Sampling to re- duce the data is not required and not preferred.

• Has easy-to-use, interactive Web interfaces that broaden the audience for analyt- ics, enabling everyone to glean new insights. Users can look at additional options, make more precise decisions, and drive success even faster than before.

FIGURE 3.24 A Storyline Visualization in Tableau Software. Source: Used with permission from Tableau Software, Inc.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 217

• Answers complex questions faster, enhancing the contributions from your analytic talent. SAS Visual Analytics augments the data discovery and exploration process by providing extremely fast results to enable better, more focused analysis. Analytically savvy users can identify areas of opportunity or concern from vast amounts of data so further investigation can take place quickly.

• Improves information sharing and collaboration. Large numbers of users, including those with limited analytical skills, can quickly view and interact with reports and charts via the Web, Adobe PDF files, and iPad mobile devices while IT maintains control of the underlying data and security. SAS Visual Analytics provides the right information to the right person at the right time to improve productivity and orga- nizational knowledge.

• Liberates IT by giving users a new way to access the information they need. Frees IT from the constant barrage of demands from users who need access to different amounts of data, different data views, ad hoc reports, and one-off requests for infor- mation. SAS Visual Analytics enables IT to easily load and prepare data for multiple users. Once data are loaded and available, users can dynamically explore data, cre- ate reports, and share information on their own.

• Provides room to grow at a self-determined pace. SAS Visual Analytics provides the option of using commodity hardware or database appliances from EMC Greenplum and Teradata. It is designed from the ground up for performance optimization and scalability to meet the needs of any size organization.

Figure 3.26 shows a screenshot of a SAS Analytics platform where time-series fore- casting and confidence interval around the forecast are depicted.

u SECTION 3.10 REVIEW QUESTIONS

1. What are the main reasons for the recent emergence of visual analytics?

2. Look at Gartner’s Magic Quadrant for Business Intelligence and Analytics Platforms. What do you see? Discuss and justify your observations.

3. What is the difference between information visualization and visual analytics?

FIGURE 3.25 An Overview of SAS Visual Analytics Architecture. Source: Copyright © SAS Institute, Inc. Used with permission.

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218 Part I • Introduction to Analytics and AI

4. Why should storytelling be a part of your reporting and data visualization?

5. What is a high-powered visual analytics environment? Why do we need it?

3.11 INFORMATION DASHBOARDS

Information dashboards are common components of most, if not all, BI or business ana- lytics platforms, business performance management systems, and performance measure- ment software suites. Dashboards provide visual displays of important information that is consolidated and arranged on a single screen so that the information can be digested at a single glance and easily drilled in and further explored. A typical dashboard is shown in Figure 3.27. This particular executive dashboard displays a variety of key performance indicators (KPIs) for a hypothetical software company called Sonatica (selling audio tools). This executive dashboard shows a high-level view of the different functional groups surrounding the products, starting from a general overview to the marketing efforts, sales, finance, and support departments. All of this is intended to give executive decision makers

FIGURE 3.26 A Screenshot from SAS Visual Analytics. Source: Copyright © SAS Institute, Inc. Used with permission.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 219

a quick and accurate idea of what is going on within the organization. On the left side of the dashboard, we can see (in a time-series fashion) the quarterly changes in revenues, ex- penses, and margins as well as the comparison of those figures to previous years’ monthly numbers. On the upper-right side are two dials with color-coded regions showing the amount of monthly expenses for support services (dial on the left) and the amount of other expenses (dial on the right). As the color coding indicates, although the monthly support expenses are well within the normal ranges, the other expenses are in the red region, indicating excessive values. The geographic map on the bottom right shows the distribution of sales at the country level throughout the world. Behind these graphical icons there are various mathematical functions aggregating numerous data points to their highest level of meaningful figures. By clicking on these graphical icons, the consumer of this information can drill down to more granular levels of information and data.

Dashboards are used in a wide variety of businesses for a wide variety of reasons. An example of their use and how they are developed is given in Application Case 3.7, which discusses the operational philosophy and development procedures of the Australian IT company Flink Labs.

FIGURE 3.27 A Sample Executive Dashboard. Source: A Sample Executive Dashboard from Dundas Data Visualization,

Inc., www.dundas.com, reprinted with permission.

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220 Part I • Introduction to Analytics and AI

Founded in Melbourne, Australia, in 2009, Flink Labs is an enterprise that develops business solu- tions by creatively using AI, machine learning, pre- dictive analytics, and design to produce easy-to-use dashboard applications. Over time, Flink Labs has developed a series of dashboard applications that rely on data visualization and manipulation user-end tools. Some of these are highly innovative and have been used across a range of sectors. On their Web site, the company describes the different phases of the development of a dashboard application (both for desktop and for mobile use) as follows.

Stage 1: Research

At the outset, Flink Labs tries to ask the right ques- tions, such as who the target audience for their applications is and which specific needs it must con- sider. The company also conducts in-depth research to determine aspects of the customers’ interests, such as goals, objectives, and intended outcomes, all of which feed into the dashboard application in development.

Stage 2: Analysis

Once needs and objectives have been clarified, the company utilizes advanced data mining techniques to collect, analyze, and parse all data of interest for a particular project. By using machine learning and AI, Flink Labs unearths hidden insights and narra- tives within the data that facilitate the visualization of relationships between them.

Stage 3: Design

Once data have been collected and successfully parsed and structured, the company begins one of the fundamental tasks of any dashboard applica- tion, which is designing the app. During this stage, engineers develop concepts and ideas using draw- ings and several types of coding. Collaboration with the client is essential to ensure that the design of the dashboard application meets all needs and expectations.

Stage 4: Launch

After the coding has been completed according to the design and the application has been tested, Flink deliv- ers the final product to the client together with all the necessary user manuals, documents, notes, and train- ing sessions to endure a perfect customer experience.

Flink’s dashboard application projects that are already up and running include morphing maps, radial chart applications for credit reporting compli- ances, interactive diagrams for rail works satisfac- tion, pedestrian flow programs, and applications for political neural networks.

Questions for Case 3.7

1. What are dashboard applications developed for?

2. Why does Flink Labs place so much emphasis on research?

Source: Flink Labs. (2019). “Our Service.” http://flinklabs.com/ index.html#services (accessed October 2019).

Application Case 3.7 Flink Labs and Dashboard Applications Development

Dashboard Design

Dashboards are not a new concept. Their roots can be traced at least to the executive information system of the 1980s. Today, dashboards are ubiquitous. For example, a few years back, Forrester Research estimated that over 40 percent of the largest 2,000 com- panies in the world used the technology (Ante & McGregor, 2006). Since then, one can safely assume that this number has gone up quite significantly. In fact, today it would be rather unusual to see a large company using a BI system that does not employ some sort of performance dashboards. The Dashboard Spy Web site (dashboardspy.com/about) provides further evidence of their ubiquity. The site contains descriptions and screenshots

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 221

of thousands of BI dashboards, scorecards, and BI interfaces used by businesses of all sizes and industries, nonprofits, and government agencies.

According to Eckerson (2006), a well-known expert on BI in general and dash- boards in particular, the most distinctive feature of a dashboard is its three layers of information:

1. Monitoring: Graphical, abstracted data to monitor key performance metrics. 2. Analysis: Summarized dimensional data to analyze the root cause of problems. 3. Management: Detailed operational data that identify what actions to take to re-

solve a problem.

Because of these layers, dashboards pack a large amount of information into a sin- gle screen. According to Few (2005), “The fundamental challenge of dashboard design is to display all the required information on a single screen, clearly and without distraction, in a manner that can be assimilated quickly.” To speed assimilation of the numbers, they need to be placed in context. This can be done by comparing the numbers of interest to other baseline or target numbers, by indicating whether the numbers are good or bad, by denoting whether a trend is better or worse, and by using specialized display widgets or components to set the comparative and evaluative context. Some of the common comparisons that are typically made in BI systems include comparisons against past val- ues, forecasted values, targeted values, benchmark or average values, multiple instances of the same measure, and the values of other measures (e.g., revenues versus costs).

Even with comparative measures, it is important to specifically point out whether a particular number is good or bad and whether it is trending in the right direction. Without these types of evaluative designations, it can be time consuming to determine the status of a particular number or result. Typically, either specialized visual objects (e.g., traffic lights, dials, and gauges) or visual attributes (e.g., color coding) are used to set the evalu- ative context. An interactive dashboard-driven reporting data exploration solution built by an energy company is featured in Application Case 3.8.

Energy markets all around the world are going through a significant change and transformation, creating ample opportunities along with significant challenges. As is the case in any industry, oppor- tunities are attracting more players in the market- place, increasing the competition, and reducing the tolerances for less-than-optimal business decision making. Success requires creating and disseminat- ing accurate and timely information to whomever whenever it is needed. For instance, if you need to easily track marketing budgets, balance employee workloads, and target customers with tailored mar- keting messages, you would need three different reporting solutions. Electrabel GDF SUEZ is doing all of that for its marketing and sales business unit with SAS'Analytics Visual Analytics platform.

The one-solution approach is a great time-saver for marketing professionals in an industry that is undergoing tremendous change. “It is a huge chal- lenge to stabilize our market position in the energy market. That includes volume, prices, and margins for both retail and business customers,” notes Danny Noppe, manager of Reporting Architecture and Development in the Electrabel Marketing and Sales business unit. The company is the largest supplier of electricity in Belgium and the largest producer of elec- tricity for Belgium and the Netherlands. Noppe says it is critical that Electrabel increase the efficiency of its customer communications as it explores new digital channels and develops new energy-related services.

“The better we know the customer, the bet- ter our likelihood of success,” he says. “That is why

Application Case 3.8 Visual Analytics Helps Energy Supplier Make Better Connections

(Continued )

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222 Part I • Introduction to Analytics and AI

What to Look for in a Dashboard

Although performance dashboards and other information visualization frameworks differ, they all share some common design characteristics. First, they all fit within the larger BI and/or performance measurement system. This means that their underlying architecture is the BI or performance management architecture of the larger system. Second, all well- designed dashboards and other information visualizations possess the following charac- teristics (Novell, 2009):

we combine information from various sources— phone traffic with the customer, online questions, text messages, and mail campaigns. This enhanced knowledge of our customer and prospect base will be an additional advantage within our competitive market.”

One Version of the Truth

Electrabel was using various platforms and tools for reporting purposes. This sometimes led to ambigu- ity in the reported figures. The utility also had per- formance issues in processing large data volumes. SAS Visual Analytics with in-memory technology removes the ambiguity and the performance issues. “We have the autonomy and flexibility to respond to the need for customer insight and data visualization internally,” Noppe says. “After all, fast reporting is an essential requirement for action-oriented depart- ments such as sales and marketing.”

Working More Efficiently at a Lower Cost

SAS Visual Analytics automates the process of updating information in reports. Instead of building a report that is out of date by the time it is com- pleted, the data are refreshed for all the reports once a week and is available on dashboards. In deploying the solution, Electrabel chose a phased approach, starting with simple reports and moving on to more complex ones. The first report took a few weeks to build, and the rest came quickly. The successes include the following:

• Reduction of data preparation from two days to only two hours.

• Clear graphic insight into the invoicing and composition of invoices for business-to-busi- ness (B2B) customers.

• A workload management report by the op- erational teams. Managers can evaluate team

workloads on a weekly or long-term basis and can make adjustments accordingly.

“We have significantly improved our effi- ciency and can deliver quality data and reports more frequently, and at a significantly lower cost,” says Noppe. And if the company needs to combine data from multiple sources, the process is equally easy. “Building visual reports, based on these data marts, can be achieved in a few days, or even a few hours.”

Noppe says the company plans to continue broadening its insight into the digital behavior of its customers, combining data from Web analytics, e-mail, and social media with data from back-end systems. “Eventually, we want to replace all labor- intensive reporting with SAS Visual Analytics,” he says, adding that the flexibility of SAS Visual Analytics is critical for his department. “This will give us more time to tackle other challenges. We also want to make this tool available on our mobile devices. This will allow our account managers to use up-to-date, insightful, and adaptable reports when visiting cus- tomers. We’ve got a future-oriented reporting plat- form to do all we need.”

Questions for Case 3.8

1. Why do you think energy supply companies are among the prime users of information visualiza- tion tools?

2. How did Electrabel use information visualization for the single version of the truth?

3. What were their challenges, the proposed solu- tion, and the obtained results?

Source: SAS Customer Story, “Visual Analytics Helps Energy Supplier Make Better Connections.” http://www.sas.com/ en_us/customers/electrabel-be.html (accessed July 2018). Copyright © 2018 SAS Institute Inc., Cary, NC, United States. Reprinted with permission. All rights reserved.

Application Case 3.8 (Continued)

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 223

• They use visual components (e.g., charts, performance bars, sparklines, gauges, meters, stoplights) to highlight, at a glance, the data and exceptions that require action.

• They are transparent to the user, meaning that they require minimal training and are extremely easy to use.

• They combine data from a variety of systems into a single, summarized, unified view of the business.

• They enable drill-down or drill-through to underlying data sources or reports, pro- viding more detail about the underlying comparative and evaluative context.

• They present a dynamic, real-world view with timely data refreshes, enabling the end user to stay up-to-date with any recent changes in the business.

• They require little, if any, customized coding to implement, deploy, and maintain.

Best Practices in Dashboard Design

The real estate saying “location, location, location” makes it obvious that the most im- portant attribute for a piece of real estate property is where it is located. For dashboards, it is “data, data, data.” Often overlooked, data are considered one of the most important things to focus on in designing dashboards (Carotenuto, 2007). Even if a dashboard’s ap- pearance looks professional, is aesthetically pleasing, and includes graphs and tables cre- ated according to accepted visual design standards, it is also important to ask about the data: Are they reliable? Are they timely? Are any data missing? Are they consistent across all dashboards? Here are some of the experience-driven best practices in dashboard de- sign (Radha, 2008).

Benchmark Key Performance Indicators with Industry Standards

Many customers, at some point in time, want to know if the metrics they are measuring are the right metrics to monitor. Sometimes customers have found that the metrics they are tracking are not the right ones to track. Doing a gap assessment with industry bench- marks aligns you with industry best practices.

Wrap the Dashboard Metrics with Contextual Metadata

Often when a report or a visual dashboard/scorecard is presented to business users, questions remain unanswered. The following are some examples:

• Where did you source these data? • While loading the data warehouse, what percentage of the data was rejected/

encountered data quality problems? • Is the dashboard presenting “fresh” information or “stale” information? • When was the data warehouse last refreshed? • When is it going to be refreshed next? • Were any high-value transactions that would skew the overall trends rejected as a

part of the loading process?

Validate the Dashboard Design by a Usability Specialist

In most dashboard environments, the dashboard is designed by a tool specialist without giving consideration to usability principles. Even though it is a well-engineered data warehouse that can perform well, many business users do not use the dashboard because it is perceived as not being user friendly, leading to poor adoption of the infrastructure and change management issues. Up-front validation of the dashboard design by a usabil- ity specialist can mitigate this risk.

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224 Part I • Introduction to Analytics and AI

Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard

Because there are tons of raw data, having a mechanism by which important exceptions/ behaviors are proactively pushed to the information consumers is important. A business rule can be codified, which detects the alert pattern of interest. It can be coded into a pro- gram, using database-stored procedures, which can crawl through the fact tables and detect patterns that need immediate attention. This way, information finds the business user as opposed to the business user polling the fact tables for the occurrence of critical patterns.

Enrich the Dashboard with Business-User Comments

When the same dashboard information is presented to multiple business users, a small text box can be provided that can capture the comments from an end user’s perspective. This can often be tagged to the dashboard to put the information in context, adding per- spective to the structured KPIs being rendered.

Present Information in Three Different Levels

Information can be presented in three layers depending on the granularity of the infor- mation: the visual dashboard level, the static report level, and the self-service cube level. When a user navigates the dashboard, a simple set of 8 to 12 KPIs can be presented, which would give a sense of what is going well and what is not.

Pick the Right Visual Construct Using Dashboard Design Principles

In presenting information in a dashboard, some information is presented best with bar charts and some with time-series line graphs, and when presenting correlations, a scatter plot is useful. Sometimes merely rendering it as simple tables is effective. Once the dashboard design principles are explicitly documented, all the developers working on the front end can adhere to the same principles while rendering the reports and dashboard.

Provide for Guided Analytics

In a typical organization, business users can be at various levels of analytical maturity. The capability of the dashboard can be used to guide the “average” business user to access the same navigational path as that of an analytically savvy business user.

u SECTION 3.11 REVIEW QUESTIONS

1. What is an information dashboard? Why is it so popular?

2. What are the graphical widgets commonly used in dashboards? Why?

3. List and describe the three layers of information portrayed on dashboards.

4. What are the common characteristics of dashboards and other information visuals?

5. What are the best practices in dashboard design?

Chapter Highlights

• Data have become one of the most valuable assets of today’s organizations.

• Data are the main ingredient for any BI, data science, and business analytics initiative.

• Although its value proposition is undeniable, to live up its promise, the data must comply with some basic usability and quality metrics.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 225

• The term data (datum in singular form) refers to a collection of facts usually obtained as the result of experiments, observations, transactions, or experiences.

• At the highest level of abstraction, data can be classified as structured and unstructured.

• Data in original/raw form are not usually ready to be useful in analytics tasks.

• Data preprocessing is a tedious, time-demanding, yet crucial task in business analytics.

• Statistics is a collection of mathematical tech- niques to characterize and interpret data.

• Statistical methods can be classified as either de- scriptive or inferential.

• Statistics in general, as well as descriptive statistics in particular, is a critical part of BI and business analytics.

• Descriptive statistics methods can be used to mea- sure central tendency, dispersion, or the shape of a given data set.

• Regression, especially linear regression, is per- haps the most widely known and used analytics technique in statistics.

• Linear regression and logistic regression are the two major regression types in statistics.

• Logistics regression is a probability-based classifi- cation algorithm.

• Time series is a sequence of data points of a vari- able, measured and recorded at successive points in time spaced at uniform time intervals.

• A report is any communication artifact prepared with the specific intention of conveying informa- tion in a presentable form.

• A business report is a written document that con- tains information regarding business matters.

• The key to any successful business report is clar- ity, brevity, completeness, and correctness.

• Data visualization is the use of visual representations to explore, make sense of, and communicate data.

• Perhaps the most notable information graphic of the past was developed by Charles J. Minard, who graphically portrayed the losses suffered by Napoleon’s army in the Russian campaign of 1812.

• Basic chart types include line, bar, and pie chart. • Specialized charts are often derived from the

basic charts as exceptional cases. • Data visualization techniques and tools make the

users of business analytics and BI systems better information consumers.

• Visual analytics is the combination of visualiza- tion and predictive analytics.

• Increasing demand for visual analytics coupled with fast-growing data volumes led to exponen- tial growth in highly efficient visualization sys- tems investment.

• Dashboards provide visual displays of important information that is consolidated and arranged on a single screen so that information can be di- gested at a single glance and easily drilled in and further explored.

Key terms

analytics ready arithmetic mean box-and-whiskers plot box plot bubble chart business report categorical data centrality correlation dashboards data preprocessing data quality data security data taxonomy data visualization datum descriptive statistics dimensional reduction

dispersion high-performance computing histogram inferential statistics key performance indicator

(KPI) knowledge kurtosis learning linear regression logistic regression mean absolute deviation median mode nominal data online analytics processing

(OLAP) ordinal data

ordinary least squares (OLS) pie chart quartile range ratio data regression report scatter plot skewness standard deviation statistics storytelling structured data time-series forecasting unstructured data variable selection variance visual analytics

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226 Part I • Introduction to Analytics and AI

Questions for Discussion

1. How do you describe the importance of data in analyt- ics? Can we think of analytics without data? Explain.

2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?

3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?

4. What are the most common metrics that make for analytics-ready data?

5. What are the main categories of data? What types of data can we use for BI and analytics?

6. Can we use the same data representation for all analytics models (i.e., do different analytics models require different data representation schema)? Why, or why not?

7. Why are the original/raw data not readily usable by ana- lytics tasks?

8. What are the main data preprocessing steps? List and explain their importance in analytics.

9. What does it mean to clean/scrub the data? What activi- ties are performed in this phase?

10. Data reduction can be applied to rows (sampling) and/ or columns (variable selection). Which is more chal- lenging? Explain.

11. What is the relationship between statistics and business analytics? (Consider the placement of statistics in a busi- ness analytics taxonomy.)

12. What are the main differences between descriptive and inferential statistics?

13. What is a box-and-whiskers plot? What types of statisti- cal information does it represent?

14. What are the two most commonly used shape character- istics to describe a data distribution?

15. List and briefly define the central tendency measures of descriptive statistics.

16. What are the commonalities and differences between regression and correlation?

17. List and describe the main steps to follow in developing a linear regression model.

18. What are the most commonly pronounced assumptions for linear regression? What is crucial to the regression models against these assumptions?

19. What are the commonalities and differences between linear regression and logistic regression?

20. What is time series? What are the main forecasting tech- niques for time-series data?

21. What is a business report? Why is it needed? 22. What are the best practices in business reporting? How

can we make our reports stand out? 23. Describe the cyclic process of management, and com-

ment on the role of business reports. 24. List and describe the three major categories of business

reports. 25. Why has information visualization become a center-

piece in BI and business analytics? Is there a difference between information visualization and visual analytics?

26. What are the main types of charts/graphs? Why are there so many of them?

27. How do you determine the right chart for a job? Explain and defend your reasoning.

28. What is the difference between information visualiza- tion and visual analytics?

29. Why should storytelling be a part of your reporting and data visualization?

30. What is an information dashboard? What does it present? 31. What are the best practices in designing highly informa-

tive dashboards? 32. Do you think information/performance dashboards are

here to stay? Or are they about to be outdated? What do you think will be the next big wave in BI and business analytics in terms of data/information visualization?

Exercises

Teradata University and Other Hands-on Exercises

1. Download the “Voting Behavior” data and the brief data description from the book’s Web site. This is a data set manually compiled from counties all around the United States. The data are partially processed, that is, some derived variables have been created. Your task is to thoroughly preprocess the data by identifying the error and anomalies and proposing remedies and solutions. At the end, you should have an analytics-ready version of these data. Once the pre- processing is completed, pull these data into Tableau (or into some other data visualization software tool) to extract useful visual information from it. To do so, conceptualize relevant questions and hypotheses

(come up with at least three of them) and create proper visualizations that address those questions of “tests” of those hypotheses.

2. Download Tableau (at tableau.com, following aca- demic free software download instructions on the site). Using the Visualization_MFG_Sample data set (available as an Excel file on this book’s Web site), answer the fol- lowing questions: a. What is the relationship between gross box office

revenue and other movie-related parameters given in the data set?

b. How does this relationship vary across different years? Prepare a professional-looking written report that is enhanced with screenshots of your graphic findings.

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Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 227

3. Go to teradatauniversitynetwork.com. Look for an article that deals with the nature of data, management of data, and/or governance of data as it relates to BI and analytics, and critically analyze the content of the article.

4. Digital B2B platforms and data-driven business models have fueled growth, industrial transformation, and job creation in Europe. The European Union has recently promoted initiatives such as Big Data Europe (https:// www.big-data-europe.eu/), which collects data reposi- tories from already completed EU projects. Visit the portal and discuss the strategies and intended aims of this initia- tive. What are some of the most representative databases?

5. Visit the Big Data Europe portal (https://www.big-data- europe.eu/) and download one of the datasets avail- able. Apply regression analysis to it. Based on the results thus obtained, explain two of the major benefits of using regression analysis. Illustrate your answers with examples.

6. Go to KDnuggest.com and become familiar with the range of analytics resources available on this portal. Then identify an article, a white paper, or an interview script that deals with the nature of data, management of data, and/ or governance of data as they relate to BI and business analytics, and critically analyze the content of the article.

7. Go to Stephen Few’s blog, “The Perceptual Edge” (perceptualedge.com). Go to the section of “Examples.” In this section, he provides critiques of various dashboard examples. Read a handful of these examples. Now go to dundas.com. Select the “Gallery” section of the site. Once there, click the “Digital Dashboard” selection. You will be shown a variety of different dashboard demos. Run a couple of them. a. What types of information and metrics are shown on

the demos? What types of actions can you take? b. Using some of the basic concepts from Few’s cri-

tiques, describe some of the good design points and bad design points of the demos.

8. Download an information visualization tool, such as Tableau, QlikView, or Spotfire. If your school does not have an educational agreement with these companies, a trial version would be sufficient for this exercise. Use your own data (if you have any) or use one of the data sets that comes with the tool (such tools usually have one or more data sets for demonstration purposes). Study the data, come up with several business prob- lems, and use data visualization to analyze, visualize, and potentially solve those problems.

9. Go to teradatauniversitynetwork.com. Find the “Tableau Software Project.” Read the description, exe- cute the tasks, and answer the questions.

10. Go to teradatauniversitynetwork.com. Find the assignments for SAS Visual Analytics. Using the informa- tion and step-by-step instructions provided in the assign- ment, execute the analysis on the SAS Visual Analytics tool (which is a Web-enabled system that does not require any local installation). Answer the questions posed in the assignment.

11. Go to the Deloitte Web site and download the report “Visual Analytics for Actionable Insights” (https://www2.

deloitte.com/us/en/pages/risk/articles/visual-analyt- ics-turning-data-into-actionable-insights.html). As the report says, in order to quantify value, business leaders need information—specifically, cost data. However, there are more data collection points now than ever before, resulting in a glut of information. Leaders with too much to sift through easily and no clear understanding of the facts are often unable to provide insights and recommendations. The report recommends four steps to apply visual analytics to process and parse all the available data. Discuss.

12. Go to ResearchNet and download the following article: Qu, Huamin et al. (2007). “Visual Analysis of the Air Pollution Problem in Hong Kong.” IEEE Transactions on Visualization and Computer Graphics, 13, 6 (https:// www.researchgate.net/publication/5878356_ Visual_Analysis_of_the_Air_Pollution_Problem_in_ Hong_Kong). Discuss how weather data visualization techniques can help analyze air pollution problems using a variety of methods, including circular pixel bar charts embedded into polar systems and weighted complete graphs. Describe the procedures adopted by researchers for Hong Kong and assess the results so obtained.

Team Assignments and Role-Playing Projects

1. Analytics starts with data. Identifying, accessing, obtaining, and processing of relevant data is the most essential task in any analytics study. As a team, you are tasked to find a large enough real-world data (either from your own orga- nization, which is the most preferred, or from the Internet that can start with a simple search, or from the data links posted on KDnuggets.com), one that has tens of thou- sands of rows and more than 20 variables to go through, and document a thorough data preprocessing project. In your processing of the data, identify anomalies and discrep- ancies using descriptive statistics methods and measures, and make the data analytics ready. List and justify your pre- processing steps and decisions in a comprehensive report.

2. Go to a well-known information dashboard provider Web site (dundas.com, idashboards.com, enterprise- dashboard.com). These sites provide a number of exam- ples of executive dashboards. As a team, select a particular industry (e.g., healthcare, banking, airline). Locate a hand- ful of example dashboards for that industry. Describe the types of metrics found on the dashboards. What types of displays are used to provide the information? Using what you know about dashboard design, provide a paper pro- totype of a dashboard for this information.

3. Go to teradatauniversitynetwork.com. From there, go to University of Arkansas data sources. Choose one of the large data sets, and download a large number of records (this could require you to write an SQL state- ment that creates the variables that you want to include in the data set). Come up with at least 10 questions that can be addressed with information visualization. Using your favorite data visualization tool (e.g., Tableau), ana- lyze the data, and prepare a detailed report that includes screenshots and other visuals.

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228 Part I • Introduction to Analytics and AI

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