mis 420 chapter 1 book.pdf

CHAPTER 1 Data Analytics Overview LEARNING OBJECTIVES After completing this chapter, you will be able to: Describe what data analytics is. Explain why th...

CHAPTER 1 Data Analytics Overview LEARNING OBJECTIVES After completing this chapter, you will be able to: Describe what data analytics is. Explain why the study of analytics is important. Recap examples of analytics in real-world situations, particularly business scenarios. Describe the structure of the model company and some of its employees, who appear in many of the examples in this text. WHAT IS DATA ANALYTICS? Before we begin a discussion of data analytics, we need to define what it is. Data analytics is a process that involves: (a) identifying the problem, (b) gathering relevant data that frequently are not in a usable form, (c) cleaning up the data to make them usable, (d) loading them into data storage models, (e) manipulating them to discover trends and patterns that leads to actionable insights, and (f ) making decisions based on those insights. From data to insights to decisions, data analytics enables us to answer the following questions: What has happened in the past? Why did it happen? What could happen in the future? With what certainty?  What actions can we take now to support or prevent certain events from happening in the future? Can some of the actions resulting from our insights be automated? Can the analytics process be automated? In short, data analytics is the process that takes us from data to decision. Figure 1-1 illustrates the steps. FIGURE 1-1: DATA TO DECISION People frequently use the term “data” and “information” interchangeably. In fact, they are distinct concepts. Data are the raw figures, numbers, or text that serve as the starting point of analysis. They can be stored locally and available only to users with access or they can be shared openly across the internet or any combination of the two. An example of the type of data that businesses typically analyze is sales revenue for each customer. Data become information when they reveal the causes or results of the event. For example, we could process sales revenue data to display the average revenue per customer or to reveal which customers did not make any purchases from us within a given time period. When information is given meaning, we gain knowledge and understanding of the data. Knowledge is created when we learn from information; for example, which customers did not buy from us because of pricing and which customers did not buy from us because of quality? Knowledge provides the answer to why something is the way it is. This knowledge then provides us with the capabilities to gain wisdom. Wisdom, in this context, is acquired when knowledge is gathered over time. Wisdom is the deep understanding of underlying principles and behaviors. For example, by examining the reasons why customers do not buy from us, over time we can gain the wisdom to identify and implement policies our company should pursue to acquire and retain customers. Finally, wisdom coupled with a goal yields a decision. We can implement the decision into action to influence and guide our direction. For instance, if our goal is to retain customers rather than seeking new customers, then we might launch loyalty programs. As data analytics has moved out of the realm of statistics into business and other fields, the vocabulary of data analytics has evolved as well. What began as a purely mathematical endeavor has evolved to encompass diverse concepts. Many of the terms associated with these concepts have overlapping meaning and scope. Figure 1-2 illustrates the relationship among analytics in three areas: statistics, computer science, and domain knowledge. Statistics is a rigorous branch of mathematics that deals with understanding data. It involves the collection, sampling, organization, modeling, analysis, interpretation, and presentation of data. Probability theory plays an important role in statistical inference. Computer science is the study of how computers work and the application of theory to improve computing methods and capabilities. Finally, domain knowledge relates to the expertise gained by individuals in certain areas or fields. For example, medicine is a domain. Business in general as well as specific types of businesses are also domains. Each domain has its distinctive vocabulary and analytical applications.  FIGURE 1-2: THE WORLD OF ANALYTICS Data analytics is both supported by and a subset of data science, which is the intersection of computer science, statistics and domain knowledge. Data science involves the use of computers to acquire knowledge by analyzing large amounts of data using models and domain expertise. A practitioner of data science is called a data scientist. Data scientists are specially trained in computer science, mathematics, and statistics. Data analytics applies the algorithms and models generated from data science. Therefore, analytics is more concerned with applying models than with creating them. This makes data analytics a subset of data science. The overlap of statistics and domain knowledge is what researchers focus on. For instance, a medical scientist may use statistical analysis to study the factors influencing an individual’s predisposition to certain medical conditions. Machine learning is at the intersection of computer science and statistics. Machine learning is the use of computing machines to learn using mathematics model. An example of machine learning is training a computer to learn language structures in order to prompt autocomplete words (and sentences) in mobile applications. Machine learning is discussed in further detail in Chapter 8, Chapter 9, and Chapter 11. The intersection of domain knowledge and computer science leads to the creation of specific software tools that assist in the analysis of niche problems. An example of such a tool is software that helps a radiologist identify tumors in a brain scan. Figure 1-3 displays some analytics techniques. We discuss each of these applications in the following sections. FIGURE 1-3: DATA ANALYTICS IS A SUBSET OF DATA SCIENCE WHY STUDY DATA ANALYTICS? Data analytics is an exciting field that has applications in all walks of life. The demand for people who possess the skills to understand and analyze data has grown to the point that employers are pushing educators to better train students in the fundamentals of data analysis. Digital transformation has become a leading trend in businesses. Digital transformation is the transformation of business processes, data, operations, marketing and competition to leverage digital technologies, thereby fundamentally changing that business. Data analytics plays a central role in this transformation. Employers of digitally transformed companies seek those that are experienced in data analytics. This book attempts to explain, expose and educate the reader to be prepared to digitally transform a business using data analytics.  BUSINESS AND DATA ANALYTICS That focus on data analytics remains critical to business operations and strategies. It is supported by the 2018 “State of the CIO Report” that lists business analysis as the most important IT investment by 33% of the CIOs surveyed, second only to enterprise applications such as ERPs.i A report in Forbes Tech4 disclosed that 89% of the companies surveyed believe that businesses that do not adopt a major data analytics strategy risk losing market share and momentum. If you search the internet today, you will find that the importance of analytics in business is almost without question. Clearly, data analytics is critical in today’s business environment. As stated earlier, businesses are encouraging educators to place greater emphasis on data analysis to address a growing need for workers with these skills. To highlight this need, Accenture and GE5 surveyed a cross-section of large companies in eight industries regarding their perceptions of big data and data analysis, and they published the results in a 2015 report. Roughly 50% of the respondents acknowledged they have talent shortfalls in these fields. Even as recently as 2017, many companies are still complaining of the lack of skilled analysts. In response, the AACSB, the accrediting body for business schools throughout the United States, includes technology and analytics as required content in coursework. The United States is not alone. As an example, Teradata’s Data and Analytics Trends report from 2017 noted that just under half of all organization in the United Kingdom believe they need to increase data and analytics training for employeesii. Let’s examine the various levels of data interpretation and utilization skills. These skills can be categorized into the following three areas of responsibility: (1) The data scientist is the most specialized analyst, possessing advanced math and computing skills. (2) Data analysts specialize in analysis, and they come from various educational backgrounds. They typically have an advanced college degree in business, usually with an emphasis in quantitative methods, and additional training in data modeling and manipulation. (3) Significantly, most data analysis is being performed by managers and other business users, who may have not received formal education related to data analysis. These relationships are illustrated in Figure 1-4.  FIGURE 1-4: DATA ANALYTICS SKILL LEVELS APPLICATIONS OF ANALYTICS The demand for analytical skills is clearly substantial. But, when you have acquired these skills, what can you do with them? The following are brief examples of how analytics is applied in various fields. Retail: Analytics is used extensively to assist in pricing, timing of pricing strategies, and amount of discounts; product placement; and upselling and cross-selling of products. Manufacturing: A type of data analysis called demand forecasting is the core of manufacturing planning. Marketing: Targeted marketing is made possible with predictive analytics. Marketers can analyze each customer’s behavior and use these data to predict future behaviors. They can then launch targeted individualized marketing campaigns based on this information. The effectiveness of the campaign is measured based on customer response. Supply Chain: Selecting suppliers and optimizing distribution costs both utilize data analytics techniques. Customer Service/Help Desk: Customized customer service is based on analysis of prior work orders or help tickets, the procedures that succeeded, problem-solving metrics, and data from various sources. Forecasting and Budgeting: Most businesses create forecasts and budgets based on historical data and knowledge of the business environment. The more accurate these forecasts are, the more likely management will make appropriate operational decisions. The use of large quantities of data and analytic tools helps improve forecasting and budgeting accuracy. Audit and Analysis of Internal Controls: Data from internal systems are used to analyze risk and determine how well systems comply with management’s policy of internal controls. Government: The federal government collects census data every 10 years. These data are made available to researchers, policymakers, state and local government agencies, nonprofit organizations, and the general public. The purpose of the census is to gather demographic data that can lead to better resource allocation and assist in the formulation of effective public policies. Here is a second government example: data analysis assists governments in collecting the revenues due them and in the correct amounts. Based on prior tax returns and other factors, governments can use analytical techniques to sift through tax returns or even identify missing returns to red-flag taxpayers for audits and notices of noncompliance. Utilities: Data analytics assist utility companies in predicting consumer demands for power and managing the supply of power from producers. Grid-tied power from residences and businesses that generate solar or wind power requires the utilities to balance all of the sources and consumption of power that is facilitated via data analytics. Financial Investors: Investors sift through the data of numerous companies to determine which are acceptable investments and which are high risk or unacceptable. Scientists: Scientists and researchers in many fields of study frequently collect vast quantities of data that must be analyzed and interpreted. They gather the data during the course of experimentation, simulations, and samplings, sometimes via sensors. Scientists have been important consumers of data analytics for a considerable period of time. Medicine: Analytics has a variety of medical applications. One application is to identify risk factors that lead to chronic diseases. Data from a specific population are collected over time: diet, exercise, ethnicity, gender, age, occupation, family history, and many other attributes. Factors that influence (both positively and negatively) the outcome are determined using an analytical technique called data mining. Medical practitioners then utilize this knowledge to make decisions regarding prevention and treatment. Another use of analytics in medicine is for disease prevention and control. By understanding people’s day-to-day habits, exposures to pathogens (disease- causing agents), behaviors of diseases, prior outbreaks, and similar factors, healthcare providers can respond to health threats in a meaningful and timely manner. Sports: While sports analytics has its beginnings in the mid-1900s, it did not gain common acceptance until relatively recently. A 2011 movie, Moneyball, featured Sabermetrics (the application of statistical methods to baseball data) and thus propelled baseball analytics into mainstream conversation. Those involved in other types of sports have also seen the value in data analysis resulting in rapid growth in the sports analytics field. Coaches can analyze team and player statistics to develop winning strategies. They can also analyze their players’ physiological data to determine whether their performance can be improved. Fraud Prevention: Data analytics is particularly valuable for fraud detection and prevention. Analytic techniques enable investigators to flag unusual activities for further investigation. After investigators have identified fraud, they can take corrective actions and implement controls to prevent a reoccurrence. A classic example of this process involves credit card fraud, where an unusual transaction on a cardholder’s account can prompt a denial of the transaction or even a hold on the card. Law Enforcement: Law enforcement uses data analysis to identify patterns of crime in order to allocate resources to the areas and populations that are most impacted by criminal activity which helps reduce crime and the cost of enforcement in those areas. Social Media Platforms: Social media is an effective way to communicate with a vast number of individuals, whether it be advertising, points-of-view, or casual posts about daily life. Unfortunately, not all posts are reliable or true. So-called “fake news” has had real social, political and economic fallout. Data analytics could play a big role in identifying and perhaps eliminating obvious falsehoods. These are just a few examples of the application of analytics, and most likely you can think of others. By now we’re certain you agree that analytics is pervasive in today’s society. Having established the vital role of analytics in today’s world, we now turn our attention to an overview of the analytics methodology. ANALYTICS METHODOLOGY The analytics process involves various activities, tools, and techniques. The methodology for analytics exists within a framework that provides guidance as to the necessary inputs to the process. In Chapter 12, after you have gained some practical experience, we revisit the analytics process in the context of the decision-making cycle. Figure 1-5 displays the framework for the analytics methodology. The cycle in the center is the analytics methodology, or lifecycle. The surrounding boxes constitute the framework within which the methodology functions. The three main areas of the framework are enablers, benefits, and people.  FIGURE 1-5: ANALYTICS METHODOLOGY WITHIN A FRAMEWORK Enablers are the essential components needed for the methodology to work. They include technology, infrastructure, tools, and techniques. The benefits of analytics are vast and varied. Examples are value/profit, performance, safety, health and longevity of the system, and many others. People are generally both the creators and the benefactors of analytics activities. User authorizations, internal controls, and training are required within the framework to ensure that analytics is used optimally and securely. The center of Figure 1-5 displays the 10 key steps in the analytics methodology or lifecycle. These steps can have many sub-steps. We address the sub-steps in more detail in later chapters. We briefly discuss each of the steps below. 1. Identify goals: The first step is to define the goals and outcomes of the analytics process. They can be in the form of quantitative measures, business questions, or qualitative descriptions. Examples of outcome goals are: How can I minimize returns from sales deliveries? Which customer brings us the highest profits? Which factors contribute to product misuse? 2. Gather data: Data gathering is the next step. In the social sciences, data are often gathered from surveys. In the physical sciences, they are gathered through experiments, simulations, and sensors. In the business world, they are commonly gathered from information systems that record all business activity within a corporation. Chapter 2 describes data acquisition in detail. 3. Design model: In this step, analysts determine how best to analyze the data they have acquired. One simple analysis involves dividing the data into more defined segments to better understand what they are informing. The process—called slicing and dicing—is covered in detail in Chapter 5. People who require more insightful analysis than simple slicing and dicing employ data mining models. Data scientists have created a number of powerful models. In most cases an existing data model can be used. If the data or the analysis goal is unique, however, then it might be necessary to design a new model. Doing so requires strong mathematical and analytical skills. 4. Apply model: After a model is chosen, it is applied to the dataset. Many easy-to-use tools are available to accomplish this task. These tools frequently provide a drag-and-drop interface that enables users to choose data sources and models simply by dropping them onto a canvas. Users can then configure the options for the chosen model. After the model is run against the dataset, the results are presented to the user, often in the form of a visualization such as a chart. 5. Review results: Results are reviewed, and in the case of predictive data models (predictive models are explained in Chapter 11), checked against test data. Any deviations should fall within acceptable model parameters. If they do not, then the model is refined or trained again with new data until the desired accuracy is reached.  6. Present findings: Findings from the analysis process can be presented in various ways. Displaying the results in tabular form (rows and columns), is sufficient if the results are not extensive and are easily understood by the viewer. Visualizations are used to display patterns and trends in large datasets. Interactive dashboards bring together multiple visualizations along with end-user controls. Finally, infographics utilize data and images in a storytelling narrative to report the results of the analysis. We discuss all of these visualization techniques in later chapters. 7. Derive insights: Insights are drawn using experience and domain knowledge. This is why analytics sits at the intersection of computer technology, domain expertise, and statistics. Domain knowledge (an understanding of the area of study) is key to generating insight. End-users obtain insight when they understand or interpret the reported analysis. 8. Make decision: The insights lead to decision-making. Theoretically, sound analysis complemented by domain knowledge and analytical skills will lead decision-makers to the optimal decision choice. 9. Deploy strategy: Decisions are then converted to strategies that are deployed as actions. 10. Improve: The outcomes of the actions are measured periodically to assess how well they have met the desired goals. The information obtained from the comparison of goals and actual outcomes is used to improve the process in the future. It also serves as an input at the beginning of the next cycle. After the improve step has been completed, we go back to step 1 of the cycle and start the process again. Now that we have explained what data analytics is and why it is important, and have discussed an analytics methodology framework, we introduce you to Global Bike Company and some of its employees, who will appear throughout the remaining chapters. After you become familiar with the material in this chapter you will be ready to study practical analytics.  GLOBAL BIKE COMPANY6. – THE MODEL COMPANY FIGURE 1-6: GB LOGO Along with real-world scenarios, many of the examples, explanations, and exhibits in this book relate to a fictitious company called Global Bike (GB). The data used to illustrate analytical processes relate to various business activities within GB. For simplicity, much of the data focus on GB sales processes. The following is an overview of GB’s structure, business processes, products, customers, and vendors. Company History John Davis is a world-renowned bicyclist and a mountain-racing champion. He created a company in the United States to produce trail bikes. Peter Weiss of Germany is an engineer who not only races road bikes but also designs bike frames. He formed a company to manufacture lightweight touring bike frames. John and Peter met in 2000 and merged their two companies to form GB. The company logo is depicted in Figure 1-6. The Business GB serves the professional and “prosumer” cyclist market for mountain (off- road) and touring (road) bikes. GB is known for its carbon composite frames, which are strong, lightweight, and low maintenance. These frames are features in the professional line of bikes. In contrast, the deluxe line of bikes uses an aluminum frame. Figure 1-7 shows the Professional Touring Bike in Black. This bike features a carbon composite bike frame. It typically sells for 3,200 USD in the United States. FIGURE 1-7: PRTR1000 PROFESSIONAL TOURING BIKE BLACK GB also sells bicycling accessories to their customers. Helmets, first aid kits, shirts, and water bottles are examples of these non-bike products. Figure 1-8 displays the complete list of GB products. GB sells these bicycles and accessories to high-end retailers who then sell them to the end consumer.  FIGURE 1-8: GB PRODUCT LIST Innovation, safety, reliability, and performance are the core of GB’s business. Adhering to this core has kept the company competitive in an environment of increasing threats from other companies and knockoffs. In 2015, GB embarked on a digital transformation journey to grow their business. They introduced an IoT (Internet-of-Things) enabled bicycle, Figure 1-9. While consumers were excited about an “intelligent” bicycle, skepticism about its capabilities and the price point led to too few sales to deliver profit to GB. The IoT bike was a drain on GB resources. GB had to rethink its new product. After several design thinking1 sessions to re-examine the failed IoT strategy, the GB strategic team came to understand that while the IoT bicycle was an attractive transportation option, many consumers did not want to invest in owning such a bike. The design team brainstormed an alternative that has digitally transformed the company from a manufacturer and distributor to one that has taken advantage of consumers’ desire to forego the costs of bicycle ownership. GB created an entirely different line of business—IoT bike sharing. The bike-sharing business is a considerable departure from the company’s core business model of manufacturing and selling bicycles and accessories to high- end bicycle retailers. Bike sharing is provided to the end consumers, not retailers. Sharing (or renting) to end consumers added complexity in delivery methods, tax requirements, billing and payments, marketing, and customer service, but the company felt it was an important strategic move to expand on their reputation for innovation and went ahead with the plan. Since its inception in July 2018, the IoT bike-sharing business has been a huge success. Now GB can take advantage of two very profitable business models: traditional wholesale sales of high-end bicycles, and the rental of leading-edge IoT bicycles in urban areas. FIGURE 1-9: GLOBAL BIKE RENTALS IOT BICYCLE [BY PERMISSION FROM THE TECHNICAL UNIVERSITY OF MUNICH, 2018] Organizational Structure John and Peter are the co-CEOs of GB. The company has approximately 100 employees. Roughly two-thirds of them work in the United States; the rest are employed in Germany. Figure 1-10 indicates the top-level organizational structure of GB.  FIGURE 1-10: TOP-LEVEL ORGANIZATIONAL STRUCTURE GB’s headquarters is located in Dallas (Figure 1-11 displays the business structure), and GB is registered as a U.S. company following U.S. generally accepted accounting principles (GAAP). GB operates two subsidiary companies, GB Europe, which is based in Heidelberg and is subject to international accounting standards (IFRS) and German tax regulations, and Global Bike Sharing (GBS), which is also based in Germany. Materials planning, finance, administration, HR, and IT functions are consolidated at the Dallas headquarters. The Dallas facility manufactures products for the U.S. and export markets, and its warehouse manages product distribution for the central U.S. and internet retailers. GB also maintains warehouses for shipping and export in both San Diego and Miami. San Diego handles West Coast distribution and exports for Asia, and Miami handles East Coast distribution and Latin America exports. Since 2018, GB has launched its ride share business featuring the IoT bike. While the bike is manufactured in Dallas, the initial cities where the ride share is being tested are Portland and Boston. GB Europe is headquartered in Heidelberg, Germany (DE). The majority of research and development for all of GB is housed in the Heidelberg offices. Heidelberg is also the primary GB manufacturing facility in Europe. The Heidelberg warehouse handles all shipping for southern Europe. The Hamburg warehouse handles all shipping for the United Kingdom, Ireland, the Middle East, and Africa.  FIGURE 1-11: GB BUSINESS STRUCTURE Business Partners (Customers and Vendors) Given the highly specialized nature of GB’s bicycles and the personalized needs of riders, GB sells its bikes exclusively through well-known and respected independent bicycle dealers (IBDs). These dealers employ staff members who are experts in off-road and tour racing to help consumers choose the right GB bike and accessories for their individual needs. These customers also provide years’ worth of historical data. These fictitious data have been embedded with some trends and patterns for you to discover. Whenever a business event is recorded by an information system, the relevant data are written to and stored in the database as transactional data. The record contains not only the content of the transaction, but also information about who created the transaction, when it was created, and for what purpose. As an example, a sales order is considered transactional data. The sales order data contain information about the date and time the order was created, the salesperson who created it, the types and quantities of products ordered, the requested delivery date, payment terms, and pricing, including discounts and surcharges. A sample sales order is displayed in Figure 1-12.  FIGURE 1-12: SAMPLE SALES ORDER In contrast, master data represent business entities that support business transactions. Examples are data about customers, products, vendors, employees, and fixed assets. These data don’t change significantly over time. The “to” and “ship to” data in Figure 1-12 are customer master data. Master data are used across the entire organization from the same single definition created within an integrated information system. Multiple processes can utilize the same master data. For example, product master data such as the item number can be used in the procurement (purchasing), manufacturing, and sales processes. The information system stores the attributes of business partners and products as master data. For example, the weight of a bike is stored in the product master data, and the customer’s bank account number is stored in the customer master data. Master data contain numerous attributes. Not all of them have a role in analytics. To facilitate analytics, only certain attributes will be extracted from the information system. For example, a customer bank account number would not have any impact on sales revenue. Therefore, this attribute would not be extracted for the sales analysis. The extracted attributes along with the transaction data will be made available to the end-user for analytics. All products in GB are measured as EACH (EA). A bolt, for example, is measured as EACH; that is, 10 bolts would be shown as 10 EA. GB uses two currencies—USD (U.S. Dollar) and EURO—because the company has customers in both the United States and Europe. The two currencies can be converted into a single target currency during the analytics process. Although sales transactions are saved in the original currency, the end- user analytics tools often have the capability for currency conversion. The conversion rate between the two currencies is based on algorithms discussed in Chapter 5. The historical data cover sales during the years 2007-2018. This range includes 2008, when the United States and most of the world experienced a severe financial crisis. Although sales transactions in the system record sales orders by the hour, minute, and second, these transactions were provisioned on a per-day, per- month, and per-year basis. In other words, the granularity (detail) of the sales data is at the daily level. We can increase the granularity to a per-hour basis if we determine that data from a specific time of day—perhaps noon or 6 PM— provide better insights in data analysis. Meet Some GB Employees Nina Kane has recently been hired as the U.S. Sales Manager. Nina is directly responsible not only for managing the sales team, but also for developing strategies to increase sales. Donna Vasant has been employed at GB for three years. She began as a college intern but was promoted to the position of Business Analyst I due to her solid analytics skills, despite the fact that she has had no analytics training. She has occupied that position for two years. Her role is to evaluate GB’s current business model and explore new business areas. Alistair Lee is the VP for the new line of business (LoB) for the bike-sharing market. He has helped build GB’s market for bike sharing in medium-size cities, downtowns of large cities, University neighborhoods, and historic centers. His other responsibility is overseeing social media presence for GB. The bike sharing app, bike sharing customer experience, bike sharing analytics and social media marketing are all under his purview. Jessi Mard is the Corporate Controller in the Dallas headquarters, where she works directly for the CFO. Jessi worked for a competitor in Germany and was hired at GB two years ago. She speaks both German and English fluently. Jessi is responsible for the accuracy of accounting reports and for recognizing any accounting data that may signal trouble for the company or are otherwise unexpected. Peter Pollard is the production manager at GB. He oversees all manufacturing activities, including planning. Peter has been with GB since its inception and has worked with most of the employees in the plant. Prior to working at GB, Peter had worked for another bicycle manufacturer in Chicago, Illinois. Peter is considered one of GB’s most experienced employees and is the go-to person for questions about the manufacturing process. He is known for a nearly infallible intuition when it comes to solving production problems. Tony Liu has been working at GB San Diego since he graduated from a local community college about a year ago. Because he has shown himself to be reliable and responsible, his boss, Bruce, has made him accountable for ensuring that all products ship within 24 hours of order receipt. Tony’s boss, Bruce Hewlett, is the shipping manager at GB’s San Diego distribution center. Bruce was transferred from the Miami office two years ago to improve the San Diego shipping processes. He has measurably improved the center’s on-time performance and reduced the occurrence of damaged goods. Each of these employees relies on data analytics to help with his or her job functions. In addition to other objectives, analytics and data-driven decision- making are key components of GB’s strategy, and they are encouraged by the co-CEOs. Market-leading analytic tools have been deployed within the technology infrastructure at GB. Our group of employees still need help with “practical analytics,” however, because none of them has a strong background in data analytics. Specifically, they need assistance in translating analytics theory into effective practice. Their job performance is evaluated each quarter to assess how effectively they are meeting the goals set by their supervisors and how closely their activities are aligned with the company’s strategic objectives. What are some of the challenge they face? Nina How do I anticipate and compete with new competitors for market share in the prosumer bike market? What data do I need to evaluate sales, discover trends, and identify opportunities? How can I motivate my sales team?  Is there value in organizing invitation-only promotional events for our loyal customers? Are there any delays in our supply chain that have led to lost sales? Donna Are there new markets for GB products? In addition to the newest product, IoT bikes, are there other new products that GB could bring to the market? How about a bike trolley to carry pets, babies, and groceries, especially in urban areas? Is an electric bike or a hoverboard a viable and profitable new product? Are internet sales the future for high-end bikes (instead of or in addition to retail stores)? Will internet sales compete with our loyal customers (distributors)? Alistair How is the bike sharing program growing? What are customers saying about their bike rental experiences? Are there bottlenecks or other friction points in the bike rental process? Is the pricing accurate? Are competitors competing for a share of the bike rental market? Jessi Are profit margins in line with expectations? Are operational efficiencies affecting profitability? How can we encourage customers to pay more quickly? How well can we forecast cash flows and other key accounting indicators? Are our internal accounting controls effective?  Peter How can we become more efficient in the manufacturing processes? Is the quality of raw materials affecting production? Can the manufacturing plant meet sales demand? Is the manufacturing facility set up to reduce unnecessary material movements and encourage efficiency in the production process? How does the production of IoT bicycles affect capabilities elsewhere in the facility? Tony What percentage of orders is shipping within 24 hours of order receipt? Bruce How many orders have not shipped within 24 hours of receipt and why? How many products were damaged during the packing and shipping process? Did any orders ship to the incorrect customer or address? How many orders were damaged by the shipping company? How many orders did not arrive at the customer’s site on time due to shipping problems, and how can those problems be avoided in the future? Nina, Donna, Jessi, Peter, Tony, and Bruce are each leveraging this book to understand and apply practical analytics to help them answer their questions and meet their challenges at GB. SUMMARY Data analytics entails gathering data from various sources and making them usable for analysis. The cleaned-up data then need to be loaded into a data storage structure where we can manipulate them to provide insights that assist us in the decision-making process. The demand for individuals with the skills to perform quality data analysis is high and continues to grow as the volumes of data continue to proliferate and organizations increasingly recognize that data analysis is crucial for them to flourish in today’s environment. Although this book focuses on business applications of data analytics, data analytics is used in a broad number of fields. This chapter highlighted the analytics process, which involves a 10-step cycle that begins with goal setting, but never actually ends because the improvements derived from the results of the analysis process feed back into establishing new system or process goals. The chapter concluded by introducing you to Global Bike and some of its employees: Nina, Donna, Jessi, Peter, Tony, and Bruce. You will encounter GB and these employees throughout the text. 1 Design thinking is a user-centric methodology for problem solving and innovation. For more information on design thinking you may wish to refer to https://www.ideou.com/pages/design- thinking. Design Thinking is taught at universities throughout the world. Here are two of them: https://hpi.de/en/school-of-design-thinking/design-thinking.html and https://mitsloan.mit.edu/ideas- made-to-matter/design-thinking-explained. This eBook is licensed to Aisha Alnaemi, [email protected]