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This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. Data visualization involves presenting the data visually or graphically to detect patterns, trends, and correlations that are not usually apparent from the raw data. The trends and the patterns in the data cannot be recognized and they go undetected if not in the visual form. Data visualization is an integral part of business intelligence (BI). Most of the BI application software heavily emphasize on data visualiza­tion and have strong data visualization capabilities. One of the reasons for the popularity of visualization tools is that they are easier to use and comprehend and do not require extensive training as in the case of sta­tistical software.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. The previous chapters in this book presented numerous graphical displays created from both variables and attributes data that are powerful visualization tools. One of the major objectives of data visualization is to reveal the essential characteristics of data that may not be apparent otherwise. Data visualization makes complex and large data understandable. Visualization with big data is becoming a requirement because of the increase in the volume of data being collected and stored. The term data analytics is often used with big data. It is the science of analyzing vast amount of data to draw meaningful conclusions and make better business decisions. Big data presents numerous challenges in storing, analyzing, processing, and communicating the huge amount of data.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. This chapter deals with the basic statistical tools of analytics. The primary objective of this chapter is to enable you to master the techniques of describing data using numerical methods, perform analyses and use these methods to compare and draw meaningful conclusions from data. All these techniques use computer software in real world.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. This chapter summarizes the preceding material, provides some insights related to implementation, and offers a brief introduction to the second volume.

Facing increased customer service scrutiny in the airline industry, JetBlue, in August 2017, announced a partnership with Gladly, a relatively new entrant in the customer service software market. JetBlue was going beyond simply using Gladly's software. The company decided to invest in Gladly through its venture capital arm, JetBlue Technology Ventures. Frankie Littleford, JetBlue's Vice President of Customer Support, stated, ""We are fortunate enough to be working so closely with Gladly that we are co-creating this [platform]together." Bonny Simi, president of JetBlue Technology Ventures group, was enthusiastic about a potential "onmichannel" approach to communicating with customers. "People just don't want to call in anymore. So we are aiming for omnichannel communication that is on at all hours, that can take advantage of AI (artificial intelligence) to resolve customers' issues as quickly as possible, and that will work with all of the important messenger apps." Picking the right customer-service platform and then rolling it out without disrupting current operations were no small tasks for any airline, but JetBlue was committed to make it simpler for its customers to communicate with the company.

Rick Stein gazed out at Propay Ltd's car park where his fifteen-year-old car now languished.A year ago ProPay's previous owner parked his Maserati in the exact same spot - a contrast that seemed oddly symbolic of Rick's current predicament. He had bought the payments gateway business after raising a Search Fund from a number of private investors and with a plan to improve what he perceived to be an 'undermanaged' business. A few months later it had become apparent that more funding was needed. He returned to his financial model to consider his position...

Rick Stein is faced with taking rapid action to both save his business and at the same time take advantage of a surprise exit opportunity. As usual there's a cash squeeze but should the business take more money and if so where from and on what terms?

The case examines two major transitions currently taking place in Hong Kong-the transformation into a knowledge-based economy (KBE) and the replacement of the Gen Xers by the Gen Ys in the workplace. It sheds light on the skills that workers require and the strategies that government and industries should adopt to suitably develop a KBE. In doing so, the case elaborates on the policy initiatives Hong Kong has undertaken to create the desired human capital and to leverage innovation and technology across knowledge-based industries. It tabulates the key statistics and trends that enable manpower planning and projections. While the case presents issues arising in the workplace due to intergenerational differences between Gen X and Gen Y, it questions how Gen Ys can effectively replace the retiring baby boomers and aging Gen X workers. It aims to arouse the attention of human resource (HR) managers in coming up with the appropriate strategies to incorporate and motivate the Gen Ys to fill the gap. In the end, can both transitions be successful and sustained? Can the new generation help transform Hong Kong into a KBE?

The Azim Premji Trust, among the largest philanthropic trusts in India, had its origins in 2001, when Azim Premji transferred Wipro shares worth US$ 125 million to the trust. As of March 31, 2017, the trust had a corpus fund of US$ 9 billion. The trust's goal was to support Premji's philanthropic pursuits through two organizations -- the Azim Premji Foundation and Azim Premji Philanthropic Initiatives. Both beneficiaries had distinct, ambitious philanthropic objectives that required large, ongoing funding. The trust's Chief Endowment Officer, K. R. Lakshminarayana, had been given the responsibility of planning the future of one of India's first endowments. The endowment was tasked with maximizing total return over a long horizon. Therefore, the trust had deliberately been created as a taxable entity to allow it the freedom to make large investments in equities and alternatives. The case describes the challenges Lakshminarayana, widely known as Lan, faced in arriving at a strategic asset allocation model in an emerging market with limited investment talent and investment firms and constraints on the trust's ability to invest outside India.

The case is about a decision problem facing James on whether or not to invest in a structured product called the "CMS Steepener" issued by a large US investment bank. The payoff from the product is linked to two constant maturity swap (CMS) rates, and the investor profits if the difference between the two CMS rates increases, or alternatively if the CMS curve steepens. The case describes the risks that investing in such a product poses, and presents relevant data on the CMS rates, term structure and recent financial history of the issuer to help resolve James's decision problem.

Discussion questions for case BAB372.

Student spreadsheet for case W17776.

In December 2016, the chief executive officer (CEO) of Château Ksara, the largest and most renowned wine producer in Lebanon, was analyzing the company's sales volume and product availability around Lebanon. To the CEO's surprise, a popular fine dining establishment in the city did not include any of Château Ksara's wines. Frustrated, he started wondering what could have been the reasons for the omission. Was the quality of wine giving competitors an edge in selling their brands? What position did Château Ksara currently hold within the wine brand landscape in its own country? How could Château Ksara add value to, and maintain profitable relationships with, its distribution channel partners?

In late 2014, the deputy minister of Ukraine's Ministry of Defence joined the public service, six months after Ukraine's revolution and change of government. He joined the ministry as part of the new government's attempt to reform a bureaucracy crippled by corruption, inefficiencies, and dysfunction. In six months, he had already made notable changes. However, by March 2015, the changes were not resulting in better outcomes where they were urgently needed-in the eastern regions, supporting a tactically and logistically crippled military that was resisting Russian attempts at incursion. What more could the deputy minister do to initiate and lead change?

Santé Au Naturel was a natural health boutique in Montreal's downtown core. The company was off to a strong start in a growing market, but as its best-selling products became more mainstream, large retailers were entering the market, stealing customers, and driving down margins. In early 2017, Santé Au Naturel's owner reflected on further differentiation of her offering and targeted marketing; she wanted to improve her marketing mix to grow sales and protect margins.

The founder and chief executive officer of Twenty One Toys Inc., a Canadian designer and manufacturer of educational toy sets, was considering her firm's future in 2017. The company had doubled its revenues each year for the past two years with its first toy-and, thus far, its only product-the Empathy Toy. The entrepreneurial founder had defied the odds, gaining recognition for her product and securing funds from socially focused awards and firms. She had built up a supply chain, starting with ethically manufactured toys, and had sold her products both directly to schools and through distributors to the wider retail market. The biggest current challenge for the firm was determining its growth strategy for the Empathy Toy and for its second product, the Failure Toy.

Sumith Dutta was the founder of Emerald Trail (ET), a resort near a well-known tourist destination in the Himalayas, which treated resort guests' pets as no less important than their owners. Dutta's passion for pets inspired him to offer this experience, which was different from what was offered at other resorts. The case details the decisions Dutta made in executing his strategy and invites students to consider how Dutta could have scaled up his business in 2017: Should he have invested in upgrades at the existing location or should he have opened a new resort in another location in India? If he opened in a new location, could he replicate his existing model? In either case, is he financially prepared for the growth?

Campbell siblings Thomas and Sally are faced with selling their childhood home. They need to make several difficult consequential decisions, all the while navigating their contentious relationship. Did it make sense to hire a broker, or should they go it alone? How much was the home worth? What should the listing price be? Most importantly, what combination of answers would get them the best outcome? And along the way, as they gather information to answer these questions, several unforeseen events occur.

Campbell siblings Thomas and Sally are faced with selling their childhood home. They need to make several difficult consequential decisions, all the while navigating their contentious relationship. Did it make sense to hire a broker, or should they go it alone? How much was the home worth? What should the listing price be? Most importantly, what combination of answers would get them the best outcome? And along the way, as they gather information to answer these questions, several unforeseen events occur.

Campbell siblings Thomas and Sally are faced with selling their childhood home. They need to make several difficult consequential decisions, all the while navigating their contentious relationship. Did it make sense to hire a broker, or should they go it alone? How much was the home worth? What should the listing price be? Most importantly, what combination of answers would get them the best outcome? And along the way, as they gather information to answer these questions, several unforeseen events occur.