Iconoclasts with strongly held beliefs and a willingness to buck orthodoxy, Apple's Steve Jobs and the Chicago Bulls' Phil Jackson shared similarities in character, leadership style, and life experience that extended beyond the extraordinary successes they achieved in their chosen fields. Both came of age in the United States of the mid-20th century, a time and place characterized by Americans' growing interest in philosophical traditions outside of the Western mainstream. In their teens and twenties, both men became interested in Eastern religious practices. As young men, Jobs (who would as a teenager travel to India to study Buddhism) and Jackson (whose interest in Eastern practices earned him the nickname "The Zen Master") developed a particular affinity for the Zen tradition of Buddhism, which had a lasting influence on each man's worldview. Although Jackson and Jobs were very different leaders operating in remarkably different industries, both men's rise to the top of intensively competitive fields were influenced by their commitment to three Zen principles: nondualism, practical wisdom, and inherent enlightenment. This note gives a brief overview of the history of Zen and the philosophical framework that underpins it, then describes the potential that these three tenets hold for unlocking strategic insights.

Away, a direct-to-consumer, digital native e-commerce seller of travel luggage, is debating how to invest its latest round of venture funding. How quickly could and should Away scale and what were the most promising growth trajectories to maximize its potential? Three decisions face the founders. Should Away continue to invest in driving growth in suitcases and other travel bags or was it time to begin to expand into other adjacent travel categories? How should they use the results of the company's first customer segmentation study to select target segments and quantify their growth aspirations? What were the right distribution strategies moving forward following a series of pilots that included company-owned stores, temporary airport kiosks, and pop-up experiences with retailer partners?

Jason Calacanis described himself as a serial entrepreneur, angel investor, and podcaster - and someone who had come a long way from the time he had to talk his way into Fordham University. Later, when finances were tight, he talked the dean into getting the bursar to cut him a break. These early experiences with "being bold," he believed, encouraged him to keep being bold - and creative. This case study explores Calacanis' path to entrepreneurship, a route that took him first into publishing, and later into angel investing, then hosting influential incubator and networking events for investors and entrepreneurs. Throughout all of these activities there was a strong creative thread - Calacanis events were not just about connecting investors and entrepreneurs, but also provided entertainment.

When Anders Eriksson was appointed CEO of Bonnier News in June 2016, he took the helm of a storied Swedish media business in the midst of a momentous transformation. Bonnier News included three of Sweden's most important newspapers-Dagens Nyheter (DN), the national paper of record; Dagens Industri (DI), the top business daily; and Expressen, a tabloid that reached roughly half of the country's population-and the southern Swedish daily HD-Sydsvenskan. But the digital age had presented multiple challenges to each of these entities, and to media enterprises worldwide, and Bonnier had grappled with its response to the changing environment. It was Eriksson's task to set Bonnier News on a growth trajectory that would eventually lead to take-off. This case explores Bonnier's restructuring; group level strategic leadership; development of group-level digital competencies; and business-level strategies.

This case describes the transformation of the UK's oldest financial services franchise from a product-aligned and brick-and-mortar traditional banking institution to a customer-aligned and digital-first technology organization. The case parallels Lloyds' business transformation with the growth of its digital-focused Transformation division - which commenced as a small hundred-person task force and ultimately grew to comprise one third of the bank's 70,000 employees.

Grupo Éxito, a leading South American retailer, faced declining market shares in Colombia in 2019 with the arrival of low-cost competitors and emerging digital trends. Originally founded in Medellín, Éxito had over the course of its seventy-year history evolved from a small textile shop into a retail powerhouse, with 1,533 stores and operations in 2018 across four countries in South America and $18.6 billion in consolidated revenues. In its home country, Éxito had grown through a series of acquisitions, consolidating a multi-brand and multi-store format strategy to cater to customers across all income segments. By 2018, the company had a total of 554 points of sale across the country. However, with the arrival of hard discount retailers, Éxito began to gradually experience a decline in its market shares, particularly at its low-cost store brands. Colombian consumers were also changing their grocery shopping habits, as they became more digitalized. Not only were retailers beefing up their digital retail platforms by launching new applications with personalized services, but they were also allying with partners such as Rappi-Colombia's largest last-mile delivery company- to speedily deliver products to customers' homes. Carlos Mario Giraldo, Éxito´s Chief Executive Officer since 2013, and top management were faced with the challenge of simultaneously maintaining Éxito´s vast operations in the country and battle the current and upcoming threats in the competitive Colombian grocery retail market. Should the company battle head to head with discounters or bet on differentiated strategies at its more premium stores and digitalized ways to serve customers? Would these strategies pay off and allow Éxito to gain more market share in the future?

The overarching vision of the global nongovernmental organization Transparency International was to free the world from corruption. Transparency International Ukraine (TIU) was founded in 2014, after three failed attempts, to tackle the high level of corruption that had taken hold of that country. Over the first two years, the chief executive officer faced significant organizational issues that hindered the organization’s performance; he appointed an external hire as chief operating officer to help facilitate a transition to a new, more professional team in Kyiv and to turn the organization around. As the two seasoned professionals worked collaboratively to revamp the organization, they realized they faced challenging external and internal pressures. Not only were they tackling highly corrupt public officers and colluding oligarchic elites, they were also faced with alienated volunteers, who questioned their ability to transform TIU. In 2017, they had made some progress but still faced many barriers. How could they reinvent the organization without risking burnout?

In October 2016, the board of Tata Sons, a company in the Tata Group, India’s largest business group, decided to remove Cyrus Mistry from the chairmanship of its board. Subsequently, Tata Sons appealed to the shareholders of the other Tata Group companies, including Tata Steel Limited, to also remove Mistry from their boards. The shareholders of Tata Steel were informed that an extraordinary general meeting had been called to vote on two resolutions that would decide whether Mistry and another independent board member should continue as directors of Tata Steel or be removed from their positions. How could the shareholders make a thoughtful and forward-thinking decision on the two resolutions?

Spreadsheet to accompany product 9B19N016.

On October 7, 2019, the Shiseido Group announced that it would acquire clean skincare brand Drunk Elephant for $845 million, a valuation of 8.5 times sales. Did Shiseido pay too much or too little for this brand asset? Every acquisition had to be measured against the cost of developing and building a look-a-like brand internally. Would Drunk Elephant prove to be an integral part of Shiseido's future success and what would it take to unlock its full brand value? Would Drunk Elephant thrive under Shiseido's management? How much further investment would Shiseido need to make to realize the new brand's potential?

In early 2019, arc Thrift Stores was supporting 14 Colorado chapters of the Arc, an organization that advocated for children with intellectual and developmental disabilities (I/DD) in schools, helped individuals with I/DD live more independently, and supported legislation regarding their rights all across the state. The chief executive officer and marketing director of arc Thrift Stores were reviewing a survey they hoped would help them understand arc Thrift Stores' customers and broaden the interest in arc Thrift Stores among younger generations. By all measures, arc Thrift Stores was very profitable, but its core audience was aging, and the team wanted to ensure that it continued the successes of its past 50 years into the next 50 years. The company wanted people to understand the value of shopping their stores and the quality of items they could expect to find there. How could the company make inroads with a younger demographic? Should it focus more on sustainability? Should it consider moving into the e-commerce space?

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 1 gives an overview of the field of business analytics, which uses data, statistical analysis, mathematical and statistical modeling, data mining, and advanced analytics tools to explore, investigate, and understand business performance. The tools of business analytics give insight to and drive business planning and decisions. Descriptive analytics tools use statistical, graphical, and numerical methods to understand the occurrence of certain business phenomena; predictive analytics tools are used to predict future business phenomena; prescriptive analytics tools have applications in optimizing and automating business processes. Business analytics helps with informed decision-making in business and can be used to automate and optimize business processes. Data is an asset and can be leveraged for competitive advantage. Successful business analytics depends on data quality and skilled analysts. Business analytics is an organizational commitment to data-driven decision-making.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter offers an overview of business analytics and business intelligence and outlines the similarities and differences between the two. Business analytics, types of analytics, and the analytics process are all explained. Different tools used in each type of analytics are discussed as well as their relationship and interconnection. These tools overlap in applications and a combination of these tools can be used in many situations. In comparing business analytics and business intelligence, business analytics, data, analytics, and advanced analytics all fall under the wide scope of business intelligence. Business intelligence tools include the business analytics tools but also include data mining, online analytical processing, and business performance management among other things.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 3 builds on chapter 2, discussing how analytics, business analytics, and data analytics all fit into business intelligence and going further into the comparison between business analytics and business intelligence. Business intelligence is a technology-driven process for processing and analyzing data to make sense from huge quantities of data that businesses collect and obtain from various sources. It is both visualization and analytics. Various functions and applications of business intelligence are described, including but not limited to reporting, online analytical processing, and business process management. Business intelligence can combine external and internal data in order to create an "intelligence" that cannot be derived by any one set of data. Business intelligence along with business analytics can empower organizations and give them a better understanding of the existing markets and customer behavior.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter provides a more in-depth look at descriptive analytics. Descriptive analytics tools are used to understand the occurrence of certain business phenomena or outcomes and explaining these outcomes through graphical, quantitative, and numerical analyses. A case is given to demonstrate the tools and applications of visual techniques used in descriptive analytics. Descriptive analytics is critical in studying the current state of a business and in learning what has happened in the past using company data. Knowledge derived from descriptive analytics lays a foundation for further analysis and leads to predictive analytics.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter starts with a discussion about what predictive analytics is and how it differs from descriptive analytics. While descriptive analytics is the process of exploring and explaining data to reveal trends, predictive analytics is about predicting future business outcomes. It uses several models that can be categorized into logic-driven models and data-driven models. A number of tools and statistical models are introduced, which are considered prerequisites to predictive analytics. Descriptions and application areas of these prerequisite tools - including probability concepts, probability distributions, and correlation analysis - are given. The understanding of these prerequisite tools is critical in understanding and applying predictive analytics models.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 6 explains key models used in predictive modeling. These models are the core of predictive analytics and are used to predict future business outcomes. Explanations and application areas of each tool are given. The tools described include various types of regression models, forecasting models, ANOVA, data mining, machine learning, artificial intelligence, neural networks, and deep learning.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 7 gives an introduction to regression and correlation analysis. Regression techniques allow us to explore the relationship between different variables. Essentials to developing regression models are explained, including dependent and independent variables, the least squares method, determining the best-fitting line through the data points, calculating the slope and y-intercept of the best fitting regression line, interpreting the meaning of the regression line, and measures of association between the covariance and the correlation coefficient. Developing regression models to predict one variable using the other variable or multiple variables is also discussed. Successful implementation of regression models requires an understanding of different types of models. A lack of understanding may lead to drawing wrong conclusions and selecting alternative courses of action.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter discusses forecasting and time series analysis, both major tools of predictive analytics. Forecasting deals with predicting future business outcomes using quantitative and qualitative methods and is used to make informed decisions. Many business planning production, operations, sales, demand, and inventory decisions are based on forecasting. Produce-to-order and produce-to-stock companies rely on forecasting for production and operations planning. Companies with good forecasting are able to balance demand and supply. The general meaning of forecasting applications and several models-including the simple moving average, weighted moving average, and exponential smoothing-are discussed as well as measuring forecast accuracy.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 9 provides an introduction to data mining and its applications to predictive analysis. Data mining explores patterns and relationships in the vast amounts of data that businesses collect. The information that is extracted is used to predict future business outcomes. Several data mining methodologies and tasks are described, including data preparation, knowledge representation, pattern evaluation, and descriptive and predictive data mining. Data mining techniques can also be categorized into supervised and unsupervised learning. Key methodologies of data mining include anomalies detection, association learning, classification, clustering, sequence, prediction, and time series and forecasting. The growing application areas of data mining are discussed, including applications in machine learning. The relationship between data mining and machine learning as well as different types of machine learning are also introduced.