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Glenda Crisp became the chief data officer of TD Bank Group in October 2015. The Office of the chief data officer had only been established in 2013; the scope of the department still needed to be fully defined, and the original enterprise data governance policies needed to be updated. Crisp saw issues in a variety of areas, such as organizational structure, the processes used to manage data governance practices, and technology, all of which needed to be identified and prioritized. An implementation plan then needed to be developed.

In the summer of 2017, after completing his third year of business school, a student was eager to apply his newly developed business and technology skill sets at his father's company during his four-month summer position. His father's business, Custom Closet Contractors, designed and manufactured custom home organization solutions for home builders and individual clients. In the past year, the successful company's sales growth had slowed, while expenses as a percentage of sales had increased. The company had experienced operational problems leading to higher expenses, and the student had found that part of the problem was the lack of data organization, which led to miscommunication between the front office and the production staff. To solve the problem, he proposed to design a centralized database and set to work trying to understand the business processes in order to design a data model.

GasBuddy is a 15-year-old mobile app-led company, providing gas station and pricing information to drivers and selling ads to fuel stations and convenience store owners. Though the GasBuddy Mobile App has received many awards and often ranks among the top-three travel apps in downloads, competitive pressure is building. Recognizing the competitive shifts, its parent company, UCG, brings in a new management team that is steeped in start-up experience spanning payments, convenience stores, social and mobile media, digital transformation, point of sale systems, modern technology platforms, agile development, and UI/UX. The new team recognizes that speed as well as agility will be essential for future growth. The case takes readers through the company's cultural, digital platform, and product line changes, including a bold entry into the payments industry. The changes support the company's evolved branding and a new multi-sided platform (MSP) business strategy. Early changes across the three areas - culture, digital platform, and product line - are put to the test during highly destructive Hurricanes Harvey and Irma.

From security cameras to GPS tracking systems, nearly 80% of organizations use some type of electronic performance monitoring (EPM). EPM uses technology to gather, store, analyze, and report employee behavior data to assess performance and observe actions on the job (i.e., productivity, use of company time, incivility). The objective, real-time data that EPM systems collect can be used for performance appraisal, training and development, logistical tracking, wellness programs, employee safety, and more. Despite the organizational benefits of EPM, these systems can have adverse effects on employee satisfaction, organizational commitment, fairness perceptions, and employee behavior. Research provides evidence, however, that these downfalls can be mitigated by implementing these systems with employee attitudes and privacy perceptions in mind. Using theory and empirical research evidence, we offer five recommendations for maximizing the positive effects and minimizing the negative effects of EPM: (1) Be transparent with employees about EPM use, (2) be aware of all potential employee reactions to being monitored, (3) use EPM for learning and development rather than deterrence, (4) restrict EPM to only work-related behaviors, and (5) consider organizational makeup when implementing an EPM system.

Growing up with the internet and unparalleled access to technology, millennials (individuals born during 1981-1995, also known as Gen Y and Gen Me) extensively use various technologies for non-work-related reasons while at work. Both popular media and scholarly research have portrayed this issue negatively and have supported monitoring and restricting personal use of technology. However, if organizations are to attract and retain millennials-now the largest generation in the U.S. workforce-it is crucial to understand their characteristics and what drives them. Drawing on research on generational differences, organizational control, and cyberloafing, this article explains how unique characteristics of millennials lead them to engage in personal use of technology at work and how organizations might address this issue. Specifically, I contrast two one-sided approaches (deterrence and laissez-faire) that can lead to dysfunctional outcomes when used in excess and recommend more viable solutions. These solutions include establishing a workplace technology use policy based on shared understanding, fostering both relaxation and urgency mentalities, and training both millennials and their managers.

On June 25, 2014, the director of marketing and development at Allied Founders Pvt. Ltd., a company that operated out of Belgaum in India, was analyzing various problems associated with his company's management approach. The director was considering a change from the company's system of breakdown maintenance to a new information system of preventive maintenance. While executing its first international export order, the company faced several challenges related to production delays, mainly due to poor equipment maintenance. To plan for his company's future in the international market, the director proposed adopting preventive maintenance as a solution, but he was unsure whether to use traditional means (i.e., manual record keeping) or implement a new information system. He was concerned about the issues that could arise from the change, including resistance from employees worried about job loss, providing the necessary training, and exposing the company to other risks.

At the end of 2012, the chief information officer (CIO) at UCB, a global pharmaceutical company based in Brussels, started to implement analytics as a service. Between 2012 and 2016, he put this vision into practice, introducing agile sprints and proving the competence of analytics within the organization, and at the beginning of 2016, he felt the company was ready to upgrade its analytics capability. As he prepared to meet with UCB's chief executive officer in March 2016, the CIO considered how to advise the board as the organization worked to make an impact with analytics and big data against the backdrop of digital turbulence in its strategic environment. How could UCB balance empowerment and bottom-up experimentation with enterprise focus and control? What was the best location for analytics roles and responsibilities within the organization?

ZBJ.com (ZBJ), an online platform that connects knowledge workers to small and medium-sized enterprises, is China's largest outsourcing platform. Founded by Mingyue Zhu in 2006, ZBJ had grown into a unicorn with 4,000 employees and a daily transaction volume of RMB15 million by 2017. During the company's 11-year history, ZBJ had launched ten transformation campaigns to overcome various challenges by changing its products, operations, and business model. For example, it encountered winner-takes-all competition, disintermediation (when service providers and clients took transactions off the platform to avoid the platform's 20% commission fees), the rise of fake transactions, member management issues, and difficulties in delivering satisfactory services to large enterprises. Zhu must decide how to use the next campaign to address challenges that had emerged since the last campaign and how ZBJ should expand globally.

In the fall of 2014, Chad Kartchner, senior manager of marketing and product management at Honeywell Aerospace (HA), pondered how technology could transform the way aircraft were maintained. He had heard a lot of buzz about cognitive analytics, an artificial intelligence term referring to the use of computer models and algorithms to simulate human thought through self-learning systems, data mining, pattern recognition, and natural language processing. The sheer volume of parts and the time-sensitive nature of repairs in the aviation industry made it complicated to identify problems and address them quickly. Kartchner contemplated the options for updating HA's ground-based maintenance system. Should he emulate HA's state-of-the-art on-board system for an entire aircraft or try something new? Emulating the on-board system, which HA developed internally, would be an easy sell to leadership given internal buy-in and satisfaction with the on-board system, but he contemplated new approaches because he did not want to overlook rapidly emerging technologies. The latter could include crowdsourced features that leveraged the abundance of knowledge among HA's customers' technicians or a cognitive analytics approach. Even if he could persuade leadership to try a new cognitive analytics approach, should HA partner with an established entity or work with a relatively unproven startup who promised lower cost, better features, and quicker turnaround to develop a new system? Students will step into the shoes of Kartchner as he leads the internal discussion on whether and how to tap into the benefits of cognitive analytic solutions for Honeywell Aerospace and its customers.

Fintech brings about a new paradigm in which information technology is driving innovation in the financial industry. Fintech is touted as a game changing, disruptive innovation capable of shaking up traditional financial markets. This article introduces a historical view of fintech and discusses the ecosystem of the fintech sector. We then discuss various fintech business models and investment types. This article illustrates the use of real options for fintech investment decisions. Finally, technical and managerial challenges for both fintech startups and traditional financial institutions are discussed.

The convergence of communications, information, entertainment, commerce, and computing-combined with a rising number of new services-has caused changes in the way people access and consume media. In recent months, the phenomenon of viewing audiences shifting wholly or partially from cable TV providers to over-the-top (OTT) media, also known as cord cutting or cord shaving respectively, has gained much attention. Despite anecdotal evidence of cord cutting and cord shaving presented in the trade press and industry conferences, there has been little rigorous examination of the true effect that cord cutting or cord shaving may have on TV networks and the media industry at large. In this article, we use behavioral data from a leading cable operator in the U.S. to identify and describe the key viewer segments. We also conduct simulations to examine the effects and implications of cord cutting and cord shaving from a CLV perspective for content providers, content distributors, and advertisers.

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. Chapter 1 provides an overview of business analytics and why these techniques are important.

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. Chapter 2 discusses analytics, business intelligence (BI) and business analytics (BA) as decision-making tools in businesses today. Although these terms are used interchangeably, there are slight differences in the terms of the tools they use. The connection between BI and BA tools combined with the applications in the computer science and information technology are critical for the success of BA in a company. This chapter discusses the broad meaning of the terms-BI, BA, data analytics (DA), computer and information technology, and how they are used in business decision making. The chapter also distinguishes between BI and BA, and the specific tools used in each case. provides an overview of business analytics and why these techniques are important.

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. Statistics is the science and art of making decision using data. Almost every field uses data and statistics to learn about systems and their processes. In fields such as business, research, health care, and engineering, a vast amount of raw data are collected and warehoused rapidly. Data and statistical tools aid in gaining skills such as: (1) Collecting, describing, analyzing, and interpreting data for intel­ligent decision-making (2) realizing that variation is an integral part of data and almost all data show variation (3) understanding the nature and pattern of variability of a phenomenon in the data, and (4) being able to measure reliability of the population parameters from which the sample data are collected to draw valid inferences.

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.

This case study focuses on the creation of SingularityNET, a blockchain-based marketplace for AI. SingularityNET was conceived by Ben Goertzel, Simone' Giacomelli and David Hanson in a series of brainstorming sessions at Hanson Robotics in Hong Kong in 2017. A key goal for creating this democratized marketplace platform was to accelerate the development of artificial general intelligence, or AGI. Companies that could not afford customizing their own AI could access AI Agents that would subcontract to and collaborate with other AI agents to create customized solutions to meet specific needs. The distributed blockchain-based network allowed for stitching together clusters of narrow AIs that could mimic sections of a brain. Collectively, these clusters could eventually function like a global brain.

This is an MIT Sloan Management Review article. Executives often look at the network effects of digital platforms as a key source of competitive advantage -without understanding that platforms need to also leverage other factors at play in the local markets and among preferred customers. Network effects can help, but on their own, they offer very limited competitive value.