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Many companies are making big bets on the metaverse (even though most people still aren't quite sure what it is). Three new books help explain why.

The case is set in May 2021 and talks about a Singapore-based small-medium enterprise decision science company called Blackbox (BB) and how it used conversational AI, human-centered design, and a technology innovation partner to design and develop a chatbot that helped its clients understand data insights with the language of everyday conversations. BB generated vast data through opinion surveys and complex market analyses for its clients. While dashboards and reports provided a glimpse into that data, these tools did not necessarily help clients make immediate data-driven business decisions. Analysts would spend time on the phone answering simple and often repetitive questions to explain the key findings. BB decided to build a chatbot that could handle natural language queries independently to reduce the time of live support calls and allow even non-specialized users to access data insights directly. The CEO, David Black, and his team, led by Chief Operating Officer, Saurabh Sardana, embarked on designing a conversational data platform to provide improved services to clients at scale. However, how could BB execute this idea efficiently without an internal technical team specializing in building chatbots? How could the company avoid the pitfalls of typical commercial chatbots that fail to engage users? How could Black and his team design a conversational data platform that efficiently imitated human-to-human conversations, which clients could use independently? Could this new platform help the company become a differentiator in the market?

Infosys Consulting was known for delivering innovative consulting services to some of the world's most complex companies. Launched in 2004 as a separate subsidiary, Infosys Consulting was integrated into a new business unit within its parent company in 2011. This case study examines strategic and operational decisions the global leader in consulting and technology faced in growing the increasingly commoditized-and competitive-systems integration and IT outsourcing business. This path involved several bumps in the road, to be sure, and achieving a healthy growth trajectory would require careful decisions about everything from leadership to client relationships to company governance. And it would take a new global model primed to capture the massive opportunities at the intersection of strategy and technology.

Throughout 2021, excitement about the metaverse as the next generation of the internet experience had been building steadily. The buzz reached a fever pitch in October 2021, when the CEO of Facebook, Mark Zuckerberg, announced that the company was changing its name to Meta and that it would spend $10 billion to build "the metaverse." Nike had been exploring opportunities in this new virtual environment with several experimental probes, including its purchase of a virtual sneaker company called RTFKT, partnerships with game platforms Roblox and Fortnite, the creation of blockchain-based digital sneakers called CryptoKicks, and moves to protect its brands and logos by filing for trademarks for its virtual sneakers and logos. In its early metaverse initiatives, Nike had employed a "probe and learn" strategy to evaluate the metaverse as a new channel for customer engagement and to create new digital revenue streams. However, it was now time for Nike to define a cohesive metaverse strategy that would help drive its business goals. To do this, Nike's digital team needed to understand how each of the metaverse initiatives would help Nike to grow revenue, build brands, and promote its thought-leadership position as an innovator. The team then needed to prioritize these initiatives by assessing their risks, rewards, and reversibility. Finally, it needed to define a roadmap to scale and enhance each initiative. In charting its course in the metaverse, Nike also needed to consider whether the timing was right to place big bets on metaverse platforms and assets, given the high level of uncertainty about the evolution of this emerging space.

The COVID-19 crisis has fundamentally changed how many businesses operate and connect with their customers. Previously unheard-of government restrictions and sheltering-in-place requirements forced most professional services to transition to remote delivery methods (e.g., email, telephone, video consults, Shopify storefronts). Providers of low-touch services (e.g., lawyers, accountants) naturally lent themselves to remote delivery; however, those that offer high-touch services, particularly those in healthcare (e.g., doctors, chiropractors, physical therapists), experienced a drastic change in working conditions when going virtual. Despite a long history of resistance to virtual delivery, the pandemic created an unprecedented incentive for these high-touch professionals to experiment with underutilized care models such as telehealth: the provision of healthcare services remotely using telecommunications technologies. We examine the rapid adoption of telehealth during COVID-19 through the coming together or convergence of previously unrelated technologies, spaces, and practices. Our analysis reveals opportunities and challenges associated with going hands-off that apply to many other professionals providing high-trust services. Specifically, we offer nine guiding principles for building and protecting cognitive and affective trust in virtual and hybrid delivery models. This is important given the pace of compounding technology convergences that lie ahead for service professionals.

While few people think about it, underlying every application of computers in organizations today is math, calculated using binary digits or 'bits.' The original computers of the 1950s could perform about 465 multiplications per second, and today's computers are billions of times faster. However, there is an important class of arithmetic problems that remain out of reach for classical computers: Large-scale 'combinatorics' calculations. Combinatorics problems ask the question, 'How many ways can this set of objects be combined?' Such problems can also ask whether a certain combination is possible, or what combinations of objects are 'best' by some defined metric. The authors argue that a surprising number of practical organizational problems can be viewed as combinatorics problems and describe how fields from finance to chemical engineering and cybersecurity will be impacted by the emergence of quantum computers.

The case tells the story of a project at Frontelco (a major telco company in disguise) aiming at defining and piloting a business model (Network as a Service, NaaS) in response to the advancement of a new technology (5G). It is written from the perspective of a "trusted advisor" who had been invited by the protagonist to provide methodological support to the project team. The team, which primarily represents the perspective of product management, spends significant time and effort on developing a methodical approach to their own work, leaving the substantive issue (business model innovation) to be defined only vaguely, under the dominant influence of a few team members. In the follow up to the case we learn that by the time concerns emerge that the team does not seem to have developed any presentable output, they find out that their key competitor has already made significant progress and the technology department, their "internal rival", has also moved ahead with a concept that allows them to claim ownership for a major corporate-level project.

When political discussions enter the workplace, partisan disagreements can lead to a range of issues, including stereotyping, bias, discrimination, and animosity among coworkers. But providing guidelines for civil political discourse is often not enough to prevent uncivil behavior. The author offers five recommendations to help managers avoid the damage to workplace relationships and culture that can result from ideological disagreements.

Google Cloud Platform (GCP) offers a popular cloud computing resource for writing Structured Query Language (SQL) queries. The resource is called BigQuery. This note provides step-by-step information about accessing BigQuery and using it to upload and explore data. It also includes a quick-start guide.

This case describes multiple open banking initiatives made by China Construction Bank, ShenZhen (CCB-SZ) between 2016 and 2018 in an effort to remain competitive in the retail customer market against online financial services provided by big tech companies such as BATJ (Baidu, Alibaba, Tencent, and JingDong), in China. In particular, CCB-SZ was facing severe competition from mobile payment technologies provided by Alipay and WeChat Pay in individuals' and SMEs' payment markets, which were traditionally underserved by incumbent banks. With new regulations enacted in 2016 to encourage incumbent banks to provide online financial services to individuals and SMEs, especially through mobile phones, CCB-SZ made a number of efforts to regain some of the lost territory, including utilizing its long-term relationship with large enterprises as a bridge to reach their customers, collaborating with existing and popular apps, and being creative in terms of offering innovative payment services and redesigning the corresponding business process. Its open banking strategy was to break down its financial services into smaller units and embed them into customers' daily financial needs, wherever and whenever they arose. Such efforts put CCB-SZ back on the playing field of mobile payment, as it was able to catch up with nonbank mobile payment solutions and maintain a similar growth rate after 2016. However, the nonbank payment providers had multiple advantages over incumbent banks, such as better technology, more data, a larger customer base, and less regulation. Hence, the future of incumbent banks' open banking strategy remained unclear.

After initial success, China Construction Bank, Shen Zhen (CCB-SZ) continued its open banking journey by building a more integrated open banking platform in 2019. The platform enabled integration of e-accounts, consolidated payments, contactless payments, a credit card manager, a fee calculator, and other management functions for its open banking projects. CCB-SZ also continued expanding into more service areas, including smart city, smart medical platform, consumer rights, supply chain loans, SMEs quick loan approval, etc. Despite these efforts and achievements, the overall development of open banking projects by CCB-SZ was not able to maintain an upward trajectory, partly due to the regulatory limits that PBOC (the People's Bank of China or central bank) placed on the e-accounts that incumbent banks are allowed to offer to their customers. Meanwhile, BATJ (Baidu, Alibaba, Tencent, and JingDong) and fintech companies continued advancing into the financial sector. To secure its position in the mobile payment markets, CCB actively sought out other strategies such as creating separate entities that focused on fintech; digitalization of the entire backend operation; and cultivating technical manpower. These efforts allowed CCB-SZ to respond quickly to the COVID-19 pandemic by offering many new financial services online, such as "green pass quick loan", "smart community", and "grocery shopping."

Mark Zuckerberg's brave new world sounds cool-but regulators need to recognize it is being designed by the Facebook culture responsible for creating major social issues

This module note provides an overview of exploratory data analysis for an introduction to data science course. It begins by defining the term "data", and then describes the different types of data that companies work with (structured v. unstructured, categorical v. numeric, etc.). Next, the note describes the basic summary statistics that firms use to track key business outcomes. Finally, the note provides an overview of different visualizations. An appendix is provided, which includes the R code for creating all of the figures and visualizations shown in the note.

This note provides an overview of statistical inference for an introductory data science course. First, the note discusses samples and populations. Next the note describes how to calculate confidence intervals for means and proportions. Then it walks through the logic of hypothesis testing and the interpretation of p-values (in the context of two-sample hypothesis testing for means and proportions). The appendix of the note contains R code for all of these topics.

This note provides an overview of linear regression for an introductory data science course. It begins with a discussion of correlation, and explains why correlation does not necessarily imply causation. The note then describes the method of least squares , and how to interpret the r-squared and model coefficient values of a simple linear regression model. Next, the note describes how the interpretation of a model coefficient changes when there are multiple independent variables in the model. Finally, the note explains how to interpret the coefficients on dummy variables in a regression model. The appendix includes R code for implementing all of these topics.

This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional statistical modeling and machine learning. Next, the note covers two models used for classification, logistic regression and decision trees. After introducing these two models, the note explains how train, validation, and holdout sets (and k-fold cross validation) are used to tune and evaluate different models. Finally, the note concludes with a discussion of different performance metrics (ROC cruves, confusion matrices, log loss) that are used to evaluate classification models.

Contextually effective leadership comprises three essential tasks drawing the map, establishing the mindset, and communicating the message that drive desired change. Each task will vary according to the type of change that the leader seeks to achieve. The authors describe three main tasks (map, mindset, and message) that are key to leaders' contextual effectiveness and illustrate how the objective of those tasks varies according to the type of change that the leader is trying to achieve.

In December 2021, Unilever-one of the world's largest producers of consumer goods-was in the midst of a pilot project to digitize its manufacturing facilities and enable remote work for factory employees. This was possible because of an earlier project to retrofit a facility in Brazil with state-of-the art sensors on factory equipment to collect real-time data. The data was then analyzed using machine learning applications on the cloud so that key capabilities of the factory could be run remotely on a laptop in a technician's kitchen. The company hoped expanding this effort would improve efficiency and performance across its network of factories, and result in cost savings and decreased energy consumption for the entire organization. The company's next step was to explore the possibility of further digitizing factory operations with the creation of a global virtual operations room, where operators working remotely could ultimately oversee production at all of its 200 manufacturing sites around the world.

Benchmarking and executive expertise don't necessarily determine the best key performance indicators (KPIs). Data-driven companies increasingly employ predictive analytics such as machine learning to help identify and refine key strategic measures. More finely tuned KPI measures lead to behaviors that are better aligned with strategic objectives.