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The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

Companies spend millions on training their sales representatives. Thousands of textbooks have been published; thousands of training videos have been recorded. Hundreds of good pieces of advice and tips for sales representatives have been presented along with hundreds of sales methods and techniques. Probably the largest number of indicators and measures are applied in sales and distribution. On the one hand, this is a result of the fact that sales provide revenue and profit to a company; on the other hand, the concept of management by objectives turns out to be most effective in regional sales teams with reference to sales representatives and methods of performance evaluation. As a result, a whole array of indices has been created which enable the evaluation of sales representatives’ work and make it possible to manage goods distribution in a better way.

The indices presented in this chapter are rooted in the consumer market and are applied most often to this type of market (particularly in relation to fast-moving consumer goods at the level of retail trade). Nevertheless, many of them can be used on other markets (services, means of production) and at other trade levels (wholesale).

Although the values of many indices presented herein are usually calculated by market research agencies and delivered to companies in the form of synthetic results, we have placed the emphasis on the ability to determine them independently, both in descriptive and exemplifying terms. We consider it important to understand the genesis of indices and build the ability to interpret them on that basis. What is significant is that the indices can be interpreted differently; the same index may provide a different assessment of a product’s, brand or company’s position in the market depending on the parameters taken into account. Therefore, we strive to show a certain way of thinking rather than give ready-made recipes and cite ‘proven’ principles. Sales and distribution are dynamic phenomena, and limiting them within the framework of ‘one proper’ interpretation would be an intellectual abuse.

Nowadays, most of our activities and personal details are recorded by one entity or another. These data are used for many applications that fundamentally enrich our lives, such as navigation systems, social networks, search engines, and health monitoring. On the darker side of data collection lie usages that can harm us and threaten our sense of privacy. Marketing, as an academic field and corporate practice, has benefited tremendously from this era of data abundance, but has concurrently heightened the risk of associated harms.

In this chapter, we discuss both the great advantages and potential harms ushered in by this era of data collection, as well as ways to mitigate the harms while maintaining the benefits. Specifically, we propose and discuss classes of potential solutions: methods for collecting less data overall, transparency of code and models, federated learning, and identity management tools, among others. Some of these solutions can be implemented now, others require a longer horizon, but all can begin through the advocacy of marketing research. We also discuss possible ways to improve on the benefits of data collection – by developing methods to assist individuals pursue their long-term goals while advocating for privacy in such pursuits.

There has been a digital transformation of the sport industry that has resulted in an increase in the number of startups. Technological innovations derived from big data and social media have altered the way entrepreneurship is embedded in a sport context. This has influenced more technologically enabled sport startups that are driving change in the global economy. This chapter discusses the role of digitalization in changing existing business models and fostering a more entrepreneurial ecosystem. This includes focusing on technological innovations such as the impact of cloud computing and other data changes.

In the age of data, enterprises have more information available to them than ever before, yet many organizations still struggle to harness its full potential. In this chapter, we explore the data value equation and how it translates into an end-to-end data management strategy that enables enterprises to turn their business data into business value. Starting with the concept of “amount,” the chapter looks at the challenge of storing big data. The second element of the equation relates to the “quality” of data and its fundamental role in enabling confident decision-making. Finally, the third element of the equation focuses on the importance of the consumption of that data in analytics tools that not only visualize the data but proactively help users uncover, explore, and act on insights. By yielding the highest value at every stage of this equation, businesses can see more, understand more, and do more with their data.

Introduction: The Internet has tremendously transformed the computer and networking world. Information reaches our fingertips and adds data to our repository within a second. Big data was initially defined as three Vs, where data come with greater variety, increasing volumes and extra velocity. Big data is a collection of structured, unstructured and semi-structured data gathered from different sources and applications. It has become the most powerful buzzword in almost all the business sectors. The real success of any industry can be counted based on how the big data is analysed, potential knowledge is discovered and productive business decisions are made. New technologies such as artificial intelligence and machine learning have added more efficiency to storing and analysing data. This big data analytics (BDA) becomes more valuable to those companies, focusing on getting insight into customer behaviour, trends and patterns. This popularity of big data has inspired insurance companies to utilise big data at their core systems and advance the financial operations, improve customer service, construct a personalised environment and take all possible measures to increase revenue and profits.

Purpose: This study aims to recognise what big data stands for in the insurance sector and how the application of BDA has opened the door for new and innovative changes in the insurance industry.

Methodology: This study describes the field of BDA in the insurance sector, discusses the benefits, outlines tools, architectural framework, the method, describes applications in general and specific and briefly discusses the opportunities and challenges.

Findings: The study concludes that BDA in insurance is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however, there remain challenges to overcome.

The study focusses on the legal issues surrounding artificial intelligence (AI), which are being investigated and debated about several European Union initiatives to manage and regulate Information and Communication Technologies. The goal is to discuss the benefits and drawbacks of adopting AI technology and the ramifications for the articulations of law and politics in democratic constitutional countries. Thus, the study aims to identify socio-legal concerns and possible solutions to protect individuals’ interests. The exploratory study is based on statutes, rules, and committee reports. The study has used news pieces, reports issued by organisations and legal websites. The study revealed computer security vulnerabilities, unfairness, bias and discrimination, and legal personhood and intellectual property issues. Issues with privacy and data protection, liability for harm, and lack of accountability will all be discussed. The vulnerability framework is utilised in this chapter to strengthen comprehension of key areas of concern and to motivate risk and impact mitigation solutions to safeguard human welfare. Given the importance of AI’s effects on weak individuals and groups as well as their legal rights, this chapter contributes to the discourse, which is essential. The chapter advances the conversation while appreciating the legal work done in AI and the fact that this sector needs constant review and flexibility. As AI technology advances, new legal challenges, vulnerabilities, and implications for data privacy will inevitably arise, necessitating increased monitoring and research.