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Introduction: Big data in the insurance industry can be defined as structured or unstructured data that can affect the rating, marketing, pricing, or underwriting. The five Vs of big data provide insurers with a valuable framework for converting their raw data into actionable information. These five Vs are specifically: (1) Volume: The need to look at the type of data and the internal systems; (2) Velocity: The speed at which big data is generated, collected, and refreshed; (3) Variety: Refers to both the structured and unstructured data; (4) Veracity: Refers to trustworthiness and confidence in data; and (5) Value: Refers to whether the data collected are good or bad.

Purpose: Insurance companies face many data challenges. However, the administration of big data has allowed insurers to acknowledge the demand of their customers and develop more personalised products. In addition, it can be used to make correct decisions about insurance operations such as risk selection and pricing.

Methodology: We do this by conducting a systematic literature review on big data. Our emphasis is on gathering information on the five Vs of the big data and the insurance market. Specifically, how big data can help in data-driven decisions.

Findings: Big data technology has created an endless series of opportunities, which have ensured a surge in its usage. It has helped businesses make the process more systematic, cost-effective, and helped in the reduction in fraud and risk prediction.

Big data and related technologies are expected to drastically change the way industrial maintenance is managed. However, at the moment, many companies are collecting large amounts of data without knowing how to systematically exploit it. It is therefore important to find new ways of evaluating and quantifying the value of data. This paper addresses the value of data-based profitability of maintenance investments.

An analytical wasted value of data model (WVD-model) is presented to quantify how the value of data can be increased through eliminating waste. The use of the model is demonstrated with a case example of a maintenance investment appraisal of an automotive parts manufacturer.

The presented model contributes to the gap between the academic research and the solutions implemented in practice in the area of value optimization. The model provides a systematic way of evaluating if the benefits of investing in maintenance data exceed the additional costs incurred. Applying the model to a case study revealed that even though the case company would need to spend more time in analyzing and processing the increased data, the investment would be profitable if even a modest share of the current asset failures could be prevented through improved data analysis.

The model is designed and developed on the principle of eliminating waste to increase value, which has not been previously extensively discussed in the context of data management.

Gives an in depth view of the strategies pursued by the world’s leading chief executive officers in an attempt to provide guidance to new chief executives of today. Considers the marketing strategies employed, together with the organizational structures used and looks at the universal concepts that can be applied to any product. Uses anecdotal evidence to formulate a number of theories which can be used to compare your company with the best in the world. Presents initial survival strategies and then looks at ways companies can broaden their boundaries through manipulation and choice. Covers a huge variety of case studies and examples together with a substantial question and answer section.

Develops an original 12‐step management of technology protocol and applies it to 51 applications which range from Du Pont’s failure in Nylon to the Single Online Trade Exchange for Auto Parts procurement by GM, Ford, Daimler‐Chrysler and Renault‐Nissan. Provides many case studies with regards to the adoption of technology and describes seven chief technology officer characteristics. Discusses common errors when companies invest in technology and considers the probabilities of success. Provides 175 questions and answers to reinforce the concepts introduced. States that this substantial journal is aimed primarily at the present and potential chief technology officer to assist their survival and success in national and international markets.

This is the second in a series of papers focused on alcohol and substance abuse rehabilitation centers. Centers face the ongoing challenge of validating outcomes to meet the burden of evidence for insurance companies. In the first paper, data mining was used to establish baseline patterns in treatment success rates, for the Futures: Palm Beach Rehabilitation Center, that have a direct impact on a client’s ability to receive insurance coverage for treatment programs. In this paper, we examine 2016 outcomes and report on facility efficacy, alumni progression and sobriety, and forecast treatment success rates (short and long term) in support of client insurability. Data collection has been standardized and includes admissions data, electronic medical records data, satisfaction survey data, post-discharge survey data, Centers for Disease Control (CDC) data, and demographic data. Clustering, partitioning, ANOVA, stepwise regression and stepwise Logistic regression are applied to the data to determine statistically significant drivers of treatment success.

This paper aims to explore the prevalence of depression, anxiety symptoms and suicidal ideation among the Romani population in Ukraine and their connections with various social health determinants: age, gender, household characteristics, employment and living conditions.

For measuring mental health conditions, GAD-7 and PHQ-9 were used. Individual interviews were conducted by trained volunteers of the International Charitable Organization “Roma Women’s Foundation Chirikli”. Data were gathered from January to March 2020.

The overall level of depression found in the sample was 8.08, while the mean for anxiety was 7.22. In general, 32.7% of respondents scored positively for signs of depression and 29.6% for anxiety. The two-week prevalence of suicidal ideations was 26.9%. Compared to the general population, the prevalence of depression among the Romani research participants was twofold higher, and anxiety was 2.5-fold higher. Signs of depression and anxiety in women were significantly higher (36% vs 28.6% for depression and 33.9% vs 24.2% for anxiety) than in men. Signs of depression and anxiety were higher for people without education than for university students (9.32 vs 3.04 for depression and 8.26 vs 3.00 for anxiety). The lowest levels of depression, anxiety and suicidal ideation were among officially married persons (6.61, 6.36 and 0.23, respectively). Significant small positive correlations were found between all measurements and the number of household members (0.149 for depression, 0.124 for suicidal ideation and 0.175 for anxiety; p < 0.001) and the number of children (0.303 for depression, 0.224 for suicidal ideation and 0.243 for anxiety; p < 0.001). In terms of employment, the highest scores for depression, anxiety and suicidal ideation were found among those who are employed seasonally (9.06, 8.25 and 0.61) or irregularly (9.09, 8.12 and 0.57) in contrast with self-employed (4.88, 4.90 and 0.19) and full-time employees (5.86, 5.51 and 0.18). Living place (city, village or camp) showed no relation with mental health, except for suicidal ideation: those living in villages had higher levels of suicidal ideation than those living in cities (0.49 vs 0.31).

The study has some limitations. Data were gathered from January to March 2020, and since then, the situation in Ukraine has drastically changed due to the full-scale Russian invasion. While this study’s data and conclusions might serve as a baseline for further research, they do not represent the real-time situation. While many social factors were analysed, the effects found for them do not necessarily represent causality, given the statistical methods used. Interactions among factors were not studied; therefore, no firm conclusions can be made about the effects of those interactions on mental health.

To the best of the authors’ knowledge, this paper is original in terms of its topic, as the first-ever in Ukraine quantitative study of mental health and social determinants of mental health of the Romani population.

Discusses the 27 papers in ISEF 1999 Proceedings on the subject of electromagnetisms. States the groups of papers cover such subjects within the discipline as: induction machines; reluctance motors; PM motors; transformers and reactors; and special problems and applications. Debates all of these in great detail and itemizes each with greater in‐depth discussion of the various technical applications and areas. Concludes that the recommendations made should be adhered to.