Статья написана по итогам комплекса авторских исследований, в которых выявлены причины того, почему длительное время внедрение достижений научно-технологического прогресса, в том числе современных высокотехнологичных достижений XXI века, только усиливают кризис в социально-экономическом развитии России и глобального мира в целом. Главная цель работы-обозначить основные условия решения указанных проблем для успешного формирования, развития и реализации цифровой экономики (ЦЭ), а также понимания роли государства в этом процессе. Методология проведения работы: Исследование основано на использовании нового методологического инструментария, в соответствии с авторским подходом к постановке цели и особенностям развития человеческой системы с мировоззренческой позиции. Результаты работы: Мировоззренческие основания исследования закономерностей в развитии человеческой системы позволили выявить ту парадигму ее развития, в которой формируется определенная модель отношений между людьми, не входящая в противоречия с цифровыми технологиями и другими достижениями XXI века, и позволяющая найти адекватный этой модели механизм функционирования. Такой механизм предупреждает возникновение возможных рисков для каждого конкретного человека и общества в целом, и в полной мере открывает созидательные возможности цифровой экономики.

Enterprizes need Data Quality Management (DQM) to respond to strategic and operational challenges demanding high-quality corporate data. Hitherto, companies have mostly assigned accountabilities for DQM to Information Technology (IT) departments. They have thereby neglected the organizational issues critical to successful DQM. With data governance, however, companies may implement corporate-wide accountabilities for DQM that encompass professionals from business and IT departments. This research aims at starting a scientific discussion on data governance by transferring concepts from IT governance and organizational theory to the previously largely ignored field of data governance. The article presents the first results of a community action research project on data governance comprising six international companies from various industries. It outlines a data governance model that consists of three components (data quality roles, decision areas, and responsibilities), which together form a responsibility assignment matrix. The data governance model documents data quality roles and their type of interaction with DQM activities. In addition, the article describes a data governance contingency model and demonstrates the influence of performance strategy, diversification breadth, organization structure, competitive strategy, degree of process harmonization, degree of market regulation, and decision-making style on data governance. Based on these findings, companies can structure their specific data governance model.

The paper presents the findings from a 3-year single-case study conducted in connection with the International Data Spaces (IDS) initiative. The IDS represents a multi-sided platform (MSP) for secure and trusted data exchange, which is governed by an institutionalized alliance of different stakeholder organizations. The paper delivers insights gained during the early stages of the platform's lifecycle (i.e. the platform design process). More specifically, it provides answers to three research questions, namely how alliance-driven MSPs come into existence and evolve, how different stakeholder groups use certain governance mechanisms during the platform design process, and how this process is influenced by regulatory instruments. By contrasting the case of an alliance-driven MSP with the more common approach of the keystone-driven MSP, the results of the case study suggest that different evolutionary paths can be pursued during the early stages of an MSP's lifecycle. Furthermore, the IDS initiative considers trust and data sovereignty more relevant regulatory instruments compared to pricing, for example. Finally, the study advances the body of scientific knowledge with regard to data being a boundary resource on MSPs.

Many companies see Data Governance as a promising approach to ensuring data quality and maintaining its value as a company asset. While the practitioners' community has been vigorously discussing the topic for quite some time, Data Governance as a field of scientific study is still in its infancy. This article reports on the findings of a case study on the organization of Data Governance in two large telecommunications companies, namely BT and Deutsche Telekom. The article proposes that large, service-providing companies in general have a number of options when designing Data Governance and that the individual organizational design is context-contingent. Despite their many similarities, BT and Deutsche Telekom differ with regard to their Data Governance organization. BT has followed a more project-driven, bottom-up philosophy; Deutsche Telekom, on the other hand, favors a rather constitutive, top-down approach. The article also proposes a research agenda for further studies in the field of Data Governance organization.