Informing the Global Data Future: Benchmarking Data Governance Frameworks

Abstract

Data has become a critical trans-national and cross-border resource. Yet, the lack of a well-defined approach to using it poses challenges to harnessing its value. This article explores the increasing importance of global data governance due to the rapid growth of data, and the need for responsible data practices. The purpose of this paper is to compare approaches and identify patterns in the emergent data governance ecosystem within sectors close to the international development field, ultimately presenting key takeaways and reflections on when and why a global data governance framework may be needed. Overall, the paper provides information about the conditions when a more holistic, coordinated transnational approach to data governance may be needed to responsibly manage the global flow of data. The report does this by (a) considering conditions specified by the literature that may be conducive to global data governance, and (b) analyzing and comparing existing frameworks, specifically investigating six key elements: purpose, principles, anchoring documents, data description and lifecycle, processes, and practices. The article closes with a series of final recommendations, which include adopting a broader concept of data stewardship to reconcile data protection and promotion, focusing on responsible reuse of data to unlock socioeconomic value, harmonizing meanings to operationalize principles, incorporating global human rights frameworks to provide common North Stars, unifying key definitions of data, adopting a data lifecycle approach, incorporating participatory processes and collective agency, investing in new professions with specific roles, improving accountability through oversight and compliance mechanisms, and translating recommendations into practical tools.

Unpacking Smart Campus Assessment: Developing a Framework via Narrative Literature Review

“…The data governance processes indicator, on the other hand, measures the level of means to implement data governance, where business data is driven by advanced digital technology within an evolving data management ecosystem. It connects all components and organizes heterogenous data into uniform structures to simplify the data environment and improves adaptability to varying domains with less training [146]. In addition, existing data stewardship frameworks do not involve users in data security to promote participatory innovation in data governance.…”

Data Governance in Multimodal Behavioral Research

“…From a strategic perspective, data governance is seen as a means of aligning an organization's data assets with its business objectives [21]. This involves defining the key performance indicators (KPIs) and metrics that will be used to measure the success of data initiatives, as well as establishing the governance structures and processes necessary to support data-driven decision-making [24]. For example, a healthcare organization may establish a data governance committee to oversee the development of a data warehouse that will be used to support population health management and clinical research [25].…”