Data openness

Data openness, at its core, refers to making data freely available, accessible, and usable while ensuring it remains machine-readable and interoperable. However, this seemingly straightforward definition encompasses multifaceted interpretations across different communities and traditions. The open source movement, government transparency initiatives, and open science advocates each bring distinct perspectives on what openness means and how it should be implemented. These interpretations stem from fundamental questions about data itself—whether it constitutes neutral "raw material" or whether it is inherently framed by technical, economic, ethical, and philosophical contexts shaping its collection, measurement, and interpretation.

The potential of open data spans numerous domains and stakeholders. For organizations, shared data catalyzes innovation, improves decision-making, and fosters collaboration across traditional boundaries. Government entities can increase transparency by disclosing information related to spending and operations, enabling accountability. Researchers benefit from accelerated discovery, reduced redundant work, and enhanced collaboration. For society broadly, open data can improve services, enable civic engagement, and generate economic value through applications transforming raw information into actionable knowledge.

The values underpinning openness illustrate its significance. Transparency enables scrutiny of processes and decisions affecting public life. Empowerment gives access to previously unavailable information, allowing meaningful participation in decisions. Economic growth through innovation creates new opportunities for entrepreneurship and employment. Social value emerges when open data leads to improved public services, enhanced civic engagement, and more informed citizens.

Despite its promise, implementing openness presents significant challenges. Organizationally, cultural resistance often persists, with concerns about sharing practices and alignment between strategic and operational levels. Standardization issues complicate usability, while resource constraints limit smaller entities' participation. Regulatory and ethical challenges include navigating complex legal frameworks governing intellectual property, privacy, and data protection, with inherent tension between transparency and confidentiality requiring careful consideration.

Technical hurdles further complicate initiatives. Quality and standardization issues emerge when different systems use varying coding standards or metadata approaches. Infrastructure requirements may exceed initial estimates, particularly for large datasets. Interoperability challenges arise when systems from different manufacturers must communicate, often requiring specialized expertise difficult to secure.

Addressing these challenges demands comprehensive approaches spanning technical architecture, governance structures, and ethical frameworks - balancing accessibility with protection while engaging stakeholders in meaningful dialogue about how we organize and utilize this invaluable resource.
 

Alexopoulos C., Saxena S., Rizun N., Shao D. (2023): "A framework of open government data (OGD) e-service quality dimensions with future research agenda"

Van de Vyvere, Brecht, Colpaert Pieter (2022): "Using ANPR data to create an anonymized linked open dataset on urban bustle"

Staunton, Ciara et al. (2019): "The GDPR and the research exemption: considerations on the necessary safeguards for research biobanks"

D Peloquin, M DiMaio, B Bierer, M Barnes (2020): "Disruptive and avoidable: GDPR challenges to secondary research uses of data"

"GDPR Confusion" (2018)

Cagnazzo, Celeste (2021): "The thin border between individual and collective ethics: the downside of GDPR"