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Data values

Data values

In the era of digital transformation, data values have emerged as fundamental principles guiding how organizations collect, manage, and utilize information to create meaningful stakeholder impact. These values represent foundational beliefs and priorities shaping data practices, ensuring technological advancements align with broader societal goals and ethical standards. As organizations increasingly rely on data-driven decision-making, establishing clear values becomes essential for maintaining public trust while maximizing digitalization benefits.

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Data values
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Data values manifest across multiple dimensions—technical, organizational, regulatory, and ethical - each contributing to a comprehensive framework balancing innovation with responsibility. Technically, values such as integration, interoperability, and infrastructure resilience enable seamless communication between systems and services. When diverse data sources unify through common standards and protocols, organizations develop holistic insights transcending departmental boundaries. Advanced analytics further enhance value by transforming raw information into actionable intelligence supporting real-time monitoring, predictive maintenance, and resource optimization.

Organizationally, data values emphasize collaboration, knowledge sharing, and capacity building. By fostering environments where expertise flows freely between stakeholders, organizations create ecosystems of continuous improvement and innovation. Leadership plays a crucial role, establishing strategic visions aligning technological capabilities with organizational missions. Data literacy and skills development represent equally important values, ensuring personnel effectively leverage digital tools while promoting culture embracing data-driven approaches to problem-solving and service delivery.

Regulatory values center on data sovereignty, compliance, and governance—principles safeguarding information while ensuring responsible use. Data sovereignty acknowledges rights of individuals and organizations to maintain control over their data. Compliance with frameworks like GDPR translates values into concrete practices protecting privacy, securing sensitive information, and mitigating breach risks. Ethical governance extends beyond compliance, establishing transparent policies and accountability mechanisms building trust among citizens and stakeholders.

The ethical dimension addresses fundamental concerns including privacy, inclusivity, and sustainability. Robust security measures protect data from unauthorized access, while accessibility ensures digital services reach all community members, including marginalized populations. Sustainability values promote practices minimizing environmental impact while enhancing quality of life. These ethical considerations ensure technological advancement serves humanity rather than compromising fundamental rights or exacerbating social inequities.

Implementing these values generates tangible benefits across society. Citizens experience improved public services, enhanced quality of life, equitable access to digital resources, and greater institutional transparency. Companies gain access to robust infrastructures enabling innovation, new business models, collaboration opportunities, and frameworks for ethical business practices. Municipalities benefit from streamlined operations, enhanced urban management capabilities, regulatory compliance, and increased public trust through ethical governance.

As digital transformation reshapes our social landscape, data values provide essential guardrails ensuring technology serves human needs while respecting fundamental rights, allowing us to harness data's power to address complex challenges while building more inclusive, sustainable, and resilient communities.
 

D4A Publications on data values

References to academic publications

Maratsi M.I. et al. (2023): "Analyzing Open Government High Value Datasets: Availability, Publishers' Contribution and Technical Specifications"

Nikiforova A. et al. (2023): "Identification of High-Value Dataset determinants: is there a silver bullet for efficient sustainability-oriented data-driven development?"

Nikiforova A. et al. (2023): "Towards High-Value Datasets Determination for Data-Driven Development: A Systematic Literature Review"

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

Chen M., Cao Y., Liang Y. (2023): "Determinants of open government data usage: Integrating trust theory and social cognitive theory"

Almuqrin A., Mutambik I., Alomran A., Gauthier J., Abusharhah M. (2022): "Factors Influencing Public Trust in Open Government Data"

Purwanto A., Zuiderwijk A., Janssen M. (2020): "Citizens' trust in open government data"

Raça V. et al. (2020): "Real-Time Monitoring and Assessing Open Government Data: A Case Study of the Western Balkan Countries"

Crusoe J. et al. (2019): "The impact of impediments on open government data use: Insights from users"

Belhiah M., Bounabat B. (2017): "A user-centered model for assessing and improving open government data quality"

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"