Enterprise AI Analysis
Construction of a Theoretical Framework for Scientific Data Governance
Authors: Yanrui Qiu & Zhimin Hu
Journal: Scientific Data
Executive Impact: Harnessing Scientific Data for Innovation
The advancement of data-intensive sciences and artificial intelligence-driven sciences has introduced governance challenges for multi-source heterogeneous scientific data across diverse scenarios. Given the intricate entanglement of stakeholders, processes, and content in scientific data governance, this study intends to propose a theoretical framework to elucidate its complex dynamics and inform governance practices. The theoretical framework for scientific data governance consists of three core dimensions: data stakeholders, data lifecycle, and data governance elements. Non-systematic literature review was employed to identify the classification of data stakeholders and data lifecycle, and bibliometric analysis was used to extract the elements of scientific data governance. Meanwhile, based on the elements of data governance, five governance systems have been summarized, including organizational operation system, technical support system, risk prevention and control system, value realization system, and regulatory system.
3
Core Dimensions
5
Governance Systems
7
Lifecycle Phases
4
Stakeholder Groups
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Data Governance Definition
Data governance is highly scene-oriented, varying significantly across domains, and a universally accepted definition has not been established. This study defines it as the effective management and control of the entire lifecycle of diverse data categories through a systematic framework comprising laws, regulations, management systems, standard specifications, and technological tools, aiming to fulfill data application requirements across multiple scenarios, specifically for scientific research data.
Theoretical Frameworks Review
Existing data governance frameworks vary in their emphases. Some focus on enterprise data governance (IBM, Zhang, Kyoung-ae), others on specific data management practices (DAMA), and some on research data management and open sharing (NIST, Kieran). Commonalities include addressing stakeholders, processes/lifecycles, data quality, standards, metadata, security, and value release. This study synthesizes these to build a generalized framework.
Scientific Data Governance Elements
To clarify the key elements, this study used bibliometric analysis on relevant literature. Keywords were extracted, filtered (≥50 times), merged, and categorized. The classification results are presented in Table 4, showing elements like technological infrastructure, data resources, public attitudes, operation mechanism, organizational structure, data quality control, data standards, talent team building, data ontology, data services, access control, data security, funding sources, policies, privacy protection, metadata management, data ownership, data circulation, ethics framework, informed consent, and data fairness. These elements are reorganized into five governance systems.
| Key Dimension | Description | Implications for Governance |
|---|---|---|
| Stakeholders | Individuals or groups involved in scientific data activities, affected by or affecting governance objectives. |
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| Data Lifecycle | Stages data passes from creation to deletion, including collection, storage, processing, management, sharing, application, and deletion. |
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| Governance Elements | Specific aspects and components required for effective data management and control. |
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Scientific Data Governance Lifecycle
The proposed refined lifecycle framework for scientific data governance comprises seven critical phases, emphasizing centralized management and distributed storage for multi-source heterogeneous data.
Data Collection
→
Data Storage
→
Data Processing
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Data Management
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Data Sharing
→
Data Application
→
Data Deletion
11.43%
Technological Infrastructure
Technological infrastructure emerged as the most frequent keyword (11.43%) in the bibliometric analysis, highlighting its critical role in scientific data governance, encompassing internet, tools, networks, machine learning, AI, blockchain, and cloud computing for efficient data management.
| Stakeholder Category | Representative Individuals and Organizations | Role in Governance |
|---|---|---|
| Data Providers | Researchers, research institutions, enterprises, the public. |
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| Data Users | Researchers, research institutions, government bodies, enterprises, the public. |
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| Data Sharing Facilitators | Research funding agencies, publishers, data centers, information centers, libraries, archives, repositories, external service providers. |
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| Policymakers | Government bodies, professional associations. |
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Integrated Governance Systems for Scientific Data
Scientific data governance requires coordinated operation of sophisticated systems, anchored in institutionalized organizations with foundational human, financial, and material resources, and scientific management systems. It aims to maximize data value realization while ensuring legal compliance and risk control.
Key Focus Areas:
Organizational Operation System: Cornerstones providing human, financial, material, and management support for data governance.
Technical Support System: Cornerstone for technological infrastructure, enhancing data quality, standardization, and enabling 'Governance-by-Design'.
Risk Prevention and Control System: Safeguards ensuring security, compliance, and privacy protection during data circulation and external provisioning.
Value Realization System: Drives data value transformation and efficiency release, supporting data-driven decision-making and social governance.
Regulatory System: Provides institutional guarantees and ethical constraints across all phases, serving as the baseline for all activities.
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Your AI Governance Implementation Roadmap
A phased approach to integrate advanced governance practices and AI into your scientific data ecosystem.
Phase 1: Discovery & Assessment (Weeks 1-4)
Comprehensive audit of existing data infrastructure, governance policies, and stakeholder needs. Identification of critical data types, lifecycle stages, and current challenges. Development of a tailored strategic plan.
Phase 2: Framework Design & Pilot (Months 1-3)
Design of the theoretical framework tailored to your organization's scientific data. Selection of appropriate technical support systems (e.g., AI-driven metadata tools). Pilot implementation on a specific dataset or research project.
Phase 3: System Integration & Automation (Months 3-6)
Integrate chosen technical solutions across the full data lifecycle. Implement AI-driven automation for data quality control, access management, and compliance monitoring. Establish initial risk prevention mechanisms.
Phase 4: Full-Scale Deployment & Training (Months 6-12)
Roll out the governance framework and systems across relevant departments. Conduct comprehensive training for data providers, users, and facilitators. Refine regulatory compliance and value realization strategies.
Phase 5: Continuous Optimization & Future-Proofing (Ongoing)
Establish continuous monitoring, evaluation, and feedback loops. Adapt the framework to evolving scientific needs and regulatory landscapes. Explore advanced AI applications for enhanced data value and societal impact.
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