[Paper Review] Knowledge-driven Data Ecosystems Towards Data Transparency

Sandra Geisler, María-Esther Vidal+10 more|arXiv (Cornell University)|May 19, 2021

Data Quality and Management27 references4 citations

TL;DR

This paper proposes a knowledge-driven data ecosystem architecture to enhance data transparency, trustability, and governance in alliance-driven data ecosystems. By integrating knowledge models, metadata, and traceable workflows, the framework enables verifiable compliance with data quality, organizational, and legal-ethical requirements—demonstrated through a clinical research use case and validated across three DE types in a comparative analysis.

ABSTRACT

A Data Ecosystem offers a keystone-player or alliance-driven infrastructure that enables the interaction of different stakeholders and the resolution of interoperability issues among shared data. However, despite years of research in data governance and management, trustability is still affected by the absence of transparent and traceable data-driven pipelines. In this work, we focus on requirements and challenges that data ecosystems face when ensuring data transparency. Requirements are derived from the data and organizational management, as well as from broader legal and ethical considerations. We propose a novel knowledge-driven data ecosystem architecture, providing the pillars for satisfying the analyzed requirements. We illustrate the potential of our proposal in a real-world scenario. Lastly, we discuss and rate the potential of the proposed architecture in the fulfillment of these requirements.

Motivation & Objective

• Address the persistent challenge of data transparency and trustability in data ecosystems, especially in alliance-driven settings.
• Identify critical requirements for data governance, data quality, and legal-ethical compliance in multi-stakeholder data environments.
• Develop an architecture that enables traceability, verifiability, and interoperability across heterogeneous data sources and stakeholders.
• Evaluate the proposed architecture against baseline and knowledge-driven data ecosystem models in terms of requirement fulfillment.
• Provide a scalable, transparent framework to support data sovereignty and fair data sharing in domains like biomedicine and B2B engineering.

Proposed method

• Derive requirements from real-world use cases, particularly multi-site clinical studies, to identify data management, organizational, and legal-ethical needs.
• Design a knowledge-driven data ecosystem architecture integrating metadata, services, business models, and strategic documentation to support transparency.
• Introduce a networked model of knowledge-driven data ecosystems to enable cross-ecosystem collaboration, negotiation tracking, and policy alignment.
• Apply a comparative analysis framework to assess three DE types: baseline (data-only), knowledge-driven (with services and models), and networked knowledge-driven DEs.
• Use traceability and verifiability metrics to evaluate how well each DE type supports requirement fulfillment, especially for data quality and compliance.
• Leverage insights from the Dagstuhl Seminar 19391 and existing data quality models to ground the architecture in established research and practice.

Experimental results

Research questions

• RQ1 What are the key data management, organizational, and legal-ethical requirements for achieving data transparency in alliance-driven data ecosystems?
• RQ2 How do different data ecosystem types (baseline, knowledge-driven, networked) compare in their ability to support traceability and verifiability of data transparency requirements?
• RQ3 To what extent can a knowledge-driven architecture enhance data quality, governance, and trustability in multi-stakeholder data environments?
• RQ4 What architectural components are necessary to enable cross-ecosystem interoperability and policy alignment in data ecosystems?
• RQ5 How can data transparency be systematically achieved and verified in complex, distributed data ecosystems with heterogeneous stakeholders?

Key findings

• Baseline data ecosystems can fulfill only a subset of data management requirements (DMR1–DMR6) and lack traceability and verifiability for legal and ethical compliance.
• Knowledge-driven data ecosystems fully satisfy data management requirements but still face challenges in traceability due to interoperability and legal regulation constraints.
• Networks of knowledge-driven data ecosystems are the only model capable of enabling full traceability and verifiability of requirement fulfillment through shared metadata, services, and documented negotiations.
• The proposed architecture enables transparent data governance by embedding knowledge models that document data lineage, quality assessments, and stakeholder agreements.
• The comparative analysis in Table 1 shows that only networked knowledge-driven DEs can support the full spectrum of data quality, organizational, and legal-ethical requirements.
• The framework provides a practical pathway for achieving data transparency in sensitive domains like biomedicine, where data sovereignty and compliance are paramount.