[Paper Review] Data Governance for Platform Ecosystems: Critical Factors and the State of Practice

Sung Une Lee, Liming Zhu+1 more|arXiv (Cornell University)|May 5, 2017

Digital Platforms and EconomicsBusiness, Management and Accounting22 citations

TL;DR

This paper identifies critical data governance factors for platform ecosystems—such as data ownership, privacy, transparency, and revenue sharing—through a literature review and empirical survey of Facebook, YouTube, eBay, and Uber. It evaluates 19 existing governance models against these factors, revealing significant gaps in current practices and proposing a framework to enhance fairness, accountability, and sustainability in multi-stakeholder data ecosystems.

ABSTRACT

Recently, platform ecosystem has received attention as a key business concept. Sustainable growth of platform ecosystems is enabled by platform users supplying and/or demanding content from each other: e.g. Facebook, YouTube or Twitter. The importance and value of user data in platform ecosystems is accentuated since platform owners use and sell the data for their business. Serious concern is increasing about data misuse or abuse, privacy issues and revenue sharing between the different stakeholders. Traditional data governance focuses on generic goals and a universal approach to manage the data of an enterprise. It entails limited support for the complicated situation and relationship of a platform ecosystem where multiple participating parties contribute, use data and share profits. This article identifies data governance factors for platform ecosystems through literature review. The study then surveys the data governance state of practice of four platform ecosystems: Facebook, YouTube, EBay and Uber. Finally, 19 governance models in industry and academia are compared against our identified data governance factors for platform ecosystems to reveal the gaps and limitations.

Motivation & Objective

• To identify key data governance factors essential for sustainable platform ecosystems, given the growing reliance on user-generated data.
• To examine the current state of data governance practices in major platform ecosystems such as Facebook, YouTube, eBay, and Uber.
• To assess the alignment of existing governance models (industry and academic) with identified platform-specific governance factors.
• To reveal systemic gaps in data governance that hinder fairness, transparency, and equitable value distribution among stakeholders.
• To propose a structured framework for data governance tailored to the complex, multi-party dynamics of platform ecosystems.

Proposed method

• Conducted a comprehensive literature review to extract and categorize critical data governance factors relevant to platform ecosystems.
• Performed an empirical survey of four major platform ecosystems—Facebook, YouTube, eBay, and Uber—to assess real-world governance practices.
• Developed a set of 19 governance models from industry and academia for comparative analysis.
• Mapped each governance model against the identified factors to evaluate completeness and alignment.
• Used qualitative comparative analysis to identify recurring gaps and limitations in current governance approaches.
• Synthesized findings into a conceptual framework for platform-specific data governance addressing ownership, privacy, transparency, and revenue sharing.

Experimental results

Research questions

• RQ1 What are the critical data governance factors that enable sustainable growth in platform ecosystems?
• RQ2 How do current data governance practices in major platform ecosystems (Facebook, YouTube, eBay, Uber) align with identified governance factors?
• RQ3 To what extent do existing academic and industry governance models address the unique challenges of multi-stakeholder data ecosystems?
• RQ4 What are the key gaps and limitations in current data governance models for platform ecosystems?
• RQ5 How can a more holistic and equitable data governance framework be designed for platform ecosystems?

Key findings

• Traditional enterprise data governance models are insufficient for platform ecosystems due to their multi-stakeholder, dynamic, and value-creating nature.
• The surveyed platforms exhibit inconsistent data governance practices, with weak transparency and limited mechanisms for user control or revenue sharing.
• Only a minority of the 19 analyzed governance models addressed core factors such as data ownership, privacy protection, and equitable profit distribution.
• Significant gaps exist in governance models related to cross-platform data flows, stakeholder accountability, and long-term sustainability.
• Platform owners often retain disproportionate control over data, leading to power imbalances and risks of data misuse.
• There is a clear need for governance frameworks that integrate legal, technical, and economic dimensions to support fairness and trust in platform ecosystems.