Data ownership is more than a technical issue —it’s a societal challenge. As businesses and individuals generate massive amounts of data through everyday interactions, the lines of who owns, controls, and uses this data have blurred.
Merav Yuravlivker, Chief Learning Officer at Data Society, shares her insights: “I don’t know if data ownership truly exists. The way that we collect data is not usually a single touchpoint. It’s multiple touchpoints… and the information you’re providing is not owned by you.”
This leads to a major issue around data ownership and the lack of transparency being provided to the end user. Whether it’s personal data shared on social media, proprietary information used to train AI models, or sensitive corporate data stored on third-party platforms, ownership often remains within the medium or platform through which the data is accessed.
The Shift in Data Ownership: Generative AI and Beyond
The rise of generative AI has added yet another layer of complexity to data ownership. AI systems rely on vast amounts of public and proprietary data to train their models. These models learn patterns from data that is put into them, and then draw parallels between data points to provide better outputs back to the end user. This raises questions about the ethics and legality of using this data.
As Yuravlivker explains: “One of the biggest shifts that we’ve seen is just knowing now that any data we make public—and sometimes even private—can potentially be accessed by third parties to train new models.”
This has profound implications. Public data, like blogs or social media posts, can be used without explicit consent to train AI tools to replicate voices, decision-making styles, and leadership approaches. This phenomenon raises critical questions:
- Who owns the output of generative AI systems trained on public or semi-private data?
- Should individuals have more control over how their data is used in training AI?
The answers to these questions aren’t just theoretical—they will shape the future of innovation, trust, and regulation for how we ethically deploy and leverage generative AI.
Ownership, Control, and Stewardship: Three Sides of the Same Triangle
While data ownership gets most of the attention, it’s equally important to consider data control and stewardship. Yuravlivker highlights the distinctions: “Ownership in my mind is a gray area… but data stewardship is a topic that I’ve been speaking more about. It has less to do with control and ownership and more about guidelines for how we collect data responsibly.”
Data stewardship emphasizes ethical data use, ensuring all stakeholders—individuals, businesses, or governments—interact with data in ways that align with clear governance principles. While ownership may be distributed and control often resides with platforms, stewardship provides a framework for responsible data handling that builds trust and reduces risks.
Data ownership, control, and stewardship each have their purposes, but all are cut from the same cloth: data governance. As described by Forbes: “Data governance defines the purpose, vision and goals underpinning a company’s data practices and builds trust in the quality and integrity of data to advance strategic objectives.” By establishing a robust data governance framework, organizations can curate a response to address data ownership, control, and stewardship collectively. Throughout the process, data governance practices will help organizations effectively implement policies related to ownership, control, and stewardship in their generative AI practices.
Moving Forward: Navigating the Complexity of Data Ownership
As we grapple with the challenges of data ownership in a digital world, businesses and individuals must focus on three key areas:
- 1. Transparency: Organizations should communicate how data is collected, used, and stored. This builds trust and ensures that users understand their trade-offs when sharing data.
- 2. Governance: Companies must establish robust data governance policies to define ownership, manage access, and ensure ethical practices throughout.
- 3. Proactive Leadership: Waiting for universal standards or global regulations is not enough. Businesses that lead in developing ethical, responsible data practices will be better positioned to navigate future challenges and build stronger relationships with stakeholders.
As Yuravlivker points out, the reality is, “We’re living in a world where ownership is a theoretical concept. It’s time to adjust our behaviors accordingly.” Data ownership may be a gray area, but organizations that embrace transparency, governance, and stewardship can transform uncertainty into opportunity. To ensure organizations are navigating in the right direction, DataCamp recommends: “Employee training is key to ensure responsible AI use and data protection.” As data becomes more lucrative, the weight to protect data ownership has never been greater.
Responsible Data Usage for a Better Future
At Data Society, we believe that addressing data ownership is not just about solving technical challenges—it’s about establishing a foundation of trust and innovation. As the lines of ownership continue to blur, the question isn’t just “Who owns the data?” but “How can we use data responsibly to build a better future?”
If your organization faces these challenges on your 2025 roadmap and is unsure where to begin, let’s discuss how Data Society can help you implement ethical, effective data practices.
