AI runs on data—lots of it. But not all data is created equal. The choice between public and proprietary datasets isn’t just about cost and ethics; it defines how well your model scales, how accurately it performs, and whether it can compete at the highest level. So, what’s the right call? Should you build on freely available datasets, or invest in high-quality proprietary data? The answer isn’t obvious, but it can make or break your AI strategy. Let’s break it down.

Why licensed datasets matters

Licensed datasets are often high-quality proprietary datasets that organisations such as content partners, publishers, and other types of rights holders collect and own under specific terms for usage. They offer quality, specificity, and diversity, often coming with licensing agreements that define their use. These datasets are typically sourced from trusted providers or through partnerships.

Examples of licensed datasets usage for AI

• Stock photo platforms and websites license their image datasets for training models in image recognition.

• Academic journals publishers license their scientific datasets to support AI applications in research and knowledge extraction.

• Healthcare organisations often license high-quality imaging data from medical institutions for developing diagnostic tools.

• Financial services company licenses its proprietary financial datasets for AI applications in market analysis and forecasting.

• Record labels license their audio datasets for natural language processing.

Advantages

Licensed datasets provide organisations with data that is often meticulously curated and tailored to specific needs, ensuring high levels of quality and reliability. These datasets are often structured, annotated and enriched by domain experts, making them particularly suitable for niche applications and industry-specific use cases. Moreover, since these datasets are not freely available, they provide a competitive advantage by enabling AI models to be trained on unique, proprietary data that are difficult to replicate. The legal clarity provided by licensing agreements also reduces the risk of misuse, ensuring compliance with regulatory requirements where applicable and mitigating potential liabilities.

Challenges

Acquiring high-quality datasets can be a complex and time-consuming process, often requiring organisations to establish direct partnerships with major content platforms, publishers, or data providers. Negotiating these agreements can take months, involving extensive legal and financial discussions to ensure both parties align on terms. Additionally, many licensed datasets are controlled by a few key industry players, making access highly competitive and sometimes exclusive to large enterprises with existing relationships. For smaller companies and startups, the challenge is even greater, as they may lack the resources, credibility, or leverage to secure such partnerships—Valyu helps smaller startups with this pain point.

The case for public datasets

Public datasets, made freely available for public, are data that anyone can access, use and share. For data to be open, there should be no limitations on how use it in any way. Public datasets are indispensable for academic research, experimentation, and democratising access to AI capabilities. Here are their advantages and challenges:

Advantages

Public datasets are highly accessible for startups, researchers, and organisations especially those with limited budgets. Their availability fosters a spirit of collaboration and innovation, enabling individuals and groups to collectively improve upon shared resources. Organisations like the Common Crawl have played a significant role in advancing fields such as natural language processing by providing extensive open datasets for research and development. Additionally, public datasets facilitate open research by removing financial and logistical hurdles, allowing researchers/developers to prototype and iterate on ideas at faster speeds.

Challenges

While public datasets democratise access, their quality can vary significantly, often requiring substantial preprocessing and validation before they can be used effectively. Issues like inconsistent annotations, incomplete data, or outdated information can limit their utility for production-grade models. Additionally, public datasets may not always scale to meet the demands of large-scale AI systems, necessitating supplementary data. Furthermore, public datasets may raise ethical concerns, have biases, improper sourcing, or misattributed information, commercial copyright hurdles, potentially affecting the fairness and inclusivity of AI models.

The best of both worlds

Perhaps it’s not a matter of choosing between proprietary and public datasets. Researchers and Developers could consider leveraging a balanced approach, combining high-quality and public datasets to capitalise on their respective strengths whilst being mindful of fairness. For instance, researchers often begin with open datasets for prototyping and early-stage development before transitioning to licensed/proprietary data for refinement and context enrichment and commercial use. Additionally, high-quality datasets can be used to validate or augment public datasets, ensuring better model performance, reduced hallucinations, reliability and reduced bias.

To make an informed choice, organisations must evaluate their specific needs, goals, and resources. Defining the purpose of an AI models and applications is crucial in determining whether licensed or public datasets—or a combination of both—best support its development. A cost-benefit analysis is also essential to weigh the financial and operational trade-offs between licensing fees and the potential need for extensive data cleaning. Finally, organisations must consider their long-term vision and how their data strategy aligns with their growth and scalability ambitions.

Whether leaning on proprietary datasets for their quality and diversity or public datasets for their research, organisations must craft a data strategy that balances quality, accessibility, and financial considerations. In the end, the choice isn’t always binary—it’s about finding the right balance that drives the most effective outcomes.