Revolutionizing Data Collection: A Look at the Ever-Evolving Landscape in 2026

The Significance of Public Web Data Collection in 2026

The past year has showcased the immense capabilities that public web data collection can offer, yet it is evident that the industry still has room to grow in the coming year.

Anticipated changes in legislation within the AI industry, coupled with impending legal battles, make it intriguing to witness the unfolding dynamics throughout the year. One thing remains certain: the fundamental importance of data collection will continue to be emphasized more than ever before.

A consortium of top tech experts has collaborated to provide insights into the anticipated evolution of the data collection landscape in 2026. Drawing from their industry expertise, they offer a glimpse into what the future holds for businesses and AI on a global scale.

The Evolution of Fair Use in Copyrighted Material

Denas Grybauskas, the Chief Governance and Strategy Officer at Oxylabs, highlights the growing emphasis on the transformative use of copyrighted material in US law discussions. The fair use doctrine allows for the transformative use of copyrighted content, raising questions about the application of this principle to AI training utilizing web content.

In regions where the fair use doctrine may not apply, such as the EU, the industry is challenged to develop technological solutions for proper credit attribution and fair remuneration to creators, all while maintaining the accessibility of web information.

The Rise of Agentic Systems for Data Collection

Julius Černiauskas, the CEO at Oxylabs, envisions significant advancements in agentic systems for public data collection in the upcoming year. The automation of various tasks involved in web scraping through AI agents is expected to streamline processes, reduce costs, and enhance accessibility to public data without the need for specialized skills or engineering teams.

The market is likely to witness the introduction of new tools and features that automate specific tasks, further revolutionizing data collection practices.

Utilizing LLMs for Parsing

Juras Juršėnas, the COO at Oxylabs, predicts a surge in the use of Large Language Models (LLMs) for parsing in the next 12 months. The adoption of LLMs for data parsing has been limited by factors such as pricing and prompt-size constraints in the past, necessitating additional resources for HTML cleaning before parsing.

However, the market is now witnessing a proliferation of tools that can streamline this process, leading to an anticipated increase in LLM usage for parsing tasks.

Quality Emphasis in Data Collection

Rytis Ulys, Head of Data & AI at Oxylabs, emphasizes a shift towards prioritizing quality over quantity in data collection endeavors in 2026. Recent research has highlighted the detrimental impact of even small amounts of low-quality data on overall dataset integrity.

Efforts are being directed towards ensuring robust data quality through advanced tools and technologies, underlining the critical importance of data collection fundamentals in driving AI advancements.

Enhanced Understanding of Online Data Collection

Based on these insights, the year ahead promises significant developments in agentic systems for data gathering, increased use of LLMs for parsing, and a transition towards quality-focused data search methodologies.

Legal decisions on copyright laws in both the US and Europe are anticipated to bring clarity to the industry, paving the way for improved tools and automation capabilities. Businesses can look forward to a deeper understanding of web data collection and its integral role in everyday operations.