"Data Creation Challenge" Powered by RavenPack's Bigdata.com Brings Institutional-Grade Data and Global Research Community Together Through the WorldQuant BRAIN Platform
NEW YORK, Feb. 23, 2026 /PRNewswire/ -- RavenPack, the AI decision infrastructure provider for financial enterprises, and WorldQuant, a global quantitative asset management firm, today announced the launch of the "Data Creation Challenge." With participation taking place exclusively on WorldQuant BRAIN, a web-based simulation platform fueled by data and technology, across the platform's global research consultant community who have met rank thresholds and passed background checks, this six-week competition is designed to advance education in AI-driven financial research.
This initiative combines data from Bigdata.com, RavenPack's AI-native platform delivering decision-grade financial intelligence, with WorldQuant BRAIN, which enables registered consultants to build and submit alphas[1] for potential compensation.
"Financial innovation accelerates when high-quality data and modern infrastructure are accessible to researchers everywhere," said Armando Gonzalez, CEO and Founder of RavenPack. "This initiative is about education and AI enablement, showing how institutional-grade intelligence can be explored responsibly and creatively by a global research community."
Transparent Participation, Protected Intelligence
The competition is guided by a clear principle: transparent participation, protected intelligence. The campaign is designed as an educational showcase of how datasets are built, how unstructured financial information becomes research-ready, and how collaborative data science workflows operate in practice, while fully protecting individual models, signals, and proprietary methodologies.
Participants will use RavenPack's open Search API via WorldQuant BRAIN to access data on Bigdata.com to build datasets from unstructured financial content. Throughout the competition, high-level insights will be shared.
"The world is at a fascinating inflection point: with the proliferation of data and AI, we're reaching a place where you can quantify almost anything," said Nitish Maini, Chief Strategy Officer at WorldQuant. "By powering innovation in dataset creation, this competition is a natural extension of the initiatives we're always looking to pursue as we seek to amplify the BRAIN platform and empower our thousands of consultants worldwide."
Expanding Access to Institutional-Grade Infrastructure
Updates and details regarding the "Data Creation Challenge", such as the competition overview and timeline, educational materials, and weekly highlights of creative data engineering approaches and community-level learning themes will be hosted on Bigdata.com.
Overall, the competition reflects a shared commitment by RavenPack and WorldQuant to:
- Promote responsible AI-driven research;
- Expand global access to high-quality financial data;
- Encourage experimentation in structured data engineering; and
- Support the next generation of quantitative researchers.
1. Alphas are mathematical models that seek to predict the future price movements of various financial instruments.
About RavenPack
RavenPack provides AI-driven data infrastructure for the financial industry. The company transforms unstructured content into structured, institutional-grade analytics. Through Bigdata.com, RavenPack provides open, API-first access to comprehensive financial intelligence, enabling researchers, data scientists, and institutions to explore and operationalize AI at scale.
For more information, visit www.ravenpack.com
About WorldQuant
WorldQuant is a global quantitative asset management firm. Founded in 2007 by Igor Tulchinsky with the belief that talent is global, but opportunity is not, WorldQuant has more than 1,100 employees spread among 27 global offices. WorldQuant seeks to get to the future faster, guided by the principle that there are an infinite number of insights to discover. The firm develops and deploys investment strategies across a variety of asset classes in global markets.
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