The asset management industry faces an explosion of data. Those who can organize and leverage this data will build alpha. Those who can’t will fall behind.

Alternative and ESG data represent the next frontier for competitive advantage in asset management. While alternative datasets are multiplying from 2,000 to 5,000+, investment managers face unprecedented challenges: controlling data acquisition costs, avoiding redundancy across multiple providers, building infrastructure to handle massive volumes and establishing governance frameworks to ensure quality. Yet fewer than 20% of asset managers have transformed their organizations to become truly data-driven. The winners in this new landscape won’t be those with the most data—they’ll be those who can turn fragmented datasets into actionable insights faster than competitors.

StarQube provides the complete infrastructure for data-driven asset management. Our proprietary NoSQL database automatically integrates data from any source—market data, fundamental data, ESG providers, alternative datasets—into a unified referential with automated quality controls and reconciliation. Whether you’re managing systematic strategies or incorporating ESG constraints into traditional portfolios, StarQube eliminates the months-long infrastructure projects that typically block innovation, letting you move from raw data to production strategies in days.

What You’ll Learn

• Why alternative data creates competitive advantage: The explosion from 2,000 to 5,000+ datasets and the infrastructure/governance challenges most firms haven’t solved
• Complete technical architecture: StarQube’s NoSQL database, automated data loader, and FQL language that solve collection, quality control, and manipulation at scale
• ESG implementation example: Building proprietary scores from five data providers covering 5,000 issuers with automated updates and historical accuracy
• Seamless integration: Unified referentials, automated connectors with all providers, and optional Data-as-a-Service for complete administration
• End-to-end workflow: From data collection through backtesting, optimization, and execution using the same timestamped foundation