Alternative Data Products for Institutional Investors and the Evolution of Data-Driven Decision Making in Financial Markets

Abstract

The financial services industry has experienced a transformative shift toward data-driven decision making, with alternative data (AD) emerging as a critical component of modern investment strategies. Alternative data encompasses non-traditional information sources including satellite imagery, social media sentiment, web traffic analytics, credit card transactions, and internet-of-things (IoT) sensor data that provide unique insights beyond conventional financial statements and market data. Institutional investors increasingly leverage machine learning (ML) and artificial intelligence (AI) techniques to extract actionable intelligence from these diverse datasets, enabling more informed investment decisions and enhanced alpha generation. This review examines the evolution of AD products in financial markets, analyzing their applications across various asset classes, the technological infrastructure supporting their integration, and their measurable impact on investment performance. The paper explores natural language processing (NLP) applications for textual data analysis, computer vision techniques for satellite imagery interpretation, and deep learning (DL) models for pattern recognition in complex datasets. Furthermore, this review addresses critical challenges including data quality assurance, regulatory compliance concerns, ethical considerations in data acquisition, and the competitive dynamics of proprietary versus shared data resources. The findings suggest that while AD integration offers substantial advantages in predictive accuracy and alpha generation, successful implementation requires sophisticated technological capabilities, robust governance frameworks, and careful consideration of ethical and regulatory boundaries.