A recent survey of 100 professionals across lending, hedge funds, banking, and insurance in the U.S. and U.K. reveals that while financial institutions recognize the potential benefits of integrating alternative data, they face significant challenges in analysis and data sourcing. The primary obstacles include a lack of in-house expertise, issues with data quality, and the difficulty of integrating semi-structured or unstructured data into existing models. Many institutions struggle with the volume and complexity of data, which hinders their ability to draw meaningful insights and deliver on self-imposed data agility standards. To overcome these challenges, some financial organizations are outsourcing data collection, using automated systems, and treating data as a commodity to enhance their operational efficiency. This approach allows them to focus on investment strategies without being bogged down by the technical complexities of data management, thereby improving their agility and ability to scale operations. Despite these efforts, the adoption of alternative data is still in its early stages, presenting opportunities for institutions to gain a competitive edge by developing robust data collection and governance strategies that emphasize quality over quantity.