Executive summary

The ability to effectively ingest structured and unstructured data through 'intelligent' data pipelines – underpinned by curating capabilities for self-learning – offers a distinct edge to asset managers in their quest for future-readiness in a rapidly evolving landscape.

Today's uber-connectivity and information explosion offers tremendous opportunity for asset managers to drive alpha generation, optimize decision-making capabilities, and improve efficiency, using AI/ML to enhance their data pipelines and drive richer, more advanced data transformations.

Asset managers can leverage ongoing advances in AI/ML in several ways. These include employing generative AI to create new content and data; automating data preparation and augmented analytics through data science; processing data real-time through edge computing; and cloud-based data ingestion and analysis. Any decision to select third-party and open-source data ingestion tools should account for scalability, security, and compliance.

To maintain a competitive edge and a forward-looking focus, asset managers must invest in the right technologies and talent, and develop robust data governance frameworks to use alternative data in a responsible, ethical manner.