Crowding and Alternative Data: Our Analysis and Outlook
As the use of alternative data within the investment community grows more prevalent, this prompts questions on dataset crowding and its implications for alpha generation. While opinions may be divided on the significance of buy-side client numbers in evaluating a dataset, an analysis of dataset crowding serves as an interesting source of insight into the interplay between supply and demand for different data types.
In the following analysis, we explore:
- Levels of dataset crowding across different dataset types
- Supply and demand for four dataset types: web scraping, web & app tracking, ESG, and location
- The types of dataset that will be most valued by investors in the future
HOW DOES CROWDING DIFFER BETWEEN DATASET TYPES?
Below we aggregate client numbers for individual datasets by dataset type. As expected, the chart shows a concentration of interest in some of the more ‘traditional’ data offerings, such as financial products, fundamental and fund flow datasets. However, perhaps more surprisingly Internet of Things and ESG datasets