Very interesting idea suggests that the proliferation of new data or "data abundance" affects the allocation of capital between quant and non-quant asset managers where the quants are "data miners" and the non-quants are "experts". This is a novel way of thinking about information flow and analyst behavior. See "Equilibirium Data Mining and Data Abundance".
Data miners, the quants, search for predictors and then select those that have the highest precision. The experts have a fixed ability to generate trading signals based on their expertise. The data miners gain signals through a search process and thus will be more fluid or have changing precision based on the choice of signals.This framework helps to distinguish between the effect of lower computing costs and greater data availability.
The conjecture of the authors is that data abundance will raise the precision of the best predictors which will cause the quants to search less intensively for new predictors. This process will make quant performance more disperse which leads to less capital. The authors also show that more data will increase price informativeness which will lead to a reduction in average asset manage performance.
This is an important paper to help distinguish between the behavior and choice between quants and discretionary fund managers in the context of the flow of data and information.