Big data are increasingly used to make predictions about uncertain investments, thereby helping firms identify innovation opportunities without the need for domain knowledge. This trend has led to questions about which firms will primarily benefit from the availability of these data-driven predictions. Contrary to existing research suggesting that data-driven predictions level the playing field for firms lacking domain knowledge, I argue---using a simple theoretical framework---that these predictions actually reinforce the competitive advantage of firms with domain knowledge. In innovation contexts, where returns are skewed and not all leads can be pursued, domain knowledge helps evaluate predictions and avoid false positives. I test this idea in the context of pharmaceutical innovation, exploiting the features of genome-wide association studies (GWASs) that provide data-driven predictions about new drug targets. The results show that GWASs stimulate corporate investments, but around one-third of these resources are misallocated toward false positive predictions. Companies lacking domain knowledge react more strongly but are disproportionally likely to fall into the trap of false positives. Instead, domain knowledge helps firms make fewer investments that target only the best opportunities. Together, the results show that even if data-driven predictions hold value when searching for innovations, domain knowledge remains the crucial source of competitive advantage in the age of big data.
