We study law enforcement guided by data-informed predictions of “hot spots” for likely criminal offenses. Such “predictive” enforcement could lead to data being selectively and disproportionately collected from neighbourhoods targeted for enforcement by the prediction. Predictive enforcement that fails to account for this endogenous “datafication” may lead to the over-policing of traditionally high-crime neighbourhoods and performs poorly, in particular, in some cases as poorly as if no data were used. Endogenizing the incentives for criminal offenses identifies additional deterrence benefits from the informationally efficient use of data.