As ever more technology is deployed to process and transmit financial data, this could benefit society, by allowing capital to be allocated more efficiently. Recent work supports this notion. Bai, Philippon and Savov (2016) document an improvement in the ability of S&P 500 equity prices to predict firms’ future earnings. We show that most of this “price informativeness” rise can be attributed to a size composition effect as S&P 500 firms are getting larger. In contrast, the average public firm’s price information is deteriorating. Do these facts imply that big data failed to price assets more efficiently? To answer this question, we formulate a model of data-processing choices. We find that big data growth, in conjunction with a change in the relative size of firms, can trigger a decline in informativeness for smaller firms. The model also reveals how big data growth can masquerade itself as size composition. The implication is that ever-growing reams of financial data may be helping price assets more accurately. But this might not deliver financial efficiency benefits for the vast majority of firms.