The bigger, the better? An investigation of optimal volume of big data

Big data has changed the way modern businesses run. Recently, big data has drawn huge attention from academic researchers, industry practitioners, and policy makers. While the common belief about big data is that many benefits come from the data volume, existing studies identify challenges associated with big data volume such as privacy concerns and management cost. Given the trade-off between the opportunities and the challenges from big data, we presume that there exists an optimal volume of data which may contradicts to a common misperception that the bigger volume guarantees more benefit. Grounded on a game-theoretic model, we analyze a monopolistic e-tailer's decision on the optimal volume of data to collect and the price of the product to sell in the presence of two different types of customers in terms of their privacy-sensitivity. Our preliminary results show how the number of privacy-sensitive customers and their utility loss influence the optimal information level and the price. We then explore the welfare implications by comparing the profit-maximizing data volume with welfare-maximizing level. We aim to contribute to the literature by characterizing the optimal volume of big data while modelling customers' privacy-sensitivity. In the presence of privacy and cost challenges, our results may give them implications for strategic decision-making for practitioners and policy makers.