The usage of the Internet has grown substantially in recent times. This has resulted in high volumes of data traffic. There is a concomitant rise in bandwidth demands that could result in excessive download delays (or latency). Thus, a single‐server system is no more a prudent choice for data storage. Replication of content and placing them on multiple servers is a method that is used to reduce latency. However, this solution comes at a huge cost. Moreover, replicating objects randomly does not necessarily improve system performance. It is possible to arrive at a solution to the problem of placing content so as to achieve better cost performance. Other performance measures include latency, load balancing and data availability. We refer to the problem of locating content as data location problem in information networks, or DLPIN. The choice of server locations, query routing strategy and user assignment are some of the important problems that require attention along with the location of the data/content. Resource constraints and the nature of traffic (static/dynamic) are two important parameters in the problem environment, and therefore are key distinguishing features in the models. The main contribution of this paper is a novel classification and study of DLPIN on the basis of problem features. The research in this area started with files, the smallest units of allocation. Gradually, files and programs, database segments and entire databases (or mirrors) have been studied. We design examples from these use cases to elaborate a variety of problems in a comprehensive review. Facility location models from physical logistics are extensively used to model these problems. Our paper presents a literature survey of such mathematical models for data location problems. We present a gap analysis that provides pointers to possible future research in this area. This paper also serves to document the success in the use of mathematical programming approaches for data location in information networks.