Archaeology can be ‘site centric’. Much of the primary evidence comes from excavations based on a single site so naturally the primary sources for archaeological information are organized by site. This is a great help when establishing intra-site links, be they local spatial relationships which may help reveal functions of buildings on a site, or temporal ones, perhaps how different institutions waxed and waned within a society. However, this organization of the primary evidence inhibits comparisons between sites. The regional and global interactions of each site must be deduced by secondary work, comparing information from a range of primary sources with their differing protocols. Yet deducing these wider relationships from finds is one of the key goals of archaeological research as only by understanding societies at all scales can we get a proper view of how society functions. In this sense, archaeologists, and social science in general, have long appreciated that societies are complex systems, with some coherent large-scale phenomena emerging from microscopic interaction, a language that physical scientists have only articulated over the last couple of decades; for example, see Ball (2004) or Lane et al. (2009). Archaeology meets this challenge with several well-developed approaches. Some are rooted in physical science, such as through the chemical analysis of materials. Others are the product of human expertise, as when styles of product are compared across sites. There are efforts to produce secondary regional catalogues through human analysis of the primary sources, for instance see Mills et al. (2013), Sindbæk (2007), Terrell (2010) and chapter 4 of this volume (Peeples et al. 2016) for recent examples. Yet, there remain limitations. Chemical analysis may reveal the sources of materials but not the paths used for their transfer. Stylistic analysis may be subject to unquantifiable bias. A systematic database from primary sources may be too costly to construct. Even with such a database, there is then too much information and we have to pull out the key patterns, to simplify the information into the important parts in order for us to understand what the data is telling us.