In an era of big data, there is increasing optimism that data mining will yield valuable insights. However, in the life sciences, relevant data is not only "big"; it is also highly decentralized across thousands of online databases. Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible. Identifiers are a core component of this integration infrastructure; drawing on our experience and on work by other groups, we outline ten lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (providers of online repositories, registries, and knowledgebases) should take in the design, provision and reuse of identifiers; we also outline important considerations for those referencing identifiers in various contexts. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to durability and web-accessibility/resolvability.