This chapter discusses the importance of selecting measures appropriate to the laboratory's strategy and environment. It focuses on considerations relevant to the evaluation of benchmarking data. Selection of variables for consideration and measurement requires a clear vision of the laboratory's strategy and precision with respect to defining measures that can guide subsequent decisions. Putting appropriate effort into defining measures well at the outset is essential, because anything worth measuring is unlikely to change immediately. External resources for benchmarking data are available from a number of sources. The importance of interacting with clinical services served by the local laboratory cannot be overemphasized, as the laboratory will not be able to address their needs if there is a deficit of interaction and if the needs to be met are not recognized. Another internal data source is data mining. This term refers to searching blocks of data for meaningful information reflective of underlying relationships; this search is driven by computer algorithms and is analogous to the genome‐wide association studies (GWAS) that are proliferating in the scientific literature. More and more hospital administrations are spending resources to generate broad benchmarks applicable to the laboratory and all of their functions throughout their institutions, but it is essential to set aside resources internally to develop and monitor appropriate performance measures in the laboratory. As is frequently encountered with test results, often there are multiple data to be reconciled; similarly, internal and external benchmarking data must be evaluated together, performing an internal check on each other.