This article focuses on a preliminary step in any ex-post data harmonization project—wrangling the pre-harmonized data—and suggests a practical routine for helping researchers reduce human errors in this often-tedious work. The routine includes three steps: (1) Team-based concept construct and data selection; (2) Data entry automation; and (3) “Second-order” opening—a “Tao” of data wrangling. We illustrate the routine with the examples of pre-harmonizing procedures used to produce the Standardized World Income Inequality Database (SWIID), a widely used database that uses Gini indices from multiple sources to create comparable estimates, and the Dynamic Comparative Public Opinion (DCPO) project, which creates a workflow for harmonizing aggregate public opinion data.