A Practical Guide to Characterising Data and Investigating Data Quality

This guide is designed for data scientists to use in their day-to-day work, and describes a comprehensive list of tasks to perform when investigating data quality and profiling data, and a six-step recommended workflow. Each of the 62 tasks is articulated as a question (and sometimes several questions) to answer about your data. The guide also provides pointers to a Python package (vizdataquality) that implements the workflow, a film about visualizing data quality and other useful resources.