It is often commented that 80% of the work of data science is data cleaning, while only 20% is analysis (Browne-Anderson, 2018). Despite this, the actual contents of what data cleaning entails is largely obscured, often dismissed as a tedious and laboursome yet necessary exercise (Rawson and Muñoz, 2019). While