Engineering has entered a new phase in which ad hoc data collection plays an ever more important role in planning, development/construction, operation, and decommissioning of structures and processes. Intellectual attention has largely focused on exciting new sensing technologies, and on the prospects and challenges of ‘big data’. A critical issue that has received less attention is the need for new data analysis techniques that can handle what we might call bad data that does not obey assumptions required for a planned analysis. Most widely used statistical methods, and essentially all machine learning techniques, are limited in application to situations in which their input data is (i) precise, (ii) abundant, and (iii) characterised by specific properties such linearity, independence, completeness, balance, or being distributed according to a named or particular distribution.