  A significant portion of the effort involved in advanced process control,
process analytics, and machine learning involves acquiring and preparing data.
Literature often emphasizes increasingly complex modelling techniques with
incremental performance improvements. However, when industrial case studies are
published they often lack important details on data acquisition and
preparation. Although data pre-processing is unfairly maligned as trivial and
technically uninteresting, in practice it has an out-sized influence on the
success of real-world artificial intelligence applications. This work describes
best practices for acquiring and preparing operating data to pursue data-driven
modelling and control opportunities in industrial processes. We present
practical considerations for pre-processing industrial time series data to
inform the efficient development of reliable soft sensors that provide valuable
process insights.
