– Ground Truth for Patterns in Transaction Data

Ground truth for patterns in transaction data. A major difficulty with realising full value of transaction data is the lack of “ground truth” in standalone datasets such as loyalty cards data. The patterns in the data (e.g., escalating alcohol purchasing) need to be verified against other data to be sure that such patterns reflect individual behaviour (e.g., escalating alcohol consumption).

My work on linking transaction data into Longitudinal Population Studies (LPS) allows to use depth and breadth of LPS health and social variables as ground truth for patterns that we can detect from transaction data. By establishing patterns in linked datasets through LPS, we can set out to study those patterns in large standalone transaction datasets contributing to the knowledge about physical and mental health, wellbeing, everyday behaviours and life events.