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Process mining provides analytical tools and methods which can distil insights about process behaviour from big process-related data. Yet challenges relating to the impact of poor quality data on event logs, the input to process mining analyses, remain. Despite researchers raising concerns about event log data quality, event log preparation is, in practice, generally handled mechanistically, focusing on fixing symptoms rather than on uncovering the root causes of event log data quality issues. To address this, we introduce the Odigos (Greek for “guide”) framework. Based on semiotics and Peircean abductive reasoning, the Odigos framework facilitates an informed way of dealing with data quality issues in event logs. Odigos supports both prognostic (foreshadowing potential quality issues) and diagnostic (identifying root causes of discovered quality issues) approaches. We examine in depth how the framework supports a detailed root-cause analysis of a well-known collection of event log imperfection patterns.

Root-cause analysis of process-data quality problems