Preparing Event Log Data for Process Mining

Process mining projects often stall because raw system logs are rarely ready for analysis. To unlock true operational insights, you must first know how to locate, clean, and format your enterprise data. This text-based course guides you through the essential methodologies of extracting, transforming, and loading (ETL) event data. You will start with foundational definitions of event logs—such as Case IDs, activity names, and timestamps—before moving into practical data preparation scenarios. What you'll learn: • Understand the core schema requirements of event logs and modern data standards. • Clean and pre-process transactional data to resolve common issues like missing timestamps or duplicate entries. • Transform relational database tables into structured event logs using SQL and modern data manipulation patterns. • Map complex business processes to event log schemas, including introductory object-centric concepts. • Validate and load your prepared event logs into analytical tools to ensure accurate process discovery. Through clear written explanations, conceptual breakdowns, and structured SQL and Python examples, you will build a solid workflow for data preparation. This course is designed for data analysts, business analysts, and beginners to process mining who want to master the data-prep pipeline. No prior data engineering experience is required. Start reading today to turn raw enterprise data into actionable process insights.