Extract, Transform, Load (ETL)

Extract, transform, load, or ETL, describes a process in which data is extracted from one system then transformed and loaded into another system. It’s a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a target system. In the context of process mining, ETL sets the stage for integrating and loading data into the process mining tool for analysis. It’s usually done by data scientists or engineers. 

How does the ETL process work?

The first step of the ETL process, data extraction, involves pulling the needed data from various sources. These extracts can have different schemes, sizes, and granularities and can include CRM and ERP systems, SQL servers, and even email. In data transformation, the goal is to standardize the format of the extracted data to create a uniform data set. Data transformation is part of data preprocessing, and it’s the phase where data is consolidated for analysis. The modification can be both syntactic and semantic.

  • In syntactic data transformation, formal aspects of the data are adapted. This could look like converting date formats for unification or better processing or reformulating cryptic names to make them easier to read. Syntactic transformation does not change the meaning of the data.
  • In semantic data transformation, the data is enriched with information that adds a basic meaning to the data and the relationships between the data. For example, rather than addressing database specific details, semantic transformation relies on abstract mapping—the object and its relationships, such as generalizing the object Person into Employee, Applicant, and Customer. This approach often makes the data more meaningful. 

In the last step of ETL, data is loaded into a process mining tool. Accuracy is essential, as loading an incorrect or incomplete data set is likely to produce an incorrect or incomplete result in the analysis. Once the data has been successfully loaded into the tool, process mining can begin.

Why is ETL important?

ETL improves the quality of data before it is loaded and analyzed. This process makes data easier to access, resulting in faster and more informed decision making. In process mining, ETL enables data-driven insights by allowing organizations to organize and put all data to use.


Process Mining Glossary

Conformance Checking    |     Continuous Improvement    |     Event Log    |    Process Controlling     |     Process Deviation    |    Process Discovery   |    Process Enhancement   |     Process Management Life Cycle    |     Process Transparency    |     Process Variant    |    Target Process

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