How does process mining handle unstructured data?

Process mining effectively handles unstructured data through data preprocessing and transformation techniques. In many business environments, a significant amount of data exists in unstructured formats, such as emails, documents, and logs, which do not follow a strict format. To leverage this data for process analysis, it is necessary to preprocess it into a form that can be analyzed alongside structured data.

During preprocessing, unstructured data is examined and transformed to extract relevant insights. This might include techniques like natural language processing (NLP) to interpret text data, or conversion of non-standard data formats into a structured way that aligns with the process mining requirements. By doing this, organizations can gain a comprehensive view of their processes, including elements that traditional structured data alone would not reveal.

In contrast, ignoring or fully converting unstructured data without proper preprocessing would lead to a loss of potentially valuable insights and insights that could be derived from context or narrative, thus limiting the effectiveness of process mining analyses.