What role does “data cleansing” play in Process Mining?

Data cleansing is a critical component in the field of Process Mining as it focuses on enhancing the accuracy and quality of the data being analyzed. This process involves identifying and rectifying errors, inconsistencies, and inaccuracies in the dataset, which is vital since the reliability of insights drawn from Process Mining heavily relies on the integrity of the underlying data.

When data is cleaned, it helps to ensure that the information reflected in process models and analysis accurately represents the actual processes. High-quality data contributes to more precise and trustworthy analysis, enabling organizations to make better-informed decisions based on the insights gained from their processes. Thus, the primary role of data cleansing directly aligns with maintaining the quality of data required for effective process analysis, making it a fundamental step before any data is utilized for further examination and reporting.

Increasing data storage capacity, speeding up data retrieval, and focusing exclusively on unstructured data do not pertain to the specific objectives of data cleansing in the context of Process Mining, as these aspects relate to other areas of data management and usage.