Which aspect of Celonis ensures data quality before analysis?

Data cleansing is a critical step in the Celonis process mining framework that focuses specifically on ensuring data quality before any analysis takes place. This process involves identifying and correcting inaccuracies or inconsistencies in the data to create a reliable foundation for analysis. By removing or fixing errors, filling in missing values, and standardizing data formats, data cleansing ensures that the insights derived from the data are valid and trustworthy.

High-quality data is essential, as any analysis performed on flawed data can lead to incorrect conclusions and results. Thus, by prioritizing data cleansing, Celonis helps organizations make informed decisions based on accurate information, ultimately enhancing process improvement efforts.

In contrast, data transformation involves converting data from one format or structure to another, which may also play a role in preparing data but does not directly ensure its quality. Data visualization is focused on presenting the data in a way that is easy to understand and analyze but does not inherently improve the quality of the data itself. Data modeling refers to the structuring and organization of data within databases or analytical frameworks but, like transformation, does not directly address data quality.