Dataveränderung

Dataveränderung refers to any alteration or modification made to data after its initial creation or collection. This can encompass a wide range of actions, from simple corrections of errors to complex transformations and enrichments. The purpose of dataveränderung can vary greatly depending on the context. It might be necessary to ensure data accuracy, update outdated information, adapt data for specific analytical purposes, or integrate it with other datasets.

There are several categories of dataveränderung. Data cleaning involves identifying and rectifying errors, inconsistencies, and missing values. Data transformation alters the structure or format of data, such as aggregating values, changing data types, or normalizing datasets. Data enrichment adds new information to existing data from external sources, enhancing its value and completeness. Data manipulation generally refers to any operation that changes the content or organization of data, often for analysis or reporting.

The process of dataveränderung requires careful consideration to avoid introducing new errors or losing valuable original information. It is crucial to maintain a record of all changes made, often through data lineage or audit trails, to ensure transparency and traceability. Inaccurate or uncontrolled dataveränderung can lead to flawed analyses, poor decision-making, and a lack of trust in the data itself. Therefore, robust data governance practices and well-defined procedures are essential when implementing any form of dataveränderung.