Datenartefakten

Datenartefakten, often translated as data artifacts, are the tangible outputs or byproducts of the data processing and management lifecycle. They represent the raw, transformed, or aggregated information that results from various stages of data handling. This can include anything from initial data collection logs and source system extracts to intermediate data transformations, cleaned datasets, analytical models, reports, and even the metadata that describes these data elements. Understanding data artifacts is crucial for data governance, auditing, reproducibility, and troubleshooting. They provide a trail of evidence for how data was created, modified, and used, which is essential for ensuring data quality and compliance with regulations. In data warehousing and data science, data artifacts are the building blocks of insights. They are the actual data that analysts query, models train on, and reports visualize. Without well-managed and documented data artifacts, it becomes difficult to trust the information derived from them or to replicate past analyses. The lifecycle of a data artifact involves its creation, storage, access, and eventual archival or deletion, each step requiring careful consideration to maintain its integrity and usefulness.