Rohdatenbanken

Rohdatenbanken, also known as raw databases, are systems designed to store and manage data in its original, unprocessed form. This means that the data is collected and stored without any transformations, cleaning, or aggregation. The primary purpose of a rohdatenbank is to preserve the exact state of the data as it was acquired, ensuring that no information is lost or altered during the storage process. This is particularly important for auditing, debugging, and future reanalysis or reprocessing of the data.

The concept of a rohdatenbank is often contrasted with traditional relational databases or data warehouses, which typically involve data cleansing, normalization, and structuring for specific analytical purposes. In a rohdatenbank, the data might be stored in formats like CSV, JSON, XML, or even proprietary binary formats, depending on the source. Accessing and querying data in a rohdatenbank can be more challenging than in structured databases, often requiring specialized tools or programming skills to parse and interpret the raw information.

Rohdatenbanken are valuable in various fields, including scientific research, where preserving experimental results in their original form is crucial for reproducibility. In data science and machine learning, storing raw data allows for experimentation with different preprocessing techniques and model architectures. For compliance and regulatory purposes, maintaining an unaltered record of data can be a legal requirement. The decision to implement a rohdatenbank depends on the organization's data management strategy, its need for data integrity, and the potential for future, as yet unknown, uses of the data.