Unstructured data, such as image files, text documents, and multimedia content, is a valuable source of insights. However, extracting and analyzing this data can be challenging without the right tools and technologies.
Metadata-Hub is essential for:
- Identifying and extracting relevant features from unstructured data, preparing it for analysis and machine learning.
- Improving the accuracy and interpretability of machine learning models by providing additional information.
- Scaling AI and ML analytics to large and complex datasets by managing and organizing unstructured data.
How Metadata-Hub Works
Metadata-Hub extracts embedded metadata from various unstructured data sources, supporting over 400 file types. It also offers options for building custom extractors to support specialized file formats. The extracted metadata is then aggregated into a centralized repository, providing a single, global view of all metadata across an organization. Metadata is provisioned to KNIME and other data analysis tools.
In addition to extracting and aggregating metadata, Metadata-Hub offers several other features, including:
- Metadata search and discovery: Metadata-Hub provides a powerful search and discovery engine for finding and retrieving metadata.
- Metadata enrichment: Metadata-Hub can enrich the extracted metadata with additional information from users and external sources, such as knowledge graphs and ontologies.
- Metadata governance: Metadata-Hub provides features for managing and governing metadata, such as role-based access control and versioning.
- Data lineage and impact analysis: Metadata-Hub enables tracking the lineage of data and assessing the impact of changes, ensuring data quality and compliance.
- Data visualization: Metadata-Hub provides interactive visualizations to help users explore and understand the relationships between different metadata elements. These visualizations can aid in identifying patterns, trends, and anomalies within the data.