Accelerate Active Metadata
Nimble and Agile Data Management
Easily Incorporate Metadata to Increase Trust
Leverage Automation and Speed Up ROI
Active metadata is information about metadata that is dynamically updated and thus augments the value of metadata. Examples of active metadata are: SLA violations, execution information, data quality scores etc.
By activating metadata, enterprises can significantly increase the trust in the information, gain insights more accurately, and accelerate collaboration and decision-making between teams. Orion’s EIIG provides standard connectors to load active metadata in either json, csv or xml formats.
Features and Capabilities of EIIG’s Active Metadata
Leverage Automation
EIIG leverages graph technology, and machine learning to lead in Cataloging Information assets by automatically building a Data Catalog with a search capability and organizing metadata for quick analysis, collaboration, process/change management and consumption.
Award Winning
EIIG has received numerous awards including taking home the Best Metadata Management Solution honors in 2023. EIIG was also named by Gartner as a Cool Vendor in Graph Technologies.
Award Winning
Best Metadata Management Solution
What makes the best active metadata management solution the best? You could start by finding support for 70+ technologies including legacy, modern, and hybrid/cloud applications.
Having a solution that doesn’t require coding or hiring an expensive consultant counts as two more wins. And you don’t want to forget about implementation time. Why choose a platform that takes months to implement when you can have a solution running in days or weeks instead?
Unlock Value of Data
Improve Confidence with AI
EIIG quickly and automatically extracts metadata with a set of 70+ connectors for a range of systems, including mainframes, databases, ETL tools, business intelligence reports, and even Java and Python code.
EIIG is also featured as part of the 2023 Machine Learning, AI and Data Landscape.
EIIG uses this metadata to build an end-to-end data lineage across systems at a very granular level and visualize it on an easy-to-follow knowledge graph. Overlays of active metadata such as quality scores and tags give you the ability to quickly identify problems and errors.