Media Asset Management

FATS Media Lab. MAM has a wide range of features. A user will have access to features depending on allowed permissions for their user group(s) and dependent on the features that have been enabled on their system.

FATS Media Lab. MAM keeps information (metadata) for all content in the database, so users can find assets in near line storage, offline storage, storage in other locations, archive, or on physical media. Simple and advanced search capabilities can be performed on a variety of criteria.

Unit admin users can set user permissions and metadata and other features from the Admin widget. Computer and network settings are configured in the Super Admin Console.

Users can monitor processes in the system to check their progress, resubmit failed jobs, and so forth.
The system shows a thumbnail of all assets or just those that match the recent search criteria. Users can click to choose an asset, and then perform various functions listed below or choose a group of assets for any of the asset group functions.  

Metadata is the heart of a media asset management system. Tags and labels for media allow them to be searched and reused. In addition to basic file properties and technical data (e.g. camera data) about assets, FATS Media Lab. Media asset management can have an unlimited number of custom metadata fields, with users’ groups assigned relevant groups of metadata for their own specific use. Public and private search filters can quickly find needed material. FATS Media Lab. Media asset management can maintain a disciplined metadata scheme so all media can be found by mandating a minimum number of labels for all media, avoiding misspellings, and controlling taxonomy.

Content can be tagged at ingest from the FATS Media Lab. Media asset management feeder or from a purpose-built logging interface. Third party information can enter as XML. Users can also use the web interface to tag media after ingest.

Organizations can now use artificial intelligence/machine learning tools to provide meaningful tags and markers for content previously poorly labelled. Some or all of the media can be automatically tagged by any of the leading artificial intelligence (AI)/machine learning (ML) systems with extracted metadata stored in the FATS Media Lab. MAM database. Although results vary, automated tagging allows organizations to save money on human tagging opening the possibility of using and monetising content that was previously unusable.