5 Ways you can Automate Metadata Population

After you've decided what metadata you want to use to organize and categorize your digital assets, you'll need to get metadata populated within your DAM system. There are several built-in tools, that can help you automate tasks specific to extracting or converting existing metadata from assets and other systems. Metadata automation tools help you populate your DAM system faster, to quickly increase asset findability, improve brand consistency, and reduce risks by enforcing rights management and naming conventions.

Here are the top 5 metadata technology tools that we believe your DAM system must have to help you populate your metadata faster:

1. Templates

Templates are structured forms that let users enter metadata values into preset fields to match the values you are already using within your DAM system. After creating a template, you can later select it to instantly generate a formatted set of element attributes when populating an asset's metadata.

2. Mark-up tools

Mark-up tools help add structure to your metadata attributes. The terminology evolved from the marking up of manuscripts, but in digital media, the mark-up text was replaced with tags that have a specific property. Most of these tools generate XML or SGML Document Type Definitions (DTD) that can be extracted and automatically added to your metadata schema.

3. Extraction Tools

Extraction tools automatically create metadata from an analysis of the digital asset. Generally limited to textual resources, a translation extraction tool could search an asset for any tags with an English value and automatically translate those values into French, Spanish, or any other language.

4. Conversion Tools

Conversion tools translate metadata from one standard format to another. This is especially useful when extracting and integrating data across various systems. Once the metadata is converted, you can validate the metadata according to a specific standard or export the files as standalone XML files for application within other metadata software.

5. Smart Tags

Smart tags refer to AI machine-learning algorithms that can automatically add metadata to digital asset files. Image-recognition algorithms, for example, automatically tag an image with related keywords based on photo type (portrait, landscape, etc.) color mode, geographic location, individual objects, particular emotions, and more.

In conclusion, it's always important to pair these metadata automation tools, with manual review by professional editors to ensure accuracy and compliance of your company's metadata standards.