If you are responsible for managing large volumes of media content, you are likely aware of how crucial a role metadata plays in organizing and retrieving media content. Without metadata, searching and fetching media content from large media libraries becomes practically impossible. As we say at Perifery, “If you can’t find a piece of content when you need it, you do not have it.”
Adding meaningful, sensible metadata to media content has never been easy. Traditionally, media management teams try to manually add metadata to media content. But this practice can potentially lead to more harm than good. Read this blog to understand why you should reconsider adding metadata manually and how you can leverage the latest AI-driven technological advancements to your advantage.
Human subjectivity makes metadata less effective
A fundamental purpose of metadata is to make it easy for people (or machines) to find and retrieve a piece of content in the future. This purpose can be lost when different individuals are involved in adding metadata, as everyone might interpret a piece of content differently, resulting in inconsistencies while adding metadata. Subjectivity in tagging can result in variations in keyword choices, levels of detail, and categorization, significantly reducing the effectiveness of metadata for search and retrieval.
Dealing with changes is highly complex
As times evolve and consumer preferences change, organizations might be required to change their metadata schemas or taxonomies to ensure interoperability across systems and workflows. Changing tags or adding new tags and metadata to old content by going through an entire archive library may not be possible in most cases.
Understanding content’s context is often time-consuming
Adding accurate metadata involves a deep understanding of the content’s context, relevance, and potential future use cases. Analyzing a content’s context thoroughly can require rewatching the content more than once, which may not be practically possible with a constantly fast-growing library. Additionally, some cross-cultural nuances—such as sentiment, implied meanings, or multi-language considerations—can be difficult to capture accurately through manual tagging.
Putting up with fast-growing libraries is costly
Moreover, growing media libraries pose more challenges—they require significant amounts of time and effort from multiple people. Skilled metadata specialists are hard to find and are typically quite expensive, resulting in high operational costs and significant financial strain. Very often you will find automating metadata addition through AI-based tools will be much more cost efficient.
Unlock new opportunities with Perifery AI+
To overcome these challenges associated with manually adding metadata, you need to be able to work with advanced tools that can become a part of your media workflows and automatically understand the content and add metadata as they are being ingested. Perifery AI+ is one such transformational AI-powered tool that is designed to enable media professionals to revitalize their content libraries. Through automated enriched metadata generation, you can unlock new monetization opportunities.
In this snippet from our recent webinar, you can see our solutions architect, Nicholas Llewellyn, demonstrate how a sports entertainment business leverages Perifery AI+ for automatic and enhanced metadata generation.
In addition to metadata generation, Perifery AI+ offers a comprehensive set of AI and workflow automation tools with capabilities like transcriptions, facial and object recognition, and automatic translation. These additions streamline asset management processes and automate content organization, enabling organizations to enhance content library visibility.
Learn more
To learn more about how Perifery AI+ can help you unlock the potential of your media assets, download this datasheet.
To get in touch with one of our representatives, please contact us here.
