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Problem
Poor quality content tagging
Improved by these Interventions
Introduce centralized metadata management
Create or rationalize existing metadata repositories into a centrally managed metadata system, so that humans and machines can both refer to a single version of the truth.
Prevented by these Capabilities
Knowledge Graph driven media asset management
Next generation digital asset management (DAM) where images and video are described with context and meaning.
Strong information governance
Inhibits these Capabilities
Feedback loop for AI training corpus
AI automated tagging in Production
Auto-tagging AI that learns on-the-fly
Leads to these Problems
Inability to drive meaningful user experiences with data
Due to shortfalls in core business information management, projects experience blockers and insurmountable issues when trying to realize rich user experiences
SMEs don't see value in content tagging
Subject Matter Experts (SMEs) cannot see business value being created by the use of tags or metadata.
Metadata holds no value for human or machine processes
If metadata does not include structure or context (from a model), it is impossible to harness it in structured scenarios such as AI training, and driving product features.
Caused by these Problems
Tag duplication
In content classification scenarios, the metadata "tags" often are duplicated - i.e. a user creates a new tag that is the same as (or similar -to) an existing tag. This problem arises when users do not have good visibility of the existing tag portfolio during workflows.
NLP or NER auto tagging predicts too many tags to be useful
Simple NER (Named Entity Recognition) automated tagging can generate numerous tags, covering all the things that are **mentioned** in a piece of content, and not necessarily what the content is **about**.
Poor editorial metadata tagging tools
Tagging tools that make it difficult to annotate accurate and valuable metadata to content or assets. This is a common workflow problem!