Converting descriptions to verbs & nouns
~2 m
Anyone who has migrated an archive for long-tail revenue generation knows
The problem:
| Descriptive metadata | Workflow metadata |
|---|---|
| often created for search applications | - used in automation (human or AI agent) to affect change |
| inaccuracies invisibly ignored by humans e.g. spelling mistakes | inaccuracies can be BIG problems e.g. “queue the clip with Shaquille O’Neal” |
Cleaning + validating metadata is expensive - why not use AI?
- ⚠️ Workflow Misfires and Automation Errors
- Outdated tags or categories incorrect process / skip steps / fail to log
- Formats or Taxonomy mismatch script failure / wrong process performed
- 🧩 Loss of Context and Misinterpretation
- Descriptive metadata obsolete cultural, technical, or editorial standards
- Ambiguous or biased descriptions misclassification, semantic inaccuracy
- 🕳️ Data Gaps and Inconsistencies
- Older metadata missing fields like resolution, codec, or rights
- Inconsistent formatting & abbreviations can corrupt ingestion pipelines
- 🧠 Poor Decision-Making and Risk Exposure
- Workflow engines make resource (i.e. cost) decisions on flawed metadata
- Wasted effort legal exposure, missed opportunities
- 🔍 Reduced Discoverability and Accessibility
- Failed searches poor multilingual / accessibility discoverability
- Missed revenue can’t find it? can’t sell it
- 🛠️ Increased Maintenance and Technical Debt
- Modern systems + legacy metadata = fragile expensive to maintain
- Band-aids expensive manual overrides, custom parsers
Fundamentally, doing it right now reduces total cost later
Remember - metadata can rot with time - regular cleaning and contextualization makes it an asset, not a liability.