Why tagging matters
Improper or outright-missing tagging leads to frustrations throughout your organization, including:
- User annoyance when people don’t know what’s approved or which assets are current.
- Extra emails to ask about the location of a specific asset — and to answer that query.
- Risk of brand inconsistency if someone publishes or syndicates the wrong data.
- Downstream inefficiencies because without a clear data model, it’s much harder to automate moving forward.
On top of all of this, without proper tagging, you lose reusability.
Assets are usually created for a specific purpose. They might not even get loaded into the DAM until after that goal is accomplished. But that doesn’t mean the asset should only get used that time.
The problem is that in order to find that asset when it meets a future need, you need to be able to recall it.
And metadata tagging enables that. Say you need an image of two people sitting outdoors. Your brand has probably created that image before. With it properly tagged in the DAM, you can easily pull it up to leverage it again. You save the resources and time you would have used to recreate that asset. That means you get more mileage out of already created assets and you prevent your team from needing to use resources now.
The proof is in the financial pudding here. Experts estimate that all U.S. companies could save $32,967 a year if they were able to find already created documents, while large companies could save $9.7 million annually.
Is tagging worth the work?
Data on the search efficiency gains when assets are tagged is fairly lacking — at least, right now. But as more organizations use DAMs and make team members responsible for metadata tagging, we’ll be better able to measure ROI on tagging effort.
To decide if tagging is worth the work for your organization, run a pilot program. Do the work of tagging for a few weeks. Then, over the next year, see if those assets get used more frequently than untagged ones. We’d bet they do — by a long shot.
All told, it’s probably not necessary to tag assets that are extremely simple and quick to create. But if you’ve put effort into making something, you get significantly better ROI if it gets used again. And tags can make sure that it does.
The promise of AI for metadata tagging
Human-based tagging works for now, but there could be a better way. Many vendors have already integrated AI-based tagging functionality and leveraged ML to speed that process for you. With AI-based smart tagging, you take the work hours out of attaching metadata to your assets — at least to an extent.
Still, though, AI can’t yet tag as sophisticatedly as a human can since it primarily relies on visual recognition alone. That means you miss non-visual metadata. As of yet, AI tagging can’t yet replace human oversight. That said, it can do a lot of the legwork for you here.
If you want to explore AI-supported tagging more deeply, our partner, Aprimo, has a great guide on the subject.
Best practices for tagging
- Use tags that hit the main descriptor boxes: who, what, where, and when.
- Rely on taxonomies and establish a controlled vocabulary. Don’t use words that don’t have clear, agreed-upon meanings throughout your organization.
- Double-check that tags are spelled correctly.
- Put naming conventions in place. For example, mandate that all tags are singular and in the present tense, or that state names are spelled out or abbreviated.
- Consider tagging assets with the marketing campaign for which they were originally created. That way, if you develop similar campaigns in the future, it’ll be easy to access those parallel assets.