The (Metadata) Advantage: How to Supercharge Search, Tagging and Content Velocity

Kaylie: Thanks, Ian. Hi, everyone, it’s nice to be here today for DAM Week. We’re going to talk today about how to supercharge search, tagging, and content velocity. As Ian mentioned, I’m an Account Manager here at MediaValet. I’ve been with the company for about three years now, so I’ve gotten to see how metadata really empowers all kinds of departments, industries, and use cases across any market, which I’m excited to share with you today.

To start things off, I think it’d be good to get a poll up and going. How is your metadata and tagging handled in your organization today? Is it fully standardized with full governance? Some structure but inconsistent across teams? Mostly manual and ad hoc? Or maybe you don’t really have a strategy. We’ll give everyone a few seconds to answer that one.

Okay, it looks like the results are pretty evenly split between “some structure,” “mostly manual,” and “we don’t really have a structure.” Luckily, you’re in the right spot if you’re looking to get fully standardized with full governance — so listen close today, and we’ll have some tips on how to break through that.

So, as we know, teams are experiencing content velocity problems. Organizations today must deliver content across a growing number of channels and formats — websites, social channels, emails, partner portals, paid media, internal teams — the list goes on. Creating content is rising and creating challenges, but creating content isn’t really the whole issue. It’s about finding and activating the right content fast enough, and that’s where content operations are breaking down.

Some common frustrations we hear from teams facing these challenges: searching for assets is taking too long, teams recreate content they already have, files are mislabeled, different teams describe assets differently, and the right content exists but can’t be found. The root issue behind all of these breakdowns is metadata quality.

So — a great DAM isn’t just magic, it’s all metadata. Many organizations expect a DAM to instantly solve all their problems, and I wish I had a magic wand for you, but a DAM platform itself isn’t going to automatically understand what every image, video, or document contains. What actually powers strong search and ties the DAM together is metadata. Metadata describes the asset to the system, so the system knows what that asset contains, how it should be categorized, where it should appear in search results, and who should use it and when. Modern DAM systems accelerate this process through AI tagging, which can automatically identify objects, people, scenes, and text. In other words: DAM enables search, metadata powers search, and AI makes it scalable.

So what we’re looking at here is a wide-scope view of what AVP listed as the 2026 DAM Trends — metadata being a top priority for organizations in 2026. Why? Because metadata is the foundation of a well-oiled content supply chain. Metadata powers search accuracy, filtering, asset relationships, content governance, AI discovery, and much more. Without metadata, a DAM is just a storage library with nothing to feed off of to find those assets. But with strong metadata, it becomes a discovery and activation platform that powers every other layer of content distribution across all your systems.

Good metadata accelerates every stage of the content lifecycle. Starting with discovery — this is usually where pain starts to show up. If your metadata isn’t structured or consistent, people simply can’t find what they need, and that leads to wasted time or, worse, recreating assets you’ve already created.

From there, it directly impacts reuse. When assets are properly tagged with things like campaign, product, region, and usage rights, teams can confidently repurpose that content across channels — this is where you start to unlock real scale.

Then there’s distribution. Metadata enables you to get the right content to the right audiences, whether internal teams, partners, or external audiences. It powers things like portals, permissions, and personalization — so you’re not just sharing content, you’re delivering the right content to the right audience.

And finally, but just as important, governance. This is where metadata helps maintain control — usage rights, expiration dates, versioning. It ensures what’s being used is actually approved, up to date, and compliant, so all the effort you put into creating branded assets is used consistently with your brand message in the market.

So when we say good metadata accelerates the content lifecycle, we mean it’s not just about organizing assets — it’s about removing friction at every stage, so teams can move faster, stay aligned, and ultimately get more value out of the content they create.

Which brings us to AI. Modern DAM platforms use AI to detect objects and scenes, recognize faces, extract text within images, videos, and documents, identify logos, and generate automatic tags. This allows teams to generate high-quality metadata at scale automatically. It doesn’t replace a metadata strategy, but it does accelerate it. But what’s better than AI? Your own brain. AI accelerates things like object detection, scene recognition, and large-scale tagging — but humans bring things like brand context, human nuance, campaign relevance, and internal taxonomy. The strongest DAM environments combine AI, automation, and a structured metadata strategy into a scalable, unified approach.

Which brings us to the maturity model. Most organizations tend to sit between stages one and two — the biggest gains in velocity come from moving to stages three and four.

• Stage 1 — Basic tagging. This is where most teams begin. Metadata is largely manual, often inconsistent, and usually depends on individual habits. It works to a point, but it’s hard to scale, and search tends to be unreliable. Sounds like some of you might be in basic tagging based on our poll earlier.
• Stage 2 — Structured metadata. This is where we start to introduce standard fields, controlled vocabularies, and defined taxonomies. Things become more consistent, governance improves, and search becomes much more dependable. A good example: we like to tease our former colleague from Australia, Baden, who would tag his DAM assets with “ATV” using the tag “quad” — and I could never find them because I was searching “ATV.” A custom attribute for culturally relevant terminology would have helped a lot there.
• Stage 3 — AI-assisted tagging. This is where AI starts to take on a lot of the heavy lifting, automatically generating tags, improving accuracy, and significantly reducing manual workload. It’s not replacing structure, it’s enhancing it.
• Stage 4 — Intelligent discovery. This is where everything comes together — AI, metadata, and search all working in sync to surface the right content instantly. At this stage, users don’t have to think about how things are tagged or where they are; they just need to know what they’re searching for.

The key takeaway is that this isn’t a rip-and-replace journey, it’s an evolution — each stage clearly builds on the last, and every step forward reduces friction, improves efficiency, and drives more value from your content.

A good example of this is the Jackie Robinson Foundation, who we work with. The DAM they were working with was initially structured for a museum context, but the Foundation’s DAM taxonomy was reworked to reflect how staff actually search for assets — by people, events, and initiatives. This shift made the system much more intuitive and accessible for new users, increasing usage across the organization, and ensuring the archive could serve as a long-term storytelling and operational tool beyond the original scope of the project, once the metadata was effectively set up.

AI does the hard work here too — with MediaValet’s AI face recognition, the Jackie Robinson Foundation (JRF) can instantly locate images of specific individuals across time. That’s a good call-out, since Jackie Robinson had a career that spanned many years, and the system recognizes his face across all those stages. It’s built personalized collections for alumni, donors, and public figures, and surfaced previously unknown or forgotten content — and the list goes on. Metadata and AI have turned the Jackie Robinson Foundation archive into a living, searchable history that powers storytelling, engagement, and fundraising.

Not unlike what we’ve done with Telescope. Telescope moved to MediaValet from an on-premise DAM to ensure their metadata was set up properly. The result: they migrated 10 terabytes and over one million files into the DAM, and their metadata ensured consistent categorization, reliable retrieval, and instant access across millions of assets. In fact, their metadata is retrievable across their entire content workflow — from their DAM, through their creative tools, and back to their PIM — ensuring greater control and flexibility.

I know everyone loves numbers, so we have some to share. Organizations with strong metadata strategies report dramatic reductions in search time, improved asset reuse, fewer duplicate assets created, faster campaign launches, and stronger brand governance. Metadata hasn’t just improved DAM quality — it’s improved marketing speed. That’s what we really want to get across: in the 2026 DAM Trends report, by eliminating the redundancies that come with those frustrations and redundant searches — DAM was able to save teams that reported it an average of 11 hours a week. That’s time that can now be spent on creative work, campaign strategy, and innovation, rather than repeating work that could have been avoided with better metadata.

So, how do you strengthen your metadata? It comes down to five practical steps:

1. Define your metadata taxonomy — the foundation for categorizing content across things like campaigns, projects, regions, products, or channels. Without this, everything else becomes inconsistent.
2. Standardize naming conventions. This sounds simple, but it’s one of the biggest unlocks — when the team follows the same structure for naming assets and metadata, you eliminate a lot of confusion and make search far more reliable. This can be especially important for cross-cultural teams, like Baden’s and mine.
3. Make metadata fields required at upload. This is where you start to enforce consistency — if metadata is optional, it gives people the opportunity to opt out of it. If it’s required, you’re building and strengthening the habit and foundation of your DAM.
4. Use AI tagging to scale. AI helps automate a lot of the manual work, improving accuracy and making things much easier in terms of content volume and time spent.
5. Review your metadata over time. Your taxonomy shouldn’t be static — your projects aren’t static, your industry isn’t static, your teams aren’t static. Just as often as you’re revamping your go-to-market strategy or competitive intelligence, you should be revisiting how you’re searching for and using your assets.

So that brings us to the metadata advantage. Metadata is the backbone of content velocity. Once you move through the stages of the maturity model, you understand that metadata isn’t just about organizing content — it’s the backbone of content velocity. It enables teams to find what they need faster, reuse content more efficiently, and distribute it with confidence. And when you layer in AI, it removes a lot of the manual friction too, freeing up time for teams to focus on managing and actually using content, rather than organizing it.

So whether you’re at the early stages of basic tagging or already exploring AI, the goal isn’t perfection, it’s progress. Each step you take toward a more structured, intelligent metadata strategy has a direct impact on how quickly and effectively your organization can operate.

So with that, I wanted to open it up for questions. I did promise Ian I would do my best, as an East Coast girl, to talk slowly — but I don’t think I did, I think I raced through a lot of that.

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Q&A

Q (from a viewer): What’s the right balance between having enough metadata and not overcomplicating things for users?

Kaylie: That’s a good question — I was actually supposed to have my dog Rory on this call, and she failed to make it! But it’s a good question. It might be best to talk to your Customer Success Manager about how you’re utilizing your DAM and what people are searching for, because we can look at how your users are naturally searching for things in the DAM, and work with you to come up with a plan so you’re set up the way you need to be. So maybe we can get [name]’s email and follow up with them after this. There are also ways to bulk-edit metadata, which helps here too.

Q (from James): How do you handle inconsistent or messy metadata in an existing DAM, versus starting from scratch?

Kaylie: There are ways to bulk-edit metadata in your DAM through categories, so that’s another good conversation for your Customer Success Manager. If you send in an email, I’ll send over a support article that will help, and also connect you with the team that can help you with both of those things.

No more questions? Hey, well, thank you so much, everyone — I love talking, so thanks for letting me talk for 20 minutes straight. Ian, anything else to add?

Ian: Nope, that’s brilliant. Thanks so much, Kaylie, for jumping on — really interesting talk there about metadata. I think there were some great takeaways from that session. Just so everyone knows, at 12:15 Pacific Time we’re jumping on with Alan Lee from the Schulich School of Business, and he’s going to be discussing how, across different departments in his school, they’ve approached content management and shifted from fragmented workflows to a more centralized, stabilized system. So please join us for that. Kaylie, thank you again so much for jumping on, and appreciate everyone joining us today.

Kaylie: All right, thank you so much, everybody. Hi, everyone. Bye bye.