Metadata is data about data — the information used to describe digital files. In Digital Asset Management (DAM), metadata is what you add to your brand assets to organize them and make sure the right ones appear when people search.
People are used to searching on Google or using chat-based interfaces like ChatGPT to get information. They expect the same level of accuracy, speed, and results from their DAM. Metadata is the foundation that makes that search quality possible. Without it, assets don’t show up in search results, so your DAM is just a glorified repository for storing files, like a shared server.
Good quality metadata doesn’t happen by accident, though. You need a clear structure for your metadata, as well as defined ownership and governance, to provide a solid foundation for your search experience and your whole DAM — especially at enterprise scale. Without a strategy to document this structure and governance, metadata quality declines over time, making it increasingly difficult for users to find files within your DAM.
First-time users get a guide to building their metadata strategy from the ground up, while experienced pros get practical tips and recommendations for revisiting their established strategy. Use the table of contents and navigation links to jump to the relevant sections.
This guide is produced by AVP and Frontify. AVP provides platform-neutral digital asset management consulting services to help companies get the most from their DAM system. Frontify is the DAM for leading brands — its unified platform combines asset management, brand guidelines, templates, and AI to provide a central source of truth for brands.
Building a metadata strategy from scratch
Getting metadata right from day one shapes how your DAM gets adopted and whether people can actually find what they need in it. Here’s how to build the strategy, not just the field list.
Think beyond search
Search is the most visible benefit of metadata, but it’s not the only one. Metadata is the control layer behind three things:
Brand consistency — people can confirm a file is approved and check they’re using the latest version.
Speed to market — approved assets get published faster, and no one recreates a file they couldn’t find.
Governance and risk mitigation — usage permissions, rights expiry, and legal compliance stay visible, so expired assets don’t slip through.
“Search is the immediate and visible benefit of good metadata. But the true strategic value is that it gives your organization intellectual control and effective business management of all your digital files and assets.”
John Horodyski, Managing Director, AVPStart with minimum viable metadata
“We often use the Dublin Core metadata standard as a starting point. It gives us a set of 15 core fields to start within, then we edit or expand as needed and often end up in the sweet spot of 16–20 core metadata fields.”
John Horodyski, Managing Director, AVPYour metadata strategy needs to balance quality and quantity. Consider how much metadata you need to organize files without placing too much of a burden on the people adding files to your DAM. Some DAM platforms, like Frontify, provide automatic tagging functionality, which reduces the manual work involved in metadata creation.
Start by mapping your core metadata fields, aiming for no more than 20 to reduce the cognitive load when adding new files. Assess whether each field is useful for search, governance, reporting, or automation.
Example core metadata fields
Remember, this is just the starting point for your metadata. Make your metadata schema extensible so it can grow and evolve as your business and asset needs change. For example, if you start using particular tags or keywords to describe assets, they can become new metadata fields in the future.
Choosing metadata field types
According to the National Information Standards Organization (NISO), there are three types of metadata:
- Descriptive metadata — for finding or understanding a resource
- Administrative metadata — for organizing files within a system, with three subtypes:
- Technical metadata — for decoding or rendering files
- Preservation metadata — for the long-term management of files
- Rights metadata — for intellectual property rights attached to content
- Structural metadata — for relationships of parts of resources to one another
Once you’ve got your list of core metadata fields, consider the type of data they will contain. This helps keep your metadata structured and standardized, and helps avoid inconsistent data entries that can affect search results, governance, and consistency.
Different types of metadata will be best suited to different types of data. For each field, define the metadata format — every field needs a defined one, or search quality and governance both suffer from inconsistent entries.
- Use when you need to filter, group, or report consistently.
- A fixed pick list. Only as good as its upkeep — review it on a schedule.
- Use sparingly, where descriptive nuance genuinely needs flexibility.
- Everything required burdens users. Everything optional thins your data.
Metadata for different asset types
While some metadata fields will be used for all asset types — file name, region, brand, approval status — others need to be tailored for different file types and formats. Rich media introduces a lot of unique metadata that needs to be accurately described, organized, and categorized. Some examples for different media files:
- Rights per video element (music, footage, talent)
- Aspect ratio
- Transcript or caption
- Duration
- Cut variations
- Artist
- Genre
- Duration
- Tempo (BPM)
- Audio channels
- Artboard dimensions
- Linked assets
- Color palette
- Software version
- Editable vs. flattened state
Internal vs. external distribution
Consider the different metadata requirements for internal and external asset distribution. For internal-only assets, you may only need to think about the creative and publication workflow — whether files are drafts or approved, and who owns the file. For external assets, you need more detailed data to control usage: approval status, legal clearance, expiration controls, usage restrictions, and distribution tracking.
“Internal metadata is workflow-driven while external metadata is risk-driven. For each asset type, metadata speaks to the roles and permissions within the DAM — what a person can do with the file and, more importantly, what they can’t do with it.”
John Horodyski, Managing Director, AVPReducing friction at upload
Look for ways to make it as simple as possible for people to add metadata when uploading an asset to your DAM, rather than having to add it as a separate task later. One practical option is to connect your DAM to your creative tools — like Figma or Adobe Creative Cloud — so that basic metadata is automatically generated at the point of upload.
“The goal is to capture metadata at the point of creation. Whoever is creating the asset — the artist, designer, marketing intern — should be able to add metadata to the file when uploading it to your DAM.”
John Horodyski, Managing Director, AVPGet stakeholder involvement and buy-in early
Building your metadata strategy shouldn’t be a one-person or one-team job. Get buy-in and input from key stakeholders across the business to help shape it, and start governance from day one. At a minimum, we recommend involving the following people in your metadata project:
- Brand or creative leadership
- Marketing operations
- Legal or compliance executive for insight on rights management
- IT for insight on integrations and governance
- Someone who’s likely to be a power user, such as a marketing or brand manager
You need a variety of insights and experience to shape your metadata strategy into something that works for people across teams and across the organization.
“Your executives define the overall direction and project scope, while practitioners define the daily needs and usability of your metadata. You need both.”
John Horodyski, Managing Director, AVPGovernance and adoption
Metadata must evolve as campaigns, regions, and business models change. Your metadata strategy is an ongoing program, not a one-off task, so strong governance is needed to keep teams following best practices, adopting your DAM, and producing good-quality metadata.
“One of the most common mistakes we see when companies implement a DAM is that there’s no ownership beyond the initial launch.” — John Horodyski
Share ownership across four roles
- Defines the schema
- Maintains controlled vocabularies
- Sets governance policy
- Define what matters for their team
- Maintain domain-specific accuracy
- Approve taxonomy updates
- Enter required metadata at upload
- Apply tags and usage info
- Follow naming conventions
- Set up integrations
- Enforce data standards
- Configure automations
Team training
Proper training helps drive DAM and metadata adoption across the business.
“Regular and mandatory training is really useful. Use short, role-specific training modules and make sure each department has its own metadata champion.”
John Horodyski, Managing Director, AVPWhen planning training for your teams — in-person or remote — ensure the sessions are relevant and adapted to how people will use the system. A designer responsible for adding files into your DAM doesn’t need to know how to add new custom metadata fields or set up specific automations.
Drive adoption with incentives
Telling people metadata is important doesn’t make them use it. Make the benefit visible instead:
- Recognize teams with strong metadata adoption
- Send reminders for incomplete records
- Share reuse and time-to-find metrics so the time savings are visible
- Fast-track approval for properly tagged assets
“People adopt metadata when it benefits them, not when it benefits governance.”
John Horodyski, Managing Director, AVPEnforce governance without slowing teams down
Most DAM platforms have built-in features that handle governance behind the scenes. Here’s how you should be able to configure the metadata:
Required fields are only used for operationally critical information, not to force users to add more when uploading assets.
Templates help duplicate and auto-fill core metadata for recurring campaigns or asset groups.
Licensed content has an automated expiration workflow to retire assets when usage rights expire.
“Governance is essential to your metadata and DAM success. If you can help it feel second nature and avoid slowing teams down, it will help teams adopt your metadata strategy with less friction.”
John Horodyski, Managing Director, AVPAI, automation, and the future of metadata
AI and automation should be important parts of your metadata strategy. Many DAM platforms are adding AI functionality that’s increasingly sophisticated to help accelerate data entry and support ongoing governance.
How AI adds value for your metadata
AI helps speed up metadata creation. It’s best for descriptive and repetitive data, as the system improves by learning from your existing metadata and assets. AI excels in several areas:
However, it’s not the time to give AI free rein over all your metadata just yet. Although it can handle much of the repetitive and simple data entry, your team still needs to create metadata in several areas. Think context, not content:
“Manage your expectations when it comes to AI. Your team is still best placed to understand the particular nuances of each asset — with context from the campaign, your brand, and the wider business needs — that can’t be trained into an AI tool.”
John Horodyski, Managing Director, AVPMetadata best practices for AI assistants and chat-based search
Many DAM platforms are introducing AI assistants and chat-based search functionality. Users can interact with the assistant to ask for specific files — the latest logo, or product photos from a particular campaign. AI assistants and chat-based searches use the metadata in your system to respond to queries and provide the right files. Specifically, they rely on:
- Contextual metadata fields like audience, region, channel, or usage rights
- Structured relationships between different files
- The taxonomy and hierarchy of your DAM
- A library of synonyms and related terms to define brand-specific phrases
- Clean, non-duplicated vocabularies
“Clarity in your metadata becomes even more critical if people are asking AI tools to help them find different files.”
John Horodyski, Managing Director, AVPBefore you roll out AI chatbots or conversational search across the organization, run a few tests to assess the accuracy of the responses. If the assistant struggles, you may need to improve the quality of your metadata first, so it has all the data it needs to be a helpful resource rather than a source of frustration.
Using AI and automation to repair historical gaps
Besides adding metadata to new files, AI and automation can also help fill metadata gaps in existing ones:
- Auto-tag assets with content themes and objects
- Identify logos and brand elements
- Extract text and embedded data from files
- Suggest missing fields
To maintain metadata quality, don’t simply trust the output from AI and automation tools. Use human team members to validate automatically generated metadata — at least at first, until you reach the desired level of accuracy and confidence in the system. You can set confidence thresholds in many AI tools, providing a user-defined minimum score the AI must meet to determine whether its output is accepted automatically or escalated to your human team for review.
“If you use AI to fill in historical metadata gaps, use it to suggest, enhance, and augment your existing data. Don’t let it silently overwrite anything — make sure you know what changes it makes.”
John Horodyski, Managing Director, AVPAnd to ensure AI isn’t silently overwriting anything without your knowledge, make sure you get audit logs for AI-generated metadata. These logs give details of exactly what the AI tool does when it creates new records, edits existing ones, and makes changes to your metadata.
Getting the balance right: humans vs. AI
The simplest way to think about balancing your human team with AI for metadata entry is to give each a specific role: use AI and automation for descriptive and repetitive data, and use humans for context, nuance, rights management, and strategic classification.
“The goal is 70% automated metadata enrichment with 30% human validation. That can vary a little depending on the level of risk your company’s happy with — companies in complex regulatory environments may be more risk-averse and happier with a 50/50 split. Good news is, humans are still needed.”
John Horodyski, Managing Director, AVPMeasuring metadata success
When you build a metadata strategy from scratch, you want to be confident it’s achieving its goals and delivering real benefits to your business.
Early signals of a successful and scalable strategy
When you roll out or update your metadata strategy, there are three early signs that it’s successful and has the desired impact on your business:
Adoption — users from all departments add metadata to their files: they complete fields, use approved terms correctly, and upload fewer assets with missing or vague tags.
Consistency — users apply metadata in a uniform way, without needing constant correction or oversight from the brand or DAM team.
Search behavior — users find what they need faster and rely less on browsing through folders to find files.
After a few months, you may also see indicators that your metadata strategy is able to scale. Your team can add new campaigns, products, or asset types without needing to overhaul your schema, and teams know how to request additions — like new tags or categories — through a clear governance process.
“If metadata is working, operational friction declines measurably.”
John Horodyski, Managing Director, AVPKey performance indicators (KPIs)
You can use several metrics and KPIs to measure the success of your metadata strategy:
| Metric | What it tells you |
|---|---|
| Asset reuse rate | A higher reuse rate shows people can find assets easily, reducing duplicate work and maximizing the value of existing content. |
| Time to find | Faster search times indicate metadata is structured and relevant, letting people locate the right asset without guesswork. |
| Expired asset usage | Fewer people using expired or outdated assets shows rights and expiry fields are being used properly, supporting compliance and reducing brand risk. |
| % of assets fully tagged | A higher percentage indicates your metadata standards are being followed and improves overall searchability and system reliability. |
| Time-to-market improvements | Faster campaign or project deliveries show teams can quickly find and reuse assets, removing bottlenecks in content production. |
| Percentage of total users | A growing proportion of active users suggests the DAM and its metadata deliver value org-wide, not just within a single team. |
| Adoption rate across teams | Strong adoption across the business shows metadata processes are being followed by everyone, not just managed by brand or marketing. |
| Monthly uploads / downloads / requests | Consistent or increasing activity suggests users contribute to and benefit from well-managed metadata and a well-organized DAM. |
| Number and types of searches | Analyzing search patterns helps identify gaps in your metadata model, such as missing tags or unclear terminology. |
| Zero-result searches | Tracking searches that return nothing helps you spot metadata gaps — missing tags, categories, or assets. |
Proof it works
Many DAM platforms have analytics functionality with dashboards to help you track these KPIs. You can share dashboards with your stakeholders to demonstrate adoption, maximize productivity, and make the case for continued investment.
Making the case to leadership
“When you’re making a business case for investing in your DAM — either for ongoing investment or a complete rebuild of your metadata strategy — focus on what executives actually care about. Tie any improvements to operational efficiency and risk reduction for the business.”
John Horodyski, Managing Director, AVPWhile the KPIs above are useful for measuring overall success, not all of them will resonate with budget holders. Executives respond most directly to metrics that translate into cost savings and speed:
- Reduction in asset recreation
- Faster time to campaign
- Increased asset reuse rate
- Reduction in rights violations
To make these metrics compelling, show the direction of travel rather than just presenting current numbers. Establish a baseline on two or three of these metrics before you implement or overhaul your metadata strategy, then track them over the following two quarters. Even a modest improvement — like a 20% reduction in time spent searching for assets — becomes a concrete cost-saving argument when multiplied across the number of people using your DAM each month.
One additional area worth raising with executives is AI search accuracy. If your company has a digital transformation strategy, demonstrating that well-structured metadata improves AI performance within your DAM gives your investment case an additional angle that connects directly to broader business goals rather than just marketing operations.
Revisiting and rebuilding a strategy
Over time, companies often find that their initial metadata strategy is no longer fit for purpose. It hasn’t evolved or grown with their company, or it’s slowing their teams down rather than empowering them. If your team has a DAM but it feels like your metadata strategy no longer works for your business, here’s a guide to help you rebuild it into something that can scale with your organization.
Signs your metadata strategy is failing
Here are some issues to look out for when your metadata strategy isn’t working:
Search issues — teams search multiple times without finding the right assets.
DAM workarounds — teams bypass the DAM when looking for brand assets, maybe emailing the brand or marketing team instead.
Shadow systems — assets are scattered across shared and local drives and Slack threads, rather than centralized in the DAM.
Re-creation, not reuse — users remake assets from scratch because it’s easier than finding the existing file.
Governance problems — teams regularly use expired, off-brand, or unlicensed assets without realizing that’s a problem.
Slow campaign activation — implementing the DAM hasn’t improved time to market for campaigns.
Adoption failure — DAM adoption hasn’t spread beyond the brand or marketing team.
“If the DAM system is being used, but you’re not seeing improvements — in search times, brand consistency, or asset reuse — that suggests your metadata is underperforming.”
John Horodyski, Managing Director, AVPAudit your metadata strategy
Before you make any big changes, you need to understand the root of the issues.
“Most DAM failures are to do with metadata design and governance, rather than a software issue.”
John Horodyski, Managing Director, AVPA metadata audit will help you understand which issues are caused by tool limitations and which come from design and adoption issues. Look at assets added by different teams to get a good overview of adoption levels and metadata standards across the organization, then identify common themes or traits to understand the problems or gaps in your existing setup.
Run a four-layer audit
| Test | What it checks | Common cause when it fails |
|---|---|---|
| Retrieval | Can users find files? Count the steps. | Incomplete metadata or limited filters |
| Structure | Are fields logically designed and populated? | Metadata design issues |
| Behavior | Do people follow the standard when adding assets? | Governance or adoption gaps |
| Data | When was metadata last reviewed? | No ongoing governance |
Rebuild your metadata strategy
“DAMs age and businesses change, so your metadata needs to evolve with it.”
John Horodyski, Managing Director, AVPIf your audit suggests that a lot of your DAM adoption issues stem from your metadata strategy, it’s time to rethink and update it. Start by getting a clear picture of your current reality, as this will help you understand what needs to change:
- What asset types do your teams produce and store most?
- How many brands or sub-brands does your company have?
- How many regions does your company operate in?
- How have the compliance risks evolved since you set up your DAM initially?
- What’s your company’s current level of AI adoption and the goals for AI or automation?
Decide what to keep, revise, or remove
Put together a spreadsheet of all your metadata fields. Mark any fields that are required. Then go through and decide what to do with each field, asking whether it helps with searching, supports asset reuse, reduces risk or compliance exposure, is required for automations or integrations, and whether it’s actually being populated and used.
- Mandatory fields, fields required for automations and integrations
- Fields that help with searching or rights management that aren’t used all the time
Remove — low operational or governance value, rarely used: fields that don’t reflect how teams think about assets and don’t get used.
Simplify overly complex metadata models
Next, look at the fields you’ve decided to keep or revise, and look for opportunities to simplify them.
“More metadata fields don’t necessarily give better search results; less is more. Advanced search depends on metadata being consistently applied, rather than added in vast quantities.”
John Horodyski, Managing Director, AVPReplace free text with controlled vocabularies — reduces typos, spelling errors, and terminology variations, making metadata easier to filter and group.
Use hierarchical taxonomies instead of flat tags — groups related assets together. Avoid very deep, nested hierarchies; three to four levels works well for most organizations (e.g., Brand → Region → Product → Campaign).
Separate required vs. optional metadata fields — reduces the administrative burden on users. Scrutinize required fields to make sure they’re actually all necessary, not nice-to-haves.
You may find it easier to review metadata fields for different asset types — video, audio, image, document — together, rather than looking at all your fields at once.
Clean up inconsistent metadata
Finally, take the time to clean up your existing metadata. The goal is to standardize inconsistencies and remove outdated terminology and near-duplicate tags and fields.
Create, review, and maintain your controlled vocabulary list — define a single, approved set of terms for key fields like categories, campaigns, or product names (e.g., deciding between “UK,” “United Kingdom,” or “GB”).
Map legacy terms to approved terms — identify outdated or inconsistent values and map them to standardized terms so nothing is lost (e.g., mapping “HR,” “Human Resources,” and “People Team” to one approved term).
Automate bulk retagging where possible — update large volumes of assets at once instead of fixing them manually (e.g., replacing all instances of “Summer_2022” with “Summer Campaign 2022” across thousands of files in one go).
Lock deprecated terms from future use — prevent users from selecting outdated or incorrect values by removing or disabling them in your schema.
Provide training resources for teams — short guides or examples showing how to apply metadata correctly in real scenarios, reducing guesswork and improving consistency during uploads.
How AVP supports metadata management
A strong metadata strategy is the difference between a DAM that works for your business and one that becomes another shared drive. Getting it right takes clear structure, ongoing governance, and a platform-neutral partner who understands both.
AVP helps companies build and maintain asset libraries with the metadata needed to keep them findable, organized, and governed at scale. As a vendor-agnostic consulting firm, AVP works across platforms to design metadata schemas, establish governance frameworks, and ensure your DAM delivers long-term value — not just at launch.
Whether you’re building a metadata strategy from scratch, auditing an underperforming one, or navigating the shift toward AI-assisted tagging, AVP brings the practitioner expertise to help you get it right. That means defining the fields that actually matter for your business, setting up ownership structures that stick, and making sure your metadata evolves as your organization does.
AVP also helps teams navigate integrations between their DAM and the broader ecosystem — from creative tools and PIMs to content management systems and ecommerce platforms — so metadata stays consistent wherever assets are used.
