Introduction

Metadata is data about data; this is 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 metadata, 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.

This guide is designed to help first-time DAM users and experienced pros: First-time users get a guide to building their metadata strategy from the ground up, while experienced pros get some practical tips and recommendations for revisiting their established metadata strategy to make it more valuable for their organization. Use the table of contents and navigation links within the guide 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.

Table of Contents

• Introduction
• 01 — Building a metadata strategy from scratch
  • Think beyond search
  • Start with minimum viable metadata
  • Example core metadata fields
  • Choosing metadata field types
  • Metadata for different asset types
    • Video file
    • Audio file
    • Design/Creative file
  • Internal vs. external distribution
  • Reducing friction at upload
  • Get stakeholder involvement and buy-in early
• 02 — Governance and adoption
  • Metadata ownership
    • Central owner (DAM manager, Information architect, Digital librarian)
    • Business / Department owners (Marketing manager, Product lead, Design manager)
    • Point-of-entry owners / Metadata creators (Designer, Videographer, Agency partner)
    • Technical oversight (IT team, Data governance team, Security team)
  • Team training
  • Drive adoption with incentives
  • Enforce governance without slowing teams down
• 03 — AI, automation, and the future of metadata
  • How AI adds value for your metadata
  • Metadata best practices for AI assistants and chat-based search
  • Using AI and automation to repair historical gaps
  • Getting the balance right: Humans vs. AI
• 04 — Measuring metadata success
  • Early signals of a successful and scalable metadata strategy
  • Key performance indicators (KPIs)
  • Next steps: Making the case to leadership
• 05 — Revisiting and rebuilding your metadata strategy
  • Signs your metadata strategy is failing
  • Audit your metadata strategy
  • Rebuild your metadata strategy
  • Decide what to keep, revise, or remove
  • Simplify overly complex metadata models
  • Clean up inconsistent metadata
• 06 — How AVP supports smarter metadata management

01 — Building a metadata strategy from scratch

When you’re implementing a DAM system for the first time, getting metadata right will make all the difference to how your system gets adopted and whether users can easily find your assets. Here’s how to build your strategy from the ground up and get it right from day one.

Think beyond search

Search is the most visible benefit of metadata, but it’s so much more than that. Think of it as the control layer that enables the following:

Brand consistency: Users can check that files are approved and that they’re using the latest version of a brand asset.

Speed to market: Users can publish approved assets more quickly and avoid recreating files from scratch when they can’t find the originals.

Governance and risk mitigation: Users and admins can check usage permissions, rights expiry dates, and legal compliance to ensure there are no expired assets in the DAM.

If you think about metadata only in terms of search functionality, you downplay its strategic importance and risk under-investing in your metadata process from the start. “Search is the immediate and visible benefit of good metadata,” explains John Horodyski, Managing Director at AVP. “But the true strategic value of good metadata is that it gives your organization intellectual control and effective business management of all your digital files and assets.”

Start with minimum viable metadata

“We often use the Dublin Core metadata standard as a starting point,” says Horodyski. “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.”

Your metadata strategy needs to balance quality and quantity. Consider how much metadata you need in order 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

• File name/title
• Audience
• Dates (creation, uploaded, embargo, expiration)
• Creator
• Channel
• Campaign or initiative
• Asset type
• Brand or business unit
• Region
• Description
• Owner
• Rights: usage rights/expiration
• Language(s)
• EXIF Data (format, size, resolution, length, dimension)
• Approval status

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 will help keep your metadata structured and standardized and help 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:

Structured field: This controls the format and structure for a metadata field, such as “date” or “file type.” Use structured fields when you need to filter, group, or report on data consistently.

Controlled vocabularies: This creates a controlled list of options for users, with a drop-down menu or pick list of pre-filled items. Use controlled vocabularies when consistency is needed to group assets by brand, campaign, or product line, for example. And they’re only as effective as their regular maintenance, so review periodically and keep them up to date.

Free text: When the metadata provides extra information or context to enhance descriptive nuance, it needs flexibility, such as in a “Notes” field.

Most of your metadata will use structured fields or controlled vocabularies, as that provides standardized data needed for automations, permissions, publishing, or compliance. But allowing some flexibility and free text will allow you to capture some data that’s harder to fit into structured boxes.

When choosing metadata types, you should also consider whether fields are required or optional. Making everything a required field places a heavy administrative burden on users, but making too many fields optional reduces the quantity of metadata in your system, affecting the overall quality and usability of that data.

Metadata for different asset types

While some metadata fields will be used for all asset types (such as file name, region, brand, or approval status), others will 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. Here’s some examples of metadata for different media files:

Video file

• Rights per video element (music, footage, talent)
• Aspect ratio
• Transcript or caption
• Duration
• Cut variations

Audio file

• Artist
• Genre
• Duration
• Tempo (BPM)
• Audio channels

Design/Creative file

• 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 to provide more detailed data to control their usage: approval status, legal clearance, expiration controls, usage restrictions, and distribution tracking.

“Internal metadata is workflow-driven while external metadata is risk-driven,” says Horodyski. “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.”

Reducing 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,” says Horodyski. “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.”

Get 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,” says Horodyski.

02 — Governance 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,” says Horodyski.

Metadata ownership

Get clear on who owns metadata within your organization, and when they should get involved. Ideally, ownership will be shared across a few key stakeholders, rather than left with one team, as this helps with company-wide buy-in. Here’s an example of how responsibility could be shared across teams after initial implementation:

Central owner (DAM manager, Information architect, Digital librarian)

• Define the metadata schema
• Maintain controlled vocabularies
• Set governance policies

Business / Department owners (Marketing manager, Product lead, Design manager)

• Define what metadata matters for their department
• Maintain accuracy of domain-specific fields
• Approve taxonomy updates (e.g., new product categories)

Point-of-entry owners / Metadata creators (Designer, Videographer, Agency partner)

• Enter required metadata during upload
• Apply tags, descriptions, and usage info
• Follow naming conventions

Technical oversight (IT team, Data governance team, Security team)

• Set up integrations with other systems
• Enforce data standards and compliance
• Set up key automations (e.g., AI tagging and validation rules)

Team training

Proper training helps drive DAM and metadata adoption across the business. “Regular and mandatory training is really useful,” says Horodyski. “Use short, role-specific training modules and make sure each department has its own metadata champion.”

When planning training for your teams — whether in-person or remotely — 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

When it comes to metadata, unfortunately, it’s not a case of “if you build it, they will come.” Simply telling people you’ve got a new metadata strategy, or that it’s important for properly categorizing files in your DAM, isn’t going to encourage people to use it.

The biggest advantage of good quality metadata is that it makes everyone’s work easier. People waste less time searching for files, recreating files that already exist, or going back and forth with the brand team trying to get sign-off on a new asset. You can make those benefits visible and tangible in several ways:

• Providing public recognition and praise for teams that have high levels of metadata adoption
• Sending automated reminders for files with incomplete metadata records
• Sharing key metrics such as asset reuse and time-to-find to make time savings visible
• Communicating the benefits of high-quality metadata
• Approving properly tagged assets for publication more quickly than improperly tagged ones

“People adopt metadata when it benefits them, not when it benefits governance,” says Horodyski. “You need a way to incentivize metadata adoption and the behaviors you want to see.”

Enforce 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 information 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,” explains Horodyski. “If you can help it feel second nature and avoid slowing teams down, it will help teams adopt your metadata strategy with less friction.”

03 — AI, 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 will improve by learning from your existing metadata and assets.

AI excels in several areas of metadata creation:

• Object recognition to automatically create tags
• Transcription of audio and video assets
• Automatic captioning of videos
• Color detection to automatically create tags

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. For example:

• Brand-specific metadata
• Campaign context
• Strategic categorization
• Rights and usage information

“Manage your expectations when it comes to AI,” advises Horodyski. “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.”

Metadata best practices for AI assistants and chat-based search

Many DAM platforms are introducing AI assistants and chat-based search functionality. Users can search or interact with the assistant to ask for various things, including specific files such as 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 a few things:

• 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

AI will be most successful at finding assets that have clear and complete metadata. “Clarity in your metadata becomes even more critical if people are asking AI tools to help them find different files,” says Horodyski.

Before 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 to ensure 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 using AI and automation to add metadata to new files and assets, it can also help to 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,” recommends Horodyski.

And to ensure AI isn’t “silently overwriting” anything without your knowledge, ensure 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
• Use humans for context, nuance, rights management, and strategic classification

“The goal is 70% automated metadata enrichment with 30% human validation,” says Horodyski. “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.”

04 — Measuring 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 metadata 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: Whether 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: How well and frequently 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. “If metadata is working, operational friction declines measurably,” explains Horodyski. 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.

Key performance indicators (KPIs)

You can use several metrics and KPIs to measure the success of your metadata strategy:

Asset reuse rate: A higher reuse rate shows that people can find assets easily, reducing duplicate work and maximizing the value of your existing content.

Time to find: Improved search efficiency, measured through faster search times, indicates that metadata is structured and relevant, allowing users to locate the right asset without guesswork.

Real-life example: Caribou Coffee estimates the Frontify DAM has saved more than 5 hours of search time across teams each week.

Expired asset usage: Fewer people using expired or outdated assets demonstrates that key metadata fields (e.g., rights information and expiry fields) are being used properly, supporting compliance efforts and reducing brand risk.

Percentage of assets fully tagged: A higher percentage of assets with complete metadata fields indicate your metadata standards are being followed and improve overall searchability and system reliability.

Time-to-market improvements: Faster campaign or project deliveries indicate that teams can quickly find and reuse assets, removing bottlenecks in content production.

Real-life example: MANN+HUMMEL saw 3x faster creative workflows across teams since implementing its Frontify DAM.

Percentage of total users: A growing proportion of active users suggests the DAM and its metadata deliver value across the organization, not just within a single team.

Adoption rate across teams: Strong adoption levels across the business show that metadata processes are being followed by everyone, rather than being managed by brand or marketing departments.

Number of monthly uploads/downloads/requests: Consistent or increasing activity levels suggest that 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, enabling continuous improvement.

Zero-result searches: Tracking the number and frequency of searches that return zero results in your DAM helps you spot metadata gaps, such as missing tags, categories, or assets.

Real-life example: Bosch saw more than 8 million asset downloads in 12 months after implementing the Frontify DAM.

Many DAM platforms have analytics functionality with dashboards to help you track these key performance indicators. You can share dashboards with your stakeholders to demonstrate adoption, maximize productivity, and make the case for continued investment.

Next steps: 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,” recommends Horodyski. “Tie any improvements to operational efficiency and risk reduction for the business.”

While the KPIs in the previous section 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.

05 — Revisiting and rebuilding your metadata 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 you can 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 your DAM when looking for particular brand assets — maybe they email the brand or marketing teams.

Shadow systems: Assets are scattered across shared and local drives and Slack threads, rather than centralized in your 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 your DAM hasn’t improved time to market for your campaigns.

Adoption failure: You haven’t seen DAM adoption spread beyond the brand or marketing team.

All these point to a metadata strategy that isn’t working for your business. Horodyski explains, “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.”

Audit your metadata strategy

Before you make any big changes to your metadata strategy, 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,” says Horodyski.

A metadata audit will help you understand which issues are caused by tool limitations and which come from design and adoption issues.

A four-layer audit gives you a much deeper understanding of your metadata strategy and how it’s currently performing. Use the following tests:

Retrieval test: Can users find files within your DAM? Get them to show you their search process and count how many steps it takes to successfully find a file.

Structure test: Are metadata fields logically designed and consistently populated?

Behavior test: Do users follow your DAM and metadata standards when adding, updating, or removing assets? Assess users from multiple teams, regions, and roles.

Data test: When was the metadata on a random selection of assets last reviewed and updated?

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 across those assets to help you understand the problems or gaps within your existing metadata strategy and DAM setup.

Test	Findings	Likely causes
Retrieval test	Users take 10+ clicks to find specific files within the DAM	Incomplete metadata or too-limited filtering options
Structure test	Metadata structure is unclear or contains lots of redundant fields	Issues with metadata design
Behavior test	Metadata fields are empty or inconsistent	Problems with governance or adoption suggest a limited understanding of how to complete metadata
Data test	Metadata hasn’t been updated on >50% of files over 12 months old	No ongoing metadata governance

Rebuild your metadata strategy

“DAMs age and businesses change, so your metadata needs to evolve with it,” says Horodyski. Here are three steps to update or completely rebuild your metadata strategy.

If 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 to improve your metadata:

• 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. Ask the following questions:

• Does it help with searching (for example, used for filtering or categorizing assets)?
• Does it support asset reuse?
• Does it reduce risk or compliance exposure, such as for rights management?
• Is it required for running automations? Do those automations run frequently?
• Is it required for integrations with other tools? Are those tools still used across the organization, or have you switched to an alternative?
• Is the metadata field actively populated and used?

Your goal is to group every field into one of three categories:

Category	Metadata type	Example
Keep	High value, consistently used	Mandatory fields, fields required for automations and integrations
Revise	Valuable, inconsistently used	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 metadata 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,” says Horodyski. “Advanced search depends on metadata being consistently applied, rather than added in vast quantities.”

These core principles will help you look for opportunities to simplify your metadata models:

Replace free text with controlled vocabularies: This reduces typos, spelling errors, and terminology variations, making your metadata easier to filter and group.

Use hierarchical taxonomies instead of flat tags: This helps group related assets together. But avoid creating very deep, nested hierarchies. Three to four levels should work well for most organizations. For example: Brand → Region → Product → Campaign

Separate required vs. optional metadata fields: This reduces the administrative burden on users entering metadata into your system. Scrutinize the 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 (e.g., video, audio, image, document) together, rather than looking at all your metadata fields at once. That way, you can consider what’s actually needed for that type of asset without looking at your metadata on a field-by-field basis.

Clean up inconsistent metadata

Finally, take the time to clean up your existing metadata. Your goal is to standardize any 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. For example, decide whether your organization will use “UK,” “United Kingdom,” or “GB” to avoid fragmented search results.

Map legacy terms to approved terms: Identify outdated or inconsistent values and map them to your new standardized terms so nothing is lost. You might map “HR,” “Human Resources,” and “People Team” to a single approved term, such as “Human Resources,” to unify search and reporting.

Automate bulk retagging where possible: Use tools within your DAM or automation scripts to update large volumes of assets at once instead of fixing them manually. For example, automatically replace 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 metadata schema. This ensures that once you retire a term like “Internal Use Only (Old),” it can’t accidentally be reintroduced by new uploads.

Provide training resources for teams: Create short guides or examples that show users how to apply metadata correctly in real scenarios. For example, a quick reference sheet demonstrates how to tag a product image vs. a campaign video, reducing guesswork and improving consistency during uploads.

06 — How AVP supports smarter 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.

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