A guide to the lifeblood of DAM: Key concepts and best practices for using metadata in DAM

8/4/2019 A guide to the lifeblood of DAM: Key concepts and best practices for using metadata in DAM.

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Copyright 2011 Widen Enterprises, Inc.

A guide to the lifeblood of DAM:

Key concepts and best practices for using metadata indigital asset management systems.

By John Horodyski. Sponsored by Widen Enterprises and DigitalAssetManagement.com

Scan for PDF
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A guide to the lifeblood of DAM: Key concepts and best practices for using metadata in digital asset management systems.

Copyright 2011 Widen Enterprises, Inc. All rights reserved.

Do you know what digital assets you have and how to identiy, organize, and describe them? This should not be

rushed, as this is critical to the impact your DAM systems use will have on your overall eciency and, ultimately,

your bottom line. Getting this wrong could damn not only your DAM, but your broader workfows and processes

as well.

Metadata is an asset unto itseland an important one, at that. It provides the structure and inormation needed

to make your assets more accessible and, thereore, more valuable. In other words: it makes them smart assets.

Simply digitizing video and audio les only scratches the surace o their value as digital assets. Their ull potential is

realized by their use and the relevance o the associated metadata. Ater all, how much value does an asset have i

you cant nd it?

This white paper will show you the essential building blocks and best practices o metadata or your digital asset

management system.

What is metadata and what does it mean to DAM?

What is metadata? Metadata is, simply put, data about data. It reers to the descriptive elements that dene and de-

scribe an asset. The National Inormation Standards Organization breaks metadata down into three main categories:

Descriptive metadata describes a resource or purposes such as discovery and identiication(i.e., inormation you would use in a search). It can include elements such as title, abstract,

author, and keywords.

Structural metadata indicates how compound objects are put together, or example, how pagesare ordered to orm chapters (e.g., ile ormat, ile dimension, ile length, etc.)

Administrative metadata provides inormation that helps manage an asset, such as whenand how it was created, ile ormat and who can access it. There are several subsets o

administrative data. Two that are sometimes listed as separate metadata types are rights

management metadata (which deals with intellectual property rights) and preservationmetadata (which contains inormation needed to archive and preserve a resource).

Here are some other key concepts to understand, especially i youre starting your metadata analysis:

Taxonomy: The science o naming and organizing things into groups or classes that share similar

characteristics. It can also reer to any scheme or such an organization o inormationin the

case o DAM, or the purpose o classiying and identiying digital assets.

Taxonomy through metadata - The categories, sub-categories and terms that make up a

taxonomy oten maniest themselves as metadata. Metadata thereore enables more precise

search results and personalization.

Controlled vocabulary: Controlled vocabularies contain preerred and variant terms with deinedrelationshipshierarchical and/or associative. Examples o controlled vocabularies include

glossaries, specialized dictionaries, standard terminology lists, synonym rings, reerence data,

authority iles, domain-speciic taxonomies, thesauri and ontologies.

Thesaurus: A tool that controls synonyms and identies the relationships among terms. It usually has a

preerred term and can be hierarchical but doesnt have to be. For example, dog, pooch, puppy, mutt and

dog is the preerred term.


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3Copyright 2011 Widen Enterprises, Inc. All rights reserved.


Authority files: Typically used or lists o people, organizations etc. e.g. list o public companies,

industry segments, geographic locations. This could be a taxonomy.

Building a metadata strategy: key issues

Now that the oundation has been set with denitions and key concepts, you can get to work on building an eective

metadata strategy. The three key questions you need to answer are:

1. What problems do you need to solve?

2. Who is going to use the metadata, and or what?

3. What kinds o metadata are important or those purposes?

It is important to consider how much metadata you need. Metadata is expensive; it takes valuable time to create the

structure and ensure that it serves your needs. I it does not, then time and money are wasted not nding assets

due to inadequate metadata. Building, testing, inputting and maintaining metadata and taxonomies come with

costs. Implementing metadata may require UI changes and/or back-end system changes. Every metadata eldcosts money and time to implement and adjust to. You need to make your model extensible and avoid the common

mistake o buying tools rst, then guring out the metadata strategy later. Ensure that you account or business

goals and how metadata should contribute to reaching those goals. To help get that going, there are some critical

components o a metadata strategy that need consideration:

Building the right team: Name a team o DAM stakeholders to take the lead in identiying

goals and designing a metadata strategy to meet those goals.

Naming your requirements: Beore getting deeply involved with any vendors, you should be

able to articulate and enumerate (both to the vendor and your own organization) those things

you absolutely need a DAM system to do or your organization.

Making the business case: Identiy all costs, beneits and risks o creating and maintaining

rich metadata. When making ROI calculations, you should account or the resources required to

add, maintain, test, and update metadata and taxonomies.

Metadata specifications: These are always subject to change, but you should have some

sense o what your metadata model will look like, including any controlled vocabularies and

keywords.

Ongoing workflow: Where will metadata come rom? Know who will be responsible or

maintaining and adding metadata, along with what processes theyll be ollowing.

Q/A & Testing: Have a method o measuring the eectiveness o your metadata model

and protocols. Detailed metrics go a long way when it comes time to evaluate and makeimprovements.

There is a considerable eort behind this, but careul observation o these components will help you start your work

and move you in the right direction.


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How Does it All Get There?

One cannot exaggerate the importance o understanding that most o the benets o DAM sotware cant be

realized without good metadata. You need to sell the vision o what the company will gain by having good metadata

in your DAM system. Implementing metadata may require UI changes and/or back-end system changes. Metadata

powers eciency in DAM which is what allows administrators control and end-users the ability to nd what is

needed on a moments notice. Furthermore, every metadata eld costs money and time to implement and adjust to.

There is no benet unless the tagged content cuts costs or improves revenues; you need to demonstrate bottom-line

and top-line benetsalthough bottom-line ones are easiest to prove early on. It is dicult to analyze how much

operations cost today and how much would be saved. Thereore, ocus on the productivity gains.

There are some key metadata elds that you should ocus on:

Basicmetadata Retrieval RightsKnowledge

maintenance

Creator Creator Embargo Date People

Creation Date Title Expiration Date Places

Owner Description Location Restrictions Organizations

Publication Date Subject Usage Restrictions Financial metadata

Title Publisher PricingHarvesting in-line

markup

Consistency is important when applying metadata. Consider the ollowing tags:

President Barack Obama

Barack Obama

President Obama

Obama

Each tag could point to a dierent topic. Yet, undamentally, its the same principal element o the subject o

President Barack Obama that is relevant. Having a principal DAM administrator and/or metadata specialist on

your team will be highly valuable. In act, depending on the size o the organization, there may well be multiple

administrators in various locations responsible or tagging and asset ingestion (i.e. insertion into the DAM library).

I this is the case, it is even more important to ensure metadata consistency.

Last, there is metadata in headers, le systems, naming conventions and query logs that could be extractedautomatically. While automatic classication tools exist and produce results that are more consistent than human-

generated ones, humans are more accurate and better at recognizing nuance. Semi-automated or hybrid

approaches are oten the best way to go, generally with human involvement or distributed manual review

and correction.


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What is your metadata model?

Time and time again, people eel the need, and rightly so, to describe their assets in multiple ways (i.e., rom the

perspectives o multiple users). More oten than not, these exercises can lead to well over 75 metadata elds or

describing assets. Sometimes, this number can rise north o 100inormation overkill in all but the rarest cases.

What you are looking or here is a manageable set o elds with which you are able to discern the most critical

characteristics (administrative, descriptive and technical) o your assets. There is no magic number o metadata

elds, but you might want to shoot or a sweet sixteen: the sixteen descriptors that you need to identiy, organize,

and describe each o your assets. Ultimately, this will be the data your users search against.

What is your taxonomy?

Once you have identied your assets and have a manageable metadata model, its time to consider how this will

be organized in the DAM system, rom on the back end and ront end. End users generally search or assets by a

variety o means:

Faceted classiication systems - searching or assets based upon more than one value ordimension (e.g. Shop by Materialgold, silver, diamond, etc.)

Well-deined older browsing

A structured vocabulary rom the corporate system eeding the DAM assisting in search & retrieval.

Think o your users and how theyll want to navigate your asset library and search or les. While there might not be

a simple one-size-ts-all solution, any good DAM sotware should be congurable enough to meet your needs.

What are the industry standards and which are right or me?

Standards should be reviewed during your strategy development. Standards are created by industry members to

meet the specic needs o that industry. It is wise to use an industry standard i you can nd one that applies andextend it as needed. You should pick standards that are extensible so that you can add your own namespace (or

other accepted extension).

Sometimes content owners require vendors to o er some level o collaboration to enable automated content

interchange and interoperability between sotware tools. It is impor tant to remember that standards are valuable or

ecient, precise, ederated search and retrieval across repositories, as well as automating workfows, distribution,

and integration with other business systems. Indeed, standards adoption results in huge cost savings due to the

eciencies created.

Examples o metadata standards to consider are Dublin Core, PRISM, (PRISM DIM2), METS, ONIX, XMP, MARC,

IPTC Headers, GILS, SCORM, IMS and JDF which one(s) you use should depend on your businessobjectives.


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Benefts o metadata

Some people waste more than 40 percent o their time searching or existing assets and recreating them when they

arent ound. This lost productivity, and redundancy can get very expensive. The key to avoiding these unnecessary

costs is good metadata to aid and assist in search and retrieval. Other benets o metadata include:

Higher ROI based on increased sales through improved product ind-ability, partner cross-sellsand up-to-the minute updates to advertising

Cost-cutting through resulting rom ewer customer calls (due to substantially improvedwebsite sel-service) and more eicient CSR responses

Improved regulatory compliance (i.e. avoidance o penalties or breaches or regulations)

Reduction in redundancies in work and data storage.

More effective rights enforcement resulting in less loss o revenue due to piracy

Metadata & taxonomy governance

The best way to plan or uture change is to apply an eective layer o metadata governance or your DAM system.

There is more to maintaining the metadata than just maintaining the taxonomy and metadata specications.

Vocabularies must change over time to stay relevant. This goes or new terminology being added to assets as well

as synonyms and/or slang terms. The DAM sotwares user interace (UI) might need a reresh or redux according to

user needs and demands. One great way to make sure the system and metadata schemes are meeting the needs

o their users is to oer them a standard change request orm and a procedure or accepting or denying the change

request. And any updates should take place at published times, only, to provide lots o notice and not a ect the

users in the DAM.

Measuring metadata and taxonomy quality

While oten neglected ater implementation, metadata demands ongoing monitoring and management. Dont orget

Q/A and testing as they are critical to your success. Testing should begin very early in the process. In act, start

testing as soon as the rst assets are loaded into DAM library.

Its important to note that metadata & taxonomy development is an iterative process and you will need to solicit

ongoing eedback rom your users. Use both qualitative and quantitative measures, and remain fexible throughout.

You should be gauging things such as:

consistency

appropriateness o tags

time to complete tasks

reaction to search results

useulness o training materials

user satisaction


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Best practices

Using metadata in a DAM system takes work, but once you get going, it will be your greatest asset. Some best

practices to adopt include:

Start with a ew metadata ields that are relevant to all assets and gradually move on to groupso less universally applicable ields (those that are speciic to certain ile ormats, products,

divisions o your organization, and so on).

Avoid overloading your users with metadata ields.

Have a subject matter expert analyze your content to inorm decisions regarding categories and tags.

Have a midpoint check-in with stakeholders to ensure youre on the right track and build ongoingconsensus (e.g. every three months).

Be prepared to adjust m etadata and taxonomies as your business needs evolve.

Practical metadata rules

Here are some practical metadata rules to ollow:

Develop an incremental, extensible process that identiies and empowers users, and engages

stakeholders with eedback loops, user tes ting and evaluations.

Do a quick implementation that provides measurable results as quickly as possible and record them.

Repurpose assets as oten as possible.

Accept that it wont be perect; all metadata schemes can be improved. Just ensure that what

you have in place meets your needs, and make adjustments when thats not the case.

Implement good governance policies.

Content is no longer king. The user is.

I you have great content and no one can nd it, the value o the content is diminished. You need to understand how

your users and customers want to interact with assets beore designing your metadata schemes. I you carry those

user needs through to the back-end data structure, youll empower users with the categories and content attributes

they need to lter and nd what they want.

Metadata shouldnt be rushed. Take the time to leverage best practices like usability testing to determine needs and

validate your metadata and taxonomies. Remember that metadata is a snapshot in timekeep it up to date and

let it evolve. Keep your eyes on your companys goals, as the best metadata design is the one that increases the

revenue o the company by harnessing the power o your data about your data.


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About the Author

John Horodyski is Principal, DAM Education (http://www.dameducation.com), a DAM consulting agency ocusing

on DAM education & training. John is also the Manager, Digital Programming, Product Development at the CBC

(Canadian Broadcasting Corporation). John also serves as Director o Marketing & Business Development or

Wrinkled Pants, an educational sotware studio ocused on the development o education and literacy based apps

or the iPad.

John teaches a graduate course at San Jos State University, School o Library & Inormation Science in Digital

Asset Management. John spent many years at Electronic Arts where he managed their global digital asset

management system as well as being a producer within the EA Sports and online divisions. He has published

proessional articles and presented at numerous conerences on digital media, metadata in video games and

taxonomy design and continues to o er DAM training & consulting.

John holds a Masters Archival Studies and Masters Library and Inormation Science rom the University o British

Columbia and is the Managing Editor to the Journal o Digital Asset Management.
http://www.dameducation.com/http://www.dameducation.com/

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A guide to the lifeblood of DAM: Key concepts and best practices for using metadata in DAM