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From the Experts:
A Guide to Data in Digital Asset Management

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Authored by:
John Horodyski,

Executive Director, Salt Flats LLC.

Data Is the Foundation: Metadata for Digital Asset Management & More

Data makes everything easier.

Every technology implementation needs to start with good data. Without good data, technology starts to decrease efficiency in an organization because the quality information that supports operations cannot be extracted from disorganized data sources.

As organizations grow in size, evolve, and take on additional market opportunities, change will be a constant for the people, processes, and technology supporting your business. That’s why it is important to start with a data foundation. Remember, your data supports your technology, and the technology supports your process, people, and ultimately your customers.

What is Data?

Data is defined as a collection of observations, characteristics, or descriptions about one or more objects, persons, processes, or companies. It is something we use every day in our personal lives and in our business affairs.

Data can be either structured or unstructured.

Structured data are those identifiable pieces of data, such as name, address, location, etc., that are found in database fields and structured for use. They are easily recorded in spreadsheets or database format. They are highly organized and ready for access, analysis, and visualization.

However, unstructured data is something quite different. It does not have the “structure” of a database or system. Images, videos, text files, even social media communication, are all examples of unstructured data, which we deal with every day in digital asset management. All of these are good content, but they lack the context that is needed for them to be easily found via traditional query methods.

The problem is that businesses are full of unstructured data. Content is often produced without any metadata – that all important context – to search and categorize by, creating a library of files that cannot be properly utilized. An intelligent and robust metadata model and data dictionary will provide that reference for the work, essential to allowing company users to find the content they’re looking for, and to power technologies like AI and machine learning. To use those technologies, you need a solid data foundation to build on.

Starting With a Data Assessment

Analytics, machine learning, and AI are all topics that are in the crosshairs at large organizations everywhere. Being able to properly predict and react to market changes and real-time news updates gives businesses a competitive advantage. The tools that supply decision-makers with those insights are built on top of a data foundation – which enables organizations to understand what is happening in their business, learn from their mistakes, and ultimately anticipate future opportunities for their business.

Therefore, it’s important to start with a data assessment. Conducting a systematic data assessment gives you insight into where your data stands in managing its information effectively. It is the first step in creating a roadmap to consolidate data from various systems, apply consistent metadata, and maximize workflow, security, and your return on investment. The data assessment should explore the following questions:

What is your data?

  • What information is being captured in your data?
  • What format is your data? Is it accessible? What about all the good old data in the archive?

Where is your data?

  • Where is your data stored, both physically and digitally?
  • Which teams are responsible for data storage? Who is managing this?

How will you access your data?

  • How will you distribute your data?

What are you trying to do with it?

  • What is the end goal for your data endeavor? Search? Analytics? Consumer experience? AI?

How are you able to use your data?

  • What state is your data in? Is it authoritative? Is it accurate? Is it being managed?
  • What type of data cleansing needs to take place? Think of using standardized terms or a controlled vocabulary (more on this in later chapters).
  • What can you do, and more importantly not do with your data? Think of licensing and rights management, and compliance issues.

This assessment gives you the opportunity to map out where your data foundation is strong, and where it needs additional support. It gives you a roadmap for action, allowing you to be proactive in how you think about your DAM data.

As you build out your data foundation, it’s important to remember it must account for both structured, quantitative data, and unstructured, qualitative data. It needs to be accessible to the multiple teams in your organization so they can apply it in their own applications, whether that’s within the DAM or an integrated system.

Your data foundation should be three things:


  • Do not create a data foundation in a bubble
  • Look to a few key stakeholders outside of the power users
  • Think of indirect technologies your implementation will affect
  • Collaborate on how to name things to ensure adoption


  • Everyone needs to be speaking the same language for findability
  • If two teams call the same asset by different names, confusion will prevail


  • If you set a data foundation but don’t enforce it, then it is all for nought
  • People will go back to calling data as their first reaction, not as the organization agrees to classify it
  • Set a standard of procedure, set up governance teams, and hold people accountable for their data

Building a data foundation that is cross-functional, consistent and enforced will help you optimize the content lifecycle, and this data assessment will start you on your journey.

Metadata and Taxonomy

Metadata matters.

But what is it?

Metadata for digital asset management refers to the descriptive elements that define and describe an asset. It provides the structure needed to make your content discoverable, accessible in search, and, therefore, more valuable.

Metadata can often be broken down into three main categories:

Descriptive Metadata: Describes a resource for discovery and identification purposes (i.e., information you would use in a search). It can include elements such as title, subject, creator, date, location, and keywords.

Structural Metadata: ​This technical metadata indicates how compound objects are created (i.e. file format, file dimension, file length, size, dimensions, etc.).

Administrative Metadata: ​Provides information that helps manage an asset. Two common subsets are rights management metadata (deals with intellectual property rights) and preservation metadata (contains information needed to archive and preserve an asset).

Data is complex and, like our content libraries, it is growing daily. The best way to manage your data is with the power and rigorous application of metadata. It is the best way to protect and defend digital assets from content clutter and mismanagement.

Once you have your metadata ready, you then need a taxonomy. Metadata and taxonomy are the best of classification friends. A taxonomy classifies and organizes information, and metadata describes the information being classified. Think of taxonomy as the big buckets in which you organize things, and metadata as the essential bits and bobs in the buckets.

More eloquently, taxonomy is the classification of information into groups or classes that share similar characteristics. It is a way to organize information to best solve a business problem based on user needs by exposing relationships between subjects. 

A well-designed taxonomy brings business processes into alignment, allowing users to intuitively navigate to the right content. It is required for meaningful information management and critical to effective findability of content.

Having a taxonomy also ensures compliance with your documented and authoritative sources of knowledge. It creates the common language in a controlled vocabulary for all so that there is a consistent and well-controlled way to use and manage your content. And lastly, it aligns your process to ensure efficiency.

Ultimately, metadata and taxonomy for digital asset management must consider people, process, and technology to get quality data, a critical step in your data maturity. Without the classification and organization of the data, you have nothing. It makes it easier to drive everything we will want to do in terms of access to content and information.

Governance & Your Data

Governance is best defined as a process or framework to ensure that program goals are met during use, now, and for the future. Think of governance as an effective form of risk management. In today’s world, governance is no longer an option.

Governance is not necessarily just a set of rules, but the good practices of organization to allow for careful, and yes, data-driven decision making. To be effective, data governance must be considered as a holistic corporate objective, going beyond IT governance, establishing policies, procedures, and training for the management of data across the organization and at all levels.

Without data governance, at best, opportunities to leverage enterprise data to respond to new opportunities may be lost. At worst, your organization may face the legal consequences of data mismanagement. You want to create that common vision, to understand all dependencies and impacts, and when possible, automate for efficiency.

Good Digital Strategy Defines a Solid Data Foundation

Technology succeeds when it is leveraged to transform data into information, and then information into insights that can generate action and meaning. Collective actions build mutual trust among community members – like your DAM users – establishing knowledge-sharing opportunities, lowering transaction costs, resolving conflicts, and creating greater coherence.

Therefore, a good digital strategy defines a foundation of consistent data to build, improve, integrate, and implement ​a world-class platform that considers people, process, and then technology.

Remember, data is the foundation for your content, all that you do in business, and how you interact with customers. Data is intimately associated with business transactions and in turn the associated actions by people. It should demand all your attention, as data is in everything that we do and drives our success in the future.