Are you maximizing the value of your digital asset management (DAM)? Understanding the complexities of DAM lifecycles and building efficient workflows can be a daunting task. DAM vendors often do a very good job of getting the DAM up and running, but do not have the deep understanding of your assets and business needs.
If you plan on implementing a new DAM, spend the extra time to gather requirements. Not only will this help you design an efficient DAM workflow, but it will also provide you the information needed to make better decisions when selecting a DAM solution.
If you have an existing DAM solution, understanding how the end users want and need to work with the assets will provide clues on how to improved your DAM workflow. There are almost always opportunities to streamline workflows and improve efficiency, but this can only be done if you have a comprehensive understanding of your assets and your users needs.
A DAM lifecycle consists of five major processes: Create, Ingest, Manage, Distribute and Archive.
The previous blog Improve DAM Workflows Without Breaking the Budget, examined opportunities in the first two processes: Create and Ingest. This blog will examine the last three processes: Manage, Distribute and Archive. Each process consists of complex workflows that offer opportunities to improve efficiency.
Once the assets have been ingested, the power of the DAM software is utilized to “automagically” enhance, manipulate, index and control access to the assets. The mechanisms are hidden from the DAM user, but provide ways to allow users to quickly and easily find and repurpose the assets. Other mechanisms provide:
- Security through granular access controls permissions to asset
- Transformation of assets to produce thumbnails, previews and other desired file formats
- Version controls to preserve the original assets
- Control of digital asset metadata classification and standardized taxonomy
- Audit trail of asset usage
- Integration to other systems though API end-points
Leveraging these internal DAM mechanisms can often be done through configuration. The default or initial configuration may not take full advantage of the DAM’s full capabilities or may not be optimized for your needs. As business requirements change or evolve, so should the workflows to ensure the DAM workflow is designed to maximize efficiency and streamline the workflow process for the users.
Having consistent, reliable metadata provides many opportunities for building efficiency, such as automatically routing the assets to the appropriate editor and notifying the teams when new content has arrived for editing or review. For example a photo that has a Category of “S” for sports could automatically be routed to the sports desk for editing. This type of workflow is easily configured and eliminates the need to scan through content not intended for that team.
Providing drop down lists of standardized taxonomy lists allows editors to select the correct terms, which minimizes typing and thus typos. This also ensured that the metadata remains consistent and matches the organization’s standards.
An interesting exercise is to export the metadata from the DAM. There are often opportunities to clean up typos by identifying commonly misspelled or unapproved terms. It is easy to identify these issues and normalize the data by updating the database with the correct terms. This is also a good exercise to perform on other database fields such as date fields.
In some ways, this is simple housekeeping, but far too often overlooked, especially when data has been ingested from previous systems that did not provide strong governance around metadata. If your company is in the process of replacing an existing DAM, conducting an extensive content inventory will provide the opportunity to identify metadata issues. These issues can be resolved and normalized as part of the extract, transform and load (ETL) process as assets are ingested into the new DAM.
Search is one of the most powerful components of a DAM solution. Modern DAM systems use high-end, full featured search engines such as Apache Lucene / SolR, Autonomy, MS Fast and others. These search tools have many features that are often overlooked (see chart below) and configured suboptimally for your specific needs. This provides an opportunity to improve the user experience by making helping users to quickly and easily find and repurpose assets.
The default installation of your DAM search will only produce modest results. A deep understanding of your assets and how they are used is needed to optimize the search tools. Most of these types modification can be done through configurations changes to the search tool.
Here are a few common features offered by most DAM search tools.
|Fielded search||Search on a value(s) in one or many specific fields or parameters|
|Search within a search||Search within the search to allow our customers to refine their initial search|
|Synonyms||Synonyms associate alternate terms that have the same meaning such as Blue, which would also include: “Azure, Cobalt, Navy, Sapphire, Cerulean or Indigo”|
|Stemming and Term Expression||Stemming reduces the words "fishing", "fished", "fish", and "fisher" to the root word, "fish". It also works the opposite direction taking the string “fish” to return “fisherman”, “fishing” and "fished".|
|Stop Words||Removes stop words from the query (e.g., “a”, “the”) so they are not part of the search. Must be able to add, modify and delete stop words list while system is running without the need to re-index.|
|More Like This||Provide a link that returns results that include similar assets based on relativity of the metadata.|
|Did you mean?||When searching a custom dictionary, a thesaurus would be used to suggest other possible search terms “Did you mean:” This is especially useful when typos occur or searching for complex or unusual names.|
|Relevant Searching||After you search for an item, as you pick a result, there should be a separate area that will search on "Like" items.|
|Facetted Filtering||Results must include facetted filtering options based on metadata within search results to allow for quickly filtering down results.|
|Query Term-based Rules||Business rules for term or terms that trigger spotlighting of search results i.e., when a specified term is used it would trigger a ranking event to move content to the top of the results.|
|Boosting||Ability to artificially influence relevancy score based on business rules i.e. published assets might have a relevancy “boost” field that increases the ranking of that asset|
|Static Ranking||Ability to elevate a specific result into the search results at a specific position based on a user’s search|
|Statistical relevancy||Results are ranked based on statistical analyses such as term frequency–inverse document frequency (TF-IDF), keyword biasing|
|Natural Biasing||Rules that bias the ranking results based on natural or intuitive affinities in the content, for example, distance the term appears from the beginning of the document, or favoring later dates over earlier dates and higher currency values over lower currency values. Must be able to turn these on or off while system is running without need to re-index.|
|Popularity biasing||Content in the index can be boosted in rank for a particular search term if it has been explicitly selected for a search on the term in the past.|
|Field weighting||Individual fields or parameters can be weighted so that results appearing in some fields are more relevant for the document than others (for example, in HTML data, a term found in the TITLE tag generally means more than if it is found in the BODY tag). Must be able to change these while system is running without need to re-index.|
Business rules can also be designed to leverage the assets. For example leveraging metadata such as a project number or assignment number can be automatically linked within the DAM search index. When a user searches for a photo on Water Conservation, a facetted result can be returned that includes linked videos, stories and even model releases, contracts describing asset usage. A wide range of options could be designed to hide assets that have rules based on asset usage restrictions or embargoes, coupled with the access control lists would hide or show assets based on permission level or even business unit.
If your organization receives high volumes of assets, it may prove difficult to find the right assets even if properly tagged. One approach to solving this is to use known data to boost your search results. Let’s look at a big event such as the Oscars. Thousands of images will be ingested in a very short period of time. Business rules could be established to show only published content first when the user performs a search.