I have been active in a number of metadata standards organizations over the years. While the standards development process can sometimes be painfully slow, I recognize there is enormous value in being involved in such efforts. I have come to believe that if one takes the time to study and understand the design considerations behind the data structures, which make up these standards, they will develop new insights and a deeper understanding of how that industry operates.

When designing new DAM systems, I always look to existing metadata standards, which I know have been painstakingly debated through committee. While there is no such thing as a perfect metadata standard, I believe that incorporating some of these elements into my work will likely help me avoid common data modeling mistakes and ensure I have included important data modeling considerations into my design.

I don’t limit my search for good metadata models to just those standards, which have derived from the specific industry vertical with which I am working. Due to the malleability of digital content, industries verticals that were once considered separate and distinct from each other are now all beginning to converge. There is a wealth of experience and knowledge that can exist inside these other standards from people with vastly different work experiences than mine, of which I am interested in taking advantage. If I think the industry I am working in will eventually experience their same challenges, I will be able to anticipate them by using aspects of these other standards.

One good example of this occurred about year ago, when I was looking for a well designed and versatile data structure to store metadata about creative artists. I wanted to capture the type of metadata that would enable me to uniquely identify a person but in addition, store a variety of metadata elements, which would describe how that person’s identity had changed over time. I was certain I was not the first data designer to have this need. I did not want to build such a complex data structure alone because it would likely be inferior to one that was collectively defined by a knowledgeable standards community. It took me about three days of searching but I finally found what I was looking for when I came across the “Encoded Archival Context – Corporate Bodies, Persons and Families (EAC-CPF)” metadata standard. It was actually more than I was looking for which was even better! The standard was reasonably new and was developed by the international library and digital archive community. Clearly, this was a community, which had expertise and knowledge in managing the identities of people, groups and organizations far beyond mine. I was delighted to discover that the standard support complex scenarios far beyond my immediate needs.

It should not be surprising that the library community had developed such a standard as this, to address the challenges associated with capturing data about an individual or group whose identifying information may change over time. An example of this might be a young woman who publishes her master’s thesis to complete her University studies. After she finishes her schooling, she gets married and changes her last name. Several years go by and she publishes more writings under her new marital name. Perhaps, she decides to sometimes use a “pen name” for some of her more controversial writings. Later, she returns to University and completes her doctorate and her name changes again. Perhaps, she is divorced or widowed and later re-marries, and again, changes her marital name. Finally, her writings are so influential, she is receives damehood (the female equivalent of knighthood) by the British Monarchy. Many works, many names but all from the same person.

The EAC-CPF standard is designed to capture this level of complexity. It was well worth the time it took for me to find the standard. I could confidently design its data structures into my core data model because I was certain it would not only meet my current needs but could support any future needs I might have.

I encourage you to do the same. The ideas and knowledge expressed within metadata standards can be leveraged in new and useful ways if you take the time to understand them. Look outside your immediate industries standards and recognize that other industry verticals may have already experienced the challenges you are now facing.