In today’s multi-platform world, simply put, finding stuff is becoming more complex. In the past, a mere browse through the shelves would suffice. But the digital era brings forth the “hoarding” syndrome. Just think, for example, of your own collection of home pictures – I know mine are in an unmanaged mess.
But before we get into searching, we first need to address quantifying things. This is where a MAM’s role is to be the record keeper of your valuable content and its associated information. More importantly, having a metadata model extensible enough to address the multiple levels and hierarchy of data is key to the success of your search power.
As the amount of content owned, archived and distributed by broadcasters is rapidly growing, it is also evolving, resulting in an exponential expansion of files that must be managed. What was once a one-to-one relationship between the “record” and the media, has evolved into a model where a complex collection of elements (audio, video, text, captions, etc.) forms a record relationship. And don’t even get me started on versioning.
To illustrate what I’m talking about, let’s look at the example of the TV Series “24,” starring Keifer Sutherland. You could annotate an episode with the actor’s name, the actor’s character’s name, the actor’s birthday, and so on … and for each element of that collection (let’s say the source master, the poster, the caption).
Having the ability to define a taxonomy and ontology so that when I specify that “24” ALWAYS has Jack Bauer in all the episodes and that the character Jack Bauer is played by actor Keifer Sutherland, we can then have a way to inherit that information down the tree for any element that is part of that tree: Series/Season/Episode. Then for the users, only saying that “this” video is actually 24/season2/ep7 will automatically inherit/apply all it’s “parent” associated metadata… without needing to enter each individual value. This greatly reduces the amount of data entry (and time) necessary to quantify something when considering the immense amount of content associated with any given record.
But the big impact of the rich metadata engine found in our MAM is its ability to not only search but to discover as well. What I mean is that there are typically two methods of searching: The first is explicit search – the user chooses the necessary fields to conduct their search, and then enters the values to obtain a result, e.g. looking for “Videos” with “Jack Bauer” in “Season 2.” The result is a list that the user must filter through to find what they want.
The second way to search is through discovery, with the MAM’s ability to display facets. For example, I could type “Actor’s height” (6’2″) in “Action role,” “On Location” (Los Angeles). The return would display facets organized by user-defined relevancy, such as Series, Media Type, Actor Name, to then produce a resulting list along with facet boxes that the user can “filter down” within the search.
The above example would show: “I found 12 Videos with Keifer Sutherland as an actor,” and “I found 34 assets shot in Los Angeles.” And then by checking the 12 Videos of Keifer and the 34 in Los Angeles to cross-eliminate, I would find that there are actually three assets of Keifer in Los Angeles. And then you would also see that the character Jack Bauer also has a cameo on “The Simpsons.”
Rich metadata allows us to create relationship between assets at multiple levels. Those various facets allow you to not only navigate through hundreds if not thousands of media assets, but to easily discover specific content as well. And finally, having immediate access to these results for viewing or editing is what makes the Dalet MAM a harmonious ecosystem for not only information but also action/manipulation of said assets.