As I was working on a presentation I gave recently on the data model our Dalet Galaxy MAM system is built upon, I realized that looking at the evolution of this data model was a nice way of explaining it. It only made sense to share it with a wider audience. By illustrating how media assets are tracked and cataloged within a MAM system, and how that model has changed significantly over time, I hope to provide a deeper understanding of the changing needs in our industry and how we can not only continue to address these needs, but also begin to predict and plan for new ones.
Let’s take look at what I call the “Dark Ages of MAM,” when our operations were almost exclusively tape-based, and there was no real MAM system. What we had were tapes with stickers (“metadata”) on them or, best case scenario, a tape management database.
Figure 1 – The Dark Ages – Tapes and Stickers
Figure 2 – The Stone Age – One file at a time
Then, as professional media workflows started to introduce file-based workflows, we saw the first MAM systems appear, i.e., a digital catalog to organize and track your media assets. This is a time I call the “Stone Age.” The asset was represented in a very simple way: one descriptive metadata set that pointed to one media file (audio or video). This was intended to allow users to search for and find their media assets, along with some information on them.
Figure 3 – The Iron Age – Multiple versions of the file
Then, things got a bit more complex in the “Iron Age.” We no longer had a single file attached to a metadata record. You needed multiple versions of that media asset, in multiple formats; let’s say one version for proxy viewing, and a few different versions for archiving, distribution to FTP or web sites, etc.
And then again, as time went by, things became even more advanced, and we reached what I would call the “Industrial Age.” The asset was not just a single media file anymore; it became a combination of many individual building blocks, with a master video track, individual audio tracks for multiplelanguages, caption or subtitle files, and even secondary video files and still images. And from this you then had to create different “virtual versions,” each with a different subset of files and their own specific metadata, in order to manage and track the delivery to the many new linear or non-linear platforms. And of course, all of these needed to be linked in order to track the various relationships. This “Industrial Age,” as I like to call it, is the time we are in today. The complex data model I describe above allows us to automate production and delivery workflows in an efficient way, by building media production factories for delivering multilingual, multiplatform content. And since a number of standards have recently emerged for delivering these complex bundles (AS-02 and IMF, for example), we have really reached a point where the full preparation, assembly and delivery workflows can be highly optimized.
Figure 4 – The Industrial Age – An asset is more than just one media file but a bundle with various versions derived from it
As the term “evolution” would imply, this “Industrial Age” is just another phase in the progression of MAM platforms, which are only going to become more advanced and more complex in the future. The next challenge for MAM platforms (or more accurately, the engineers who develop them) will be to include in their data model all the new requirements and paradigms of social media platforms and semantic technologies. The MAM data model will need to be aware not only of what’s happening inside the media factory but also of everything happening in the whole wide world of the semantic web. For us, this will be the next step in this long journey of constantly evolving our products’ data models. We have already begun the process, and it looks like it’s going to be a lot of fun for our engineering teams ;-).
Figure 5 – The Networked Age – Metadata relations will include Social Media and Semantic technologies
AI technologies progressed drastically in the last few years. Speech-to-text and face recognition are prime examples of use cases where AI-driven solutions that have existed for many years have now reached an acceptable level of maturity and commercial viability.
In today's fiercely competitive media business environment, every company is looking for means to stay ahead of the pack. Smart, highly efficient media processing can be a game-changer. Discover how Dalet AmberFin delivers high-quality content that grows your audience.
Recently, the lines between Media Asset Management (MAM) and Production Asset Management (PAM) have become increasingly blurred. This convergence reflects the quickly evolving needs of the industry. In recent years a huge leap forward in technological capability has coincided with rising creative demands and shifting media consumption trends. This has all had a significant effect...
AI technologies progressed drastically in the last few years. Speech-to-text and face recognition are prime examples of use cases where AI-driven solutions that have existed for many years have now reached an acceptable level of maturity and commercial viability.
In today's fiercely competitive media business environment, every company is looking for means to stay ahead of the pack. Smart, highly efficient media processing can be a game-changer. Discover how Dalet AmberFin delivers high-quality content that grows your audience.
Recently, the lines between Media Asset Management (MAM) and Production Asset Management (PAM) have become increasingly blurred. This convergence reflects the quickly evolving needs of the industry. In recent years a huge leap forward in technological capability has coincided with rising creative demands and shifting media consumption trends. This has all had a significant effect...
Figure 2 – The Stone Age – One file at a time
Figure 3 – The Iron Age – Multiple versions of the file
Figure 4 – The Industrial Age – An asset is more than just one media file but a bundle with various versions derived from it
Figure 5 – The Networked Age – Metadata relations will include Social Media and Semantic technologies
