We've heard a fair amount of talk about innovation in DAM lately, but one area hasn't received much attention: the need for systems that track the complex relationships between assets and other entities across an enterprise. 

In a Relationship

While most DAM systems track relationships, the scope has traditionally been limited: an image is "part of" a collection or product; a master video is the "source for" various derivatives. These are structural, first degree relationships used primarily to select and group together assets for distribution and processing. 

In my work as a DAM consultant, I have seen clients increasingly needing to track a much broader range of asset relationships for a wider range of purposes. For example, a media company recently requested a metadata hub to track the relationships between the characters, actors, funny moments and topics contained in decades worth of comedy videos. The media company used this network of relationships to power its mobile apps and create an immersive viewing experience that increased user engagement with its content. 

Another media enterprise wanted shared service hubs that could interrelate data from multiple systems, including contract data, production titles, character information, user data and digital assets. 

And It's Complicated

Driving this trend is the fact that digital assets are now part of a complex digital ecosystem. Consider, for example, a manufacturer that produces and distributes a video to promote a product. The video is part of a linked network that includes people, distribution channels, social media posts, purchases, contracts and side deals. Companies need to capture as much of this network of relationships as possible in order to drive business processes and support decision making. 

Stated another way, enterprises want to understand the full context in which their assets are used. 

complex relationships of assets

A number of different graph-based technologies provide the basis for systems that can manage and analyze complex relationships. The term “semantic web” is often attached to these technologies because they rely on information models, databases and query languages developed by academic researchers to interconnect and perform automated “reasoning” about resources distributed across the World Wide Web. 

A small number of software vendors have recognized the need to manage networks of relationships and are starting to offer commercial off-the-shelf products to meet this demand. censhare AG, for example, offers a communications product that supports functions as varied as Product Information Management, Marketing Resource Management and Digital Asset Management with a graph database. 

Pitney Bowes has also released a Customer Resource Management platform based on a "knowledge graph" that, as its literature states, allows users to "see relationships across multiple categories of data." 

Because these systems pull together information from across the enterprise, they can support operations as varied as DAM, PIM, CRM, MRM and CMS. These systems provide "asset intelligence," with the understanding that anything can be an asset: an image, a piece of content, a character, a product, a contract or a user. By tracking interactions between all of these different asset types, enterprises can gain a better understanding of how assets are currently used and decide how they should be used in the future. 

Title image by Jen Palmer