If your team struggles to view how information is consumed from a digital asset management (DAM) system, consider the option of a graph database.
Graph databases are among most promising new analytic solutions in visualizing data relationships that matter.
What's more, they are excellent analytic complements to your DAM.
The Intersection of DAM and Graph Databases
In his CMSWire article, Jonathan Moran noted the DAM systems are increasingly shifting from data storage to integration with business intelligence solutions.
That means managers look to analytic solutions that offer data visualization that complements digital asset management.
Graph databases allow users to visualize queried data in terms of nodes — vertices meant to represent the queried data — and edges — lines graphed between the nodes to show a relationship between vertices.
The edges are created according to the metadata and query parameters, and show the relationships either as directional, like information relating to a specified query path, or as bidirectional, in which edges do not point in a specific direction.
How Graph Databases Benefit Marketers
Graph databases highlight relationships among the data elements that are otherwise invisible in a tabular format.
Furthermore, the analysis is transformed from a descriptive viewpoint — analytics that explain an occurrence) to influential analysis (highlighting the influences on the data).
Open Source Options
Open source databases provide fertile ground for investigating communication among data sources. Think communication between elements in a network and you have the right idea.
There are a few database platforms available, and all of which are open source.
Neo4j has become the most prominent over the last year — its use was noted during an investigation of the Panama Papers, a collection of more than 11 million leaked documents that detailed financial and attorney–client information.
Journalists used the tool to establish relationships among papers that linked nearly 215,000 offshore accounts to a securities fraud scheme.
To present queried data results as nodes, Neo4j use Cypher, a query language designed to require few lines of code to keep query protocols simple and easy to maintain.
Titan, an Amazon offering, is another popular option. Titan is designed to efficiently store a large amount of graphs, even ones with “up to hundreds of billions of vertices and edges” according to the website.
Titan offers an advantage of integrating with a number of storage databases.
DAMs: More Sophisticated Than Ever
Digital asset management systems have begun to include media metadata details in a sophisticated way to retrieve a particular image that is best for the content requested.
That complements what graphs databases seek to solve — simplifying how those details are visualized.
Graph databases act as well optimized recommendation engines, so they are especially useful for providing a visual face to the complex relationship patterns that emerge among media.
The visualizations in graph databases have been proven in data applications such as social networks, fraud detection and inventory management.
Data from a DAM system imported into a graph database tools will be able to analyze the impact of different media on activity and downstream metrics.
Such intelligence would be paramount for a company that wants to craft media that supports an objective.
Is the company press release best served with an image of a newly released product or a CEO that has been in the news? A PR team relying on DAM tools integrated with an analytics-oriented platform will be able to offer an answer.
As businesses incorporate massive volumes of digital media, managers have turned to modern DAM architectures for cost-effective relationship management.
Layering a graph database offers a platform for processing the more complex relationships with other data sources and ultimately increases an organization’s ability to tailor its media to its customers’ expectations and needs.
Title image by Caitlin Oriel