Monetizing digital asset management: The power of metadata management
Graphing relationships
Graph technologies provide distinct advantages when determining relationships between various facets of digital content and other relevant areas such as product information, layouts, or brand audiences. Because they excel at establishing connections between what may appear as disparate data, they’re being increasingly used for DAM. O’Brien characterized GraphQL as “a new evolution of APIs that are designed to combine different sources of data so that you can do more nested queries.” The highly contextualized, comprehensive nature of graph query results enables users to traverse more information with a single question. Graphing also underpins certain elements of textual or image recognition analytics so that when querying digital assets, organizations can see what Aasman called “multiple properties” across datasets related to one use case—such as the best site for positioning images related to servers, for example.
Asset enrichment
The digital asset space is one of the more cogent business areas for cognitive computing’s automation. Automation not only enables users to classify, find, and deploy assets more advantageously than they could before it became ubiquitous, but, in many instances, it actually enhances the assets themselves—and their use cases. For instance, technologies supporting computer vision and image recognition are responsible for automatically cropping visual assets.
This capacity innately reinforces asset reusability, because these technologies are “automatically identifying image focal points and cropping content differently based on the intended channel,” Schweer explained. These cognitive computing approaches achieve these objectives by analyzing the metadata and actual data of the assets. This use case is a strong example of the correlation between automation and acceleration, since it “eliminates hours of tedious editing,” Sedegah said.
Actuating metadata
Metadata management plays a prime role in almost all of the foregoing processes for capitalizing on digital content. Notably, many of those processes involve some facet of AI. Various supervised or unsupervised learning techniques offer demonstrative business value in optimizing content via analytics, tagging, and cataloging digital assets, swiftly retrieving them, and leveraging relevant taxonomies to consistently do so. Each of these capabilities boosts the ROI of digital assets and is predicated on perfecting how metadata is managed, analyzed, and deployed in the field (in the form of the assets it supports).
Metadata creates the road map for how best to translate content into conversions; it’s activated by a hybrid of cognitive computing and data management best practices.
“With advances in search and artificial intelligence making it easy to locate content, metadata has taken on incredible importance to make these advances work,” said Schweer. “Artificial intelligence and other computing advances require the inputs of a strong and consistent metadata. Metadata has gone from being passive to being the fuel that powers the AI-powered content revolution.”