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Digital asset management benefits the future of work

Digital asset management promises to transform digital work, making it easier to tag documents, analyze video and provide image recognition. Here's where it's headed.

In today's media-saturated workplace, digital asset management promises to transform digital work in innovative, fascinating and far-reaching ways.

A modern digital asset management (DAM) platform includes a coherent set of content microservices that make it easy to weave experiences into the fabric of digital work. Within businesses, DAM provides the single source of truth -- an authoritative repository -- for making images, videos and other types of digital assets useful across a networked environment.

Here are some ways in which digital asset management will benefit the future of work.

Accelerating time to value

Consider how digital marketers might automatically find a well-organized selection of mountain biking photos, festooned with sponsors' logos, crowdsourced from fans at various off-trails events. With just a few clicks, they can add images featuring different sponsors' logos to promotional webpages.

These results build fan awareness in the moment. Marketing tasks that once spanned days and required multiple handoffs are now compressed into a matter of minutes. Sponsors increase the time to value of their investments when marketers are able to rapidly transform actual events into digital experiences.

Connecting through microservices

DAM can extend the capabilities of existing investments in digital platforms. In this example, the marketer can continue to use familiar web editing tools. There is no need to switch contexts and access a separate application to find the photos for producing the promotion. Easy access means speedy results.

Behind the scenes, a web content management (WCM) system connects seamlessly with an underlying cloud-powered DAM tool through predefined microservices. Many WCM vendors -- such as Acquia, Adobe, Contentful, e-Spirit and OpenText -- provide RESTful APIs and services to make these connections.

DAM, in turn, provides the authoritative collection of photos and videos for mountain biking events. It enables the consistency, accuracy and availability of digital assets at enterprise scale. DAM supports fan uploads, synchronizes collections from disparate locations, manages the photo-editing workflows, stores renditions for different delivery channels and tracks digital rights for required payments.

These capture, ingestion, auditing and monetization activities are part of any commercial operation, and they are essential for raising brand awareness and supporting a global branding campaign. A modern DAM tool provides the essential capabilities for producing next-generation digital experiences and is no longer simply a passive repository for storing digital assets.

DAM vendor comparison chart

Making metadata matter

Behind this transformation, there is a secret sauce. DAM depends on metadata -- the ability to succinctly describe and catalog individual files and to characterize the relevant items within them. The more granular and precise the metadata, the more useful DAM becomes for weaving images, videos and other types of assets into innovative digital experiences.

DAM can extend the capabilities of existing investments in digital platforms.

First-generation DAM systems require manual processes for tagging digital assets and managing metadata, and this can be a barrier to wide-scale adoption. Photo editors, librarians and marketers must tag assets using predefined fields and a controlled vocabulary. Taxonomies of relevant terms, often accessible to catalogers through drop-down pick lists, help standardize and accelerate categorization.

But manual processes cannot keep pace with the rising tide of digital assets, and modern digital asset management benefits how that work gets done. Here are three emerging approaches:

Contextual enrichment

Modern DAM makes metadata tagging increasingly contextual and automated. Implicit metadata -- inferred and enriched from related activities -- leads to more compelling and useful digital experiences.

For instance, a DAM tool captures attributes embedded within the digital assets themselves and produces the relevant tags during the file ingestion process. Attributes such as location, date and photographer can be recorded at their source and used to categorize assets within the system.

Moreover, DAM increasingly relies on contextual clues and cues accessible through related microservices. For example, photos tagged with product names can link to a product information management application to enrich the metadata with tags about product attributes, such as weight, colors and safety features.

Assets can be automatically tagged as they pass through steps in a business process to track how they are used, where they are used and who has viewed them. Optical character recognition can decode words in images to create implicit tags.

Image recognition

Beyond contextual enrichment, image recognition is the next frontier, where tags automatically identify important aspects within assets. With sufficient training, image recognition algorithms can detect logos, identify objects, determine colors, count the number of like items, read product labels and characterize faces.

Algorithms and training sets are continuously improving. DAM vendors often integrate with image recognition services developed by third-party platforms, such as Adobe, Amazon, Google, IBM, Microsoft and other specialized services providers.

Video analysis

Videos are easy to make and hard to analyze. The quest for intelligent video recognition is the holy grail for DAM in the enterprise. Imagine a salesman being able to easily extract the 30-second description about a new product feature, relevant for his particular customer, within an hourlong product announcement video -- without scanning the entire stream.

Next-generation video analysis tools are on the horizon that promise to make video collections as useful as any other type of digital asset. As an example, IBM is charting a path forward with its Watson Video Enrichment program. Using deep learning technologies provided by Watson, this program analyzes video streams by doing the following:

  • determining the meaningful scenes within a lengthy stream;
  • converting audio to text through speech-to-text technologies;
  • understanding text by using multiple approaches to natural language processing; and
  • detecting the contents of an image or video frame through image recognition technologies.

The enriched metadata, delivered as open JavaScript Object Notation bundles, is then stored, linked to time codes in the video and used by external applications to understand video streams in new ways, increase operational efficiency and drive viewer engagement. For example, a salesman might easily locate a 30-second clip that answers a customer's question.

How digital work gets done

In the future, effective digital experiences will depend on presenting compelling images and videos -- on demand and in context. Any purpose-built system designed for digital workplace transformation needs to be built around a predefined set of tasks and activities. While digital work is becoming increasingly visual and experiential, designing elegant services that simplify the underlying complexity becomes more important.

As the single source of truth, digital asset management benefits the digital workplace by providing the foundation for organizing rich media. With increasing frequency, enterprises will rely on contextual enrichment, image recognition and video analysis to produce more granular -- and useful -- metadata.

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